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4JOB/filter_logs/.ipynb_checkpoints/removed_entries_20250618_162341-checkpoint.log

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+Filtering log - 20250618_162341
+Input file: silence.json
+Output file: silence_filtered_output.json
+Duration range: 30s to 90s
+
+Removed Entries:
+==================================================
+Key: 83
+Original Dialog ID: SODA_PROCESSED--train--214477
+Duration: 99.83525s
+Reason: too_long
+--------------------------------------------------
+Key: 15
+Original Dialog ID: SODA_PROCESSED--train--972977
+Duration: 94.94791666666667s
+Reason: too_long
+--------------------------------------------------
+Key: 18
+Original Dialog ID: SODA_PROCESSED--train--795181
+Duration: 92.62829166666667s
+Reason: too_long
+--------------------------------------------------
+Key: 31_1
+Original Dialog ID: SODA_PROCESSED--train--1113674
+Duration: 95.01416666666667s
+Reason: too_long
+--------------------------------------------------
+Key: 53_1
+Original Dialog ID: SODA_PROCESSED--train--484021
+Duration: 98.07645833333333s
+Reason: too_long
+--------------------------------------------------
+Key: 74_1
+Original Dialog ID: SODA_PROCESSED--train--1047480
+Duration: 91.74375s
+Reason: too_long
+--------------------------------------------------
+Key: 17_1
+Original Dialog ID: SODA_PROCESSED--train--166191
+Duration: 97.76666666666667s
+Reason: too_long
+--------------------------------------------------
+Key: 46_2
+Original Dialog ID: SODA_PROCESSED--train--727552
+Duration: 91.58875s
+Reason: too_long
+--------------------------------------------------
+Key: 84_2
+Original Dialog ID: SODA_PROCESSED--train--286623
+Duration: 94.22970833333333s
+Reason: too_long
+--------------------------------------------------
+Key: 55_2
+Original Dialog ID: SODA_PROCESSED--train--317784
+Duration: 96.18079166666666s
+Reason: too_long
+--------------------------------------------------
+Key: 35_2
+Original Dialog ID: SODA_PROCESSED--train--1190867
+Duration: 99.861s
+Reason: too_long
+--------------------------------------------------
+Key: 99_2
+Original Dialog ID: SODA_PROCESSED--train--304811
+Duration: 91.12975s
+Reason: too_long
+--------------------------------------------------
+Key: 44_3
+Original Dialog ID: SODA_PROCESSED--train--1084179
+Duration: 90.02725s
+Reason: too_long
+--------------------------------------------------
+Key: 24_3
+Original Dialog ID: SODA_PROCESSED--train--209436
+Duration: 94.00129166666666s
+Reason: too_long
+--------------------------------------------------
+Key: 10_3
+Original Dialog ID: SODA_PROCESSED--train--606362
+Duration: 95.01458333333333s
+Reason: too_long
+--------------------------------------------------
+Key: 11_3
+Original Dialog ID: SODA_PROCESSED--train--33760
+Duration: 91.81675s
+Reason: too_long
+--------------------------------------------------
+Key: 73_4
+Original Dialog ID: SODA_PROCESSED--train--873625
+Duration: 92.01975s
+Reason: too_long
+--------------------------------------------------

4JOB/process_speaker.py

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+import json
+import random
+
+def seconds_to_mmss(seconds):
+    minutes = int(seconds // 60)
+    seconds = int(seconds % 60)
+    return f"{minutes:02d}:{seconds:02d}"
+
+# Templates for speaker segment descriptions
+SPEAKER_TEMPLATES = [
+    "Speaker {speaker} speaks during the following periods: {times}",
+    "Speaker {speaker}'s speaking segments occur at: {times}",
+    "Speaker {speaker} is active in the conversation at: {times}",
+    "The following time segments belong to Speaker {speaker}: {times}",
+    "Speaker {speaker} participates in the dialogue at: {times}",
+    "Speaker {speaker} contributes to the conversation during: {times}",
+    "Speaking turns for Speaker {speaker} are at: {times}",
+    "Speaker {speaker} takes the floor at: {times}",
+    "The voice of Speaker {speaker} is heard at: {times}",
+    "Speaker {speaker} engages in the discussion during: {times}"
+]
+file = "silence"
+def process_speaker_segments():
+    # Read the overlap_5s_716.json file
+    with open(f'{file}.json', 'r', encoding='utf-8') as f:
+        data = json.load(f)
+
+    # List to store results for all conversations
+    results = []
+
+    # Process each conversation
+    for conversation_id, conversation in data.items():
+        segments = conversation.get('segments', [])
+        audio_path = conversation.get('stereo_audio', [])
+        # Dictionary to store speaking times for each speaker
+        speaker_times = {}
+        
+        # Process each segment
+        for segment in segments:
+            speaker = segment['speaker']
+            start_time = segment['start_time']  # Keep as float for accurate conversion
+            end_time = segment['end_time']      # Keep as float for accurate conversion
+            
+            # Initialize list for this speaker if not exists
+            if speaker not in speaker_times:
+                speaker_times[speaker] = []
+            
+            # Add this speaking interval
+            speaker_times[speaker].append((start_time, end_time))
+        
+        # Format the output string
+        output_lines = []
+        for speaker in sorted(speaker_times.keys()):
+            times = speaker_times[speaker]
+            time_ranges = [f"{seconds_to_mmss(start)}-{seconds_to_mmss(end)}" for start, end in times]
+            # Randomly select a template for each speaker
+            template = random.choice(SPEAKER_TEMPLATES)
+            output_lines.append(template.format(speaker=speaker, times=', '.join(time_ranges)))
+        
+        # Create result entry
+        result = {
+            "key": conversation_id,
+            "audio_url": audio_path,
+            "model_output": "\n".join(output_lines)
+        }
+        results.append(result)
+
+    # Save the results to a JSON file
+    output_file = f'{file}_speaker.json'
+    with open(output_file, 'w', encoding='utf-8') as f:
+        json.dump(results, f, indent=2, ensure_ascii=False)
+
+if __name__ == "__main__":
+    process_speaker_segments() 

cotSFT/gemini-silence/.ipynb_checkpoints/run_gemini_meta-checkpoint.sh

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+python gemini2.5_metainfo.py --input_file onlyAudios_longdelay_add_silence_modified_transcriptions.json --output_file results.json --error_file errors.json --max_retries 5 --checkpoint_interval 100

cotSFT/gemini-silence/run_gemini_meta.sh

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+python gemini2.5_metainfo.py --input_file onlyAudios_longdelay_add_silence_modified_transcriptions.json --output_file results.json --error_file errors.json --max_retries 5 --checkpoint_interval 100

cotSFT/gemini-text/.ipynb_checkpoints/errors-checkpoint.json

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+[]

cotSFT/gemini-text/.ipynb_checkpoints/gemini2.5_metainfo-checkpoint.py

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+import os
+import json
+import re
+import requests
+from tqdm import tqdm
+from datetime import datetime
+import glob
+from requests.exceptions import Timeout
+import argparse
+
+prompt_template = (
+    "# Interactional Dialogue Evaluation\n\n"
+    "**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\n"  
+    "Evaluate the quality of the interaction in the given dialogue transcript, focusing on:\n"
+    "**Response Relevance:** \n"
+    "**logical consistency, topic coherence**\n"
+    "**Interactional Fluency:**\n"
+    "**Detect and evaluate extended overlaps in conversation.**\n"
+    "**Detect and evaluate long pauses between speaker turns.\n\n**"
+    "**Note**: Small pauses and brief overlaps in conversation are acceptable, while prolonged pauses and overlapping turns are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n"
+    "## Scoring Criteria\n"
+    "Assign a single holistic score based on the combined evaluation:\n"
+    "`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n"
+    "`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n"
+    "## Evaluation Output Format:\n"
+    "Strictly follow this template:\n"
+    "<response think>\n"
+    "[Analysing Response Relevance and giving reasons for scoring...]\n"
+    "</response think>\n"
+    "<fluency think>\n"
+    "[Analysing Interactional Fluency and giving reasons for scoring.]\n"
+    "</fluency think>\n"
+    "<overall score>X</overall score>\n"
+)
+
+# API configuration
+url = "https://api2.aigcbest.top/v1/chat/completions"
+headers = {
+    "Authorization": "Bearer sk-yAIqUaGzzVNSesHq4mRPaCbt53MMFRJIMB97cS4FkRy6idwN",
+    "Content-Type": "application/json",
+    "Accept": "application/json"
+}
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Process text evaluation with Gemini model')
+    parser.add_argument('--input_file', type=str, required=True,
+                      help='Input JSON file containing text data')
+    parser.add_argument('--output_file', type=str, default='texterror_gemini.json',
+                      help='Output JSON file for results')
+    parser.add_argument('--error_file', type=str, default='texterror_gemini_error.json',
+                      help='Output JSON file for errors')
+    parser.add_argument('--checkpoint_dir', type=str, default='checkpoints_test_text',
+                      help='Directory for storing checkpoints')
+    parser.add_argument('--max_retries', type=int, default=3,
+                      help='Maximum number of retries for failed predictions')
+    parser.add_argument('--checkpoint_interval', type=int, default=20,
+                      help='Number of items to process before saving checkpoint')
+    return parser.parse_args()
+
+def extract_overall_score(output_str):
+    """Extract <overall score>X</overall score> from model output."""
+    score_pattern = r"<overall score>(\d+)</overall score>"
+    match = re.search(score_pattern, output_str)
+    if match:
+        try:
+            return int(match.group(1))
+        except ValueError:
+            pass
+    return None
+
+def validate_model_output(output_str):
+    """Validate that the model output contains all required tags"""
+    required_tags = [
+        "<response think>",
+        "</response think>",
+        "<fluency think>",
+        "</fluency think>",
+        "<overall score>",
+        "</overall score>"
+    ]
+    
+    for tag in required_tags:
+        if tag not in output_str:
+            return False
+    return True
+
+def extract_tag_content(output_str, tag_name):
+    """Extract content between opening and closing tags"""
+    start_tag = f"<{tag_name}>"
+    end_tag = f"</{tag_name}>"
+    try:
+        start_idx = output_str.find(start_tag) + len(start_tag)
+        end_idx = output_str.find(end_tag)
+        if start_idx == -1 or end_idx == -1:
+            return None
+        return output_str[start_idx:end_idx].strip()
+    except:
+        return None
+
+def format_model_output(output_str):
+    """Extract and format content from all required tags"""
+    response_content = extract_tag_content(output_str, "response think")
+    fluency_content = extract_tag_content(output_str, "fluency think")
+    score_content = extract_tag_content(output_str, "overall score")
+    
+    if not all([response_content, fluency_content, score_content]):
+        return None
+        
+    formatted_output = (
+        f"<response think>\n{response_content}\n</response think>\n\n"
+        f"<fluency think>\n{fluency_content}\n</fluency think>\n\n"
+        f"<overall score>{score_content}</overall score>"
+    )
+    return formatted_output
+
+def make_api_call(text_input, retry_count=0, max_retries=5):
+    """Make API call with retry logic for API errors"""
+    try:
+        print(f"Attempting API call (attempt {retry_count + 1}/{max_retries + 1})")
+        data_req = {
+            "model": "gemini-2.5-flash-preview-05-20-thinking",
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": prompt_template
+                        },
+                        {
+                            "type": "text",
+                            "text": text_input
+                        },
+                    ]
+                }
+            ],
+            "temperature": 1,
+        }
+        
+        response = requests.post(url, headers=headers, json=data_req, timeout=(200, 200))
+        print(f"API response received with status code: {response.status_code}")
+        
+        if response.status_code == 200:
+            model_output = response.json()['choices'][0]['message']['content']
+            if not validate_model_output(model_output):
+                print("Model output missing required tags, retrying...")
+                return None, None
+                
+            formatted_output = format_model_output(model_output)
+            if formatted_output is None:
+                print("Failed to extract content from tags, retrying...")
+                return None, None
+                
+            pred_score = extract_overall_score(model_output)
+            return formatted_output, pred_score
+        else:
+            print(f"API returned error status {response.status_code}: {response.text}")
+            if retry_count >= max_retries:
+                raise Exception(f"POST error {response.status_code}: {response.text}")
+            return None, None
+    except requests.exceptions.ConnectTimeout:
+        print(f"Connection timeout (>10s)")
+        if retry_count >= max_retries:
+            raise Exception("Connection timeout")
+        return None, None
+    except requests.exceptions.ReadTimeout:
+        print(f"Read timeout (>30s)")
+        if retry_count >= max_retries:
+            raise Exception("Read timeout")
+        return None, None
+    except Exception as e:
+        print(f"Unexpected error during API call: {str(e)}")
+        if retry_count >= max_retries:
+            raise e
+        return None, None
+
+def get_latest_checkpoint(checkpoint_dir):
+    """Get the latest checkpoint file and its processed count"""
+    checkpoint_files = glob.glob(os.path.join(checkpoint_dir, "checkpoint_*.json"))
+    if not checkpoint_files:
+        return None, 0
+    
+    latest_checkpoint = None
+    max_count = 0
+    for checkpoint in checkpoint_files:
+        try:
+            count = int(os.path.basename(checkpoint).split('_')[1])
+            if count > max_count:
+                max_count = count
+                latest_checkpoint = checkpoint
+        except (ValueError, IndexError):
+            continue
+    
+    return latest_checkpoint, max_count
+
+def save_checkpoint(results, processed_count, checkpoint_dir):
+    """Save results to a checkpoint file"""
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    checkpoint_file = os.path.join(checkpoint_dir, f"checkpoint_{processed_count}_{timestamp}.json")
+    with open(checkpoint_file, "w", encoding="utf-8") as f:
+        json.dump(results, f, indent=2, ensure_ascii=False)
+    print(f"Checkpoint saved: {checkpoint_file}")
+
+def main():
+    args = parse_args()
+    
+    # Initialize results storage
+    results = []
+    save_file_name = args.output_file
+    error_file_name = args.error_file
+    
+    # Create checkpoints directory
+    checkpoint_dir = args.checkpoint_dir
+    if not os.path.exists(checkpoint_dir):
+        os.makedirs(checkpoint_dir)
+    
+    # Load test data
+    all_data_file = args.input_file
+    with open(all_data_file, 'r', encoding='utf-8') as f:
+        all_data = json.load(f)
+    
+    # Initialize error tracking
+    error_results = []
+    
+    # Load checkpoint if exists
+    latest_checkpoint, checkpoint_count = get_latest_checkpoint(checkpoint_dir)
+    if latest_checkpoint:
+        print(f"Found latest checkpoint with {checkpoint_count} processed items: {latest_checkpoint}")
+        try:
+            with open(latest_checkpoint, 'r', encoding='utf-8') as f:
+                results = json.load(f)
+                print(f"Resumed from checkpoint: processed {len(results)} items")
+        except Exception as e:
+            print(f"Warning: Failed to load checkpoint {latest_checkpoint}: {e}")
+            results = []
+    else:
+        print("No checkpoint found, starting from scratch")
+        results = []
+    
+    max_prediction_retries = args.max_retries
+    total_count = 0
+    
+    for item in tqdm(all_data, desc="Processing texts"):
+        key = item.get('key')
+        text_input = item.get('model_output')
+        
+        if not text_input:
+            print(f"No text input found for key {key}, skipping...")
+            continue
+        
+        print(f"Processing text for key={key}")
+        
+        prediction_retry_count = 0
+        success = False
+        
+        while prediction_retry_count < max_prediction_retries and not success:
+            try:
+                print(f"\nProcessing attempt {prediction_retry_count + 1}")
+                model_output, pred_score = make_api_call(text_input)
+                
+                if model_output is None or pred_score is None:
+                    print("API call failed, retrying...")
+                    prediction_retry_count += 1
+                    continue
+                    
+                print(f"Received prediction: {pred_score}")
+                
+                if pred_score == 1:
+                    success = True
+                    print("Prediction score is 1, accepting result")
+                else:
+                    prediction_retry_count += 1
+                    print(f"Prediction score is not 1 (attempt {prediction_retry_count}/{max_prediction_retries})")
+                    if prediction_retry_count >= max_prediction_retries:
+                        print("Max retries reached, accepting last prediction")
+                        success = True
+                    else:
+                        continue
+                
+                results.append({
+                    "key": key,
+                    "text_input": text_input,
+                    "model_output": model_output,
+                    "predicted_score": pred_score,
+                    "prediction_attempts": prediction_retry_count + 1
+                })
+                
+                with open(save_file_name, "w", encoding="utf-8") as f:
+                    json.dump(results, f, indent=2, ensure_ascii=False)
+                    
+                total_count += 1
+                
+                if total_count % args.checkpoint_interval == 0:
+                    save_checkpoint(results, total_count, checkpoint_dir)
+                
+            except Exception as e:
+                error_msg = str(e)
+                print(f"Failed to process text for key {key}: {error_msg}")
+                error_results.append({
+                    "key": key,
+                    "text_input": text_input,
+                    "error": f"Exception: {error_msg}"
+                })
+                break
+                
+        with open(error_file_name, "w", encoding="utf-8") as f:
+            json.dump(error_results, f, indent=2, ensure_ascii=False)
+    
+    # Save final results
+    with open(save_file_name, "w", encoding="utf-8") as f:
+        json.dump(results, f, indent=2, ensure_ascii=False)
+    
+    print(f"Results saved to {save_file_name}")
+    print(f"Total processed items: {total_count}")
+
+if __name__ == "__main__":
+    main()

cotSFT/gemini-text/.ipynb_checkpoints/run_gemini_meta-checkpoint.sh

@@ -0,0 +1 @@
+python gemini2.5_metainfo.py --input_file texterror_transcriptions.json --output_file results.json --error_file errors.json --max_retries 5 --checkpoint_interval 100

cotSFT/gemini-text/gemini2.5_metainfo.py

@@ -0,0 +1,317 @@
+import os
+import json
+import re
+import requests
+from tqdm import tqdm
+from datetime import datetime
+import glob
+from requests.exceptions import Timeout
+import argparse
+
+prompt_template = (
+    "# Interactional Dialogue Evaluation\n\n"
+    "**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\n"  
+    "Evaluate the quality of the interaction in the given dialogue transcript, focusing on:\n"
+    "**Response Relevance:** \n"
+    "**logical consistency, topic coherence**\n"
+    "**Interactional Fluency:**\n"
+    "**Detect and evaluate extended overlaps in conversation.**\n"
+    "**Detect and evaluate long pauses between speaker turns.\n\n**"
+    "**Note**: Small pauses and brief overlaps in conversation are acceptable, while prolonged pauses and overlapping turns are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n"
+    "## Scoring Criteria\n"
+    "Assign a single holistic score based on the combined evaluation:\n"
+    "`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n"
+    "`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n"
+    "## Evaluation Output Format:\n"
+    "Strictly follow this template:\n"
+    "<response think>\n"
+    "[Analysing Response Relevance and giving reasons for scoring...]\n"
+    "</response think>\n"
+    "<fluency think>\n"
+    "[Analysing Interactional Fluency and giving reasons for scoring.]\n"
+    "</fluency think>\n"
+    "<overall score>X</overall score>\n"
+)
+
+# API configuration
+url = "https://api2.aigcbest.top/v1/chat/completions"
+headers = {
+    "Authorization": "Bearer sk-yAIqUaGzzVNSesHq4mRPaCbt53MMFRJIMB97cS4FkRy6idwN",
+    "Content-Type": "application/json",
+    "Accept": "application/json"
+}
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Process text evaluation with Gemini model')
+    parser.add_argument('--input_file', type=str, required=True,
+                      help='Input JSON file containing text data')
+    parser.add_argument('--output_file', type=str, default='texterror_gemini.json',
+                      help='Output JSON file for results')
+    parser.add_argument('--error_file', type=str, default='texterror_gemini_error.json',
+                      help='Output JSON file for errors')
+    parser.add_argument('--checkpoint_dir', type=str, default='checkpoints_test_text',
+                      help='Directory for storing checkpoints')
+    parser.add_argument('--max_retries', type=int, default=3,
+                      help='Maximum number of retries for failed predictions')
+    parser.add_argument('--checkpoint_interval', type=int, default=20,
+                      help='Number of items to process before saving checkpoint')
+    return parser.parse_args()
+
+def extract_overall_score(output_str):
+    """Extract <overall score>X</overall score> from model output."""
+    score_pattern = r"<overall score>(\d+)</overall score>"
+    match = re.search(score_pattern, output_str)
+    if match:
+        try:
+            return int(match.group(1))
+        except ValueError:
+            pass
+    return None
+
+def validate_model_output(output_str):
+    """Validate that the model output contains all required tags"""
+    required_tags = [
+        "<response think>",
+        "</response think>",
+        "<fluency think>",
+        "</fluency think>",
+        "<overall score>",
+        "</overall score>"
+    ]
+    
+    for tag in required_tags:
+        if tag not in output_str:
+            return False
+    return True
+
+def extract_tag_content(output_str, tag_name):
+    """Extract content between opening and closing tags"""
+    start_tag = f"<{tag_name}>"
+    end_tag = f"</{tag_name}>"
+    try:
+        start_idx = output_str.find(start_tag) + len(start_tag)
+        end_idx = output_str.find(end_tag)
+        if start_idx == -1 or end_idx == -1:
+            return None
+        return output_str[start_idx:end_idx].strip()
+    except:
+        return None
+
+def format_model_output(output_str):
+    """Extract and format content from all required tags"""
+    response_content = extract_tag_content(output_str, "response think")
+    fluency_content = extract_tag_content(output_str, "fluency think")
+    score_content = extract_tag_content(output_str, "overall score")
+    
+    if not all([response_content, fluency_content, score_content]):
+        return None
+        
+    formatted_output = (
+        f"<response think>\n{response_content}\n</response think>\n\n"
+        f"<fluency think>\n{fluency_content}\n</fluency think>\n\n"
+        f"<overall score>{score_content}</overall score>"
+    )
+    return formatted_output
+
+def make_api_call(text_input, retry_count=0, max_retries=5):
+    """Make API call with retry logic for API errors"""
+    try:
+        print(f"Attempting API call (attempt {retry_count + 1}/{max_retries + 1})")
+        data_req = {
+            "model": "gemini-2.5-flash-preview-05-20-thinking",
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": prompt_template
+                        },
+                        {
+                            "type": "text",
+                            "text": text_input
+                        },
+                    ]
+                }
+            ],
+            "temperature": 1,
+        }
+        
+        response = requests.post(url, headers=headers, json=data_req, timeout=(200, 200))
+        print(f"API response received with status code: {response.status_code}")
+        
+        if response.status_code == 200:
+            model_output = response.json()['choices'][0]['message']['content']
+            if not validate_model_output(model_output):
+                print("Model output missing required tags, retrying...")
+                return None, None
+                
+            formatted_output = format_model_output(model_output)
+            if formatted_output is None:
+                print("Failed to extract content from tags, retrying...")
+                return None, None
+                
+            pred_score = extract_overall_score(model_output)
+            return formatted_output, pred_score
+        else:
+            print(f"API returned error status {response.status_code}: {response.text}")
+            if retry_count >= max_retries:
+                raise Exception(f"POST error {response.status_code}: {response.text}")
+            return None, None
+    except requests.exceptions.ConnectTimeout:
+        print(f"Connection timeout (>10s)")
+        if retry_count >= max_retries:
+            raise Exception("Connection timeout")
+        return None, None
+    except requests.exceptions.ReadTimeout:
+        print(f"Read timeout (>30s)")
+        if retry_count >= max_retries:
+            raise Exception("Read timeout")
+        return None, None
+    except Exception as e:
+        print(f"Unexpected error during API call: {str(e)}")
+        if retry_count >= max_retries:
+            raise e
+        return None, None
+
+def get_latest_checkpoint(checkpoint_dir):
+    """Get the latest checkpoint file and its processed count"""
+    checkpoint_files = glob.glob(os.path.join(checkpoint_dir, "checkpoint_*.json"))
+    if not checkpoint_files:
+        return None, 0
+    
+    latest_checkpoint = None
+    max_count = 0
+    for checkpoint in checkpoint_files:
+        try:
+            count = int(os.path.basename(checkpoint).split('_')[1])
+            if count > max_count:
+                max_count = count
+                latest_checkpoint = checkpoint
+        except (ValueError, IndexError):
+            continue
+    
+    return latest_checkpoint, max_count
+
+def save_checkpoint(results, processed_count, checkpoint_dir):
+    """Save results to a checkpoint file"""
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    checkpoint_file = os.path.join(checkpoint_dir, f"checkpoint_{processed_count}_{timestamp}.json")
+    with open(checkpoint_file, "w", encoding="utf-8") as f:
+        json.dump(results, f, indent=2, ensure_ascii=False)
+    print(f"Checkpoint saved: {checkpoint_file}")
+
+def main():
+    args = parse_args()
+    
+    # Initialize results storage
+    results = []
+    save_file_name = args.output_file
+    error_file_name = args.error_file
+    
+    # Create checkpoints directory
+    checkpoint_dir = args.checkpoint_dir
+    if not os.path.exists(checkpoint_dir):
+        os.makedirs(checkpoint_dir)
+    
+    # Load test data
+    all_data_file = args.input_file
+    with open(all_data_file, 'r', encoding='utf-8') as f:
+        all_data = json.load(f)
+    
+    # Initialize error tracking
+    error_results = []
+    
+    # Load checkpoint if exists
+    latest_checkpoint, checkpoint_count = get_latest_checkpoint(checkpoint_dir)
+    if latest_checkpoint:
+        print(f"Found latest checkpoint with {checkpoint_count} processed items: {latest_checkpoint}")
+        try:
+            with open(latest_checkpoint, 'r', encoding='utf-8') as f:
+                results = json.load(f)
+                print(f"Resumed from checkpoint: processed {len(results)} items")
+        except Exception as e:
+            print(f"Warning: Failed to load checkpoint {latest_checkpoint}: {e}")
+            results = []
+    else:
+        print("No checkpoint found, starting from scratch")
+        results = []
+    
+    max_prediction_retries = args.max_retries
+    total_count = 0
+    
+    for item in tqdm(all_data, desc="Processing texts"):
+        key = item.get('key')
+        text_input = item.get('model_output')
+        
+        if not text_input:
+            print(f"No text input found for key {key}, skipping...")
+            continue
+        
+        print(f"Processing text for key={key}")
+        
+        prediction_retry_count = 0
+        success = False
+        
+        while prediction_retry_count < max_prediction_retries and not success:
+            try:
+                print(f"\nProcessing attempt {prediction_retry_count + 1}")
+                model_output, pred_score = make_api_call(text_input)
+                
+                if model_output is None or pred_score is None:
+                    print("API call failed, retrying...")
+                    prediction_retry_count += 1
+                    continue
+                    
+                print(f"Received prediction: {pred_score}")
+                
+                if pred_score == 1:
+                    success = True
+                    print("Prediction score is 1, accepting result")
+                else:
+                    prediction_retry_count += 1
+                    print(f"Prediction score is not 1 (attempt {prediction_retry_count}/{max_prediction_retries})")
+                    if prediction_retry_count >= max_prediction_retries:
+                        print("Max retries reached, accepting last prediction")
+                        success = True
+                    else:
+                        continue
+                
+                results.append({
+                    "key": key,
+                    "text_input": text_input,
+                    "model_output": model_output,
+                    "predicted_score": pred_score,
+                    "prediction_attempts": prediction_retry_count + 1
+                })
+                
+                with open(save_file_name, "w", encoding="utf-8") as f:
+                    json.dump(results, f, indent=2, ensure_ascii=False)
+                    
+                total_count += 1
+                
+                if total_count % args.checkpoint_interval == 0:
+                    save_checkpoint(results, total_count, checkpoint_dir)
+                
+            except Exception as e:
+                error_msg = str(e)
+                print(f"Failed to process text for key {key}: {error_msg}")
+                error_results.append({
+                    "key": key,
+                    "text_input": text_input,
+                    "error": f"Exception: {error_msg}"
+                })
+                break
+                
+        with open(error_file_name, "w", encoding="utf-8") as f:
+            json.dump(error_results, f, indent=2, ensure_ascii=False)
+    
+    # Save final results
+    with open(save_file_name, "w", encoding="utf-8") as f:
+        json.dump(results, f, indent=2, ensure_ascii=False)
+    
+    print(f"Results saved to {save_file_name}")
+    print(f"Total processed items: {total_count}")
+
+if __name__ == "__main__":
+    main()

cotSFT/gemini-text/run_gemini_meta.sh

@@ -0,0 +1 @@
+python gemini2.5_metainfo.py --input_file texterror_transcriptions.json --output_file results.json --error_file errors.json --max_retries 5 --checkpoint_interval 100

cotSFT/test/.ipynb_checkpoints/test-checkpoint.json

@@ -0,0 +1,170 @@
+{
+  "0":{
+    "key": "DialogSum--val--1",
+    "text_input": "[00:00 - 00:10] Speaker A: Does this happen all the time or mostly when you are doing physical activities like running or lifting weights, or does it occur even when you're just resting or lying down comfortably?\n[00:03 - 00:09] Speaker B: Sorry to cut in, but I just remembered—could this be related to stress? I've been under a lot of pressure at work lately.\n[00:10 - 00:18] Speaker A: Stress can definitely contribute to breathing issues. But I was about to ask if this happens mostly when you are active or at rest?\n[00:18 - 00:20] Speaker B: It happens a lot when I work out.\n[00:20 - 00:26] Speaker A: I see. I am going to send you to a pulmonary specialist who can run tests on you for asthma.\n[00:26 - 00:27] Speaker B: Thank you for your help, doctor.",
+    "model_output": "<response think>\nThe dialogue exhibits good response relevance.\n*   **Speaker A (00:00 - 00:10):** Asks a precise diagnostic question, relevant to understanding the nature of the breathing issues.\n*   **Speaker B (00:03 - 00:09):** While interrupting, Speaker B's question about stress is relevant to potential causes of health issues, even if it deviates slightly from Speaker A's immediate line of questioning. It's a natural patient interjection.\n*   **Speaker A (00:10 - 00:18):** Acknowledges Speaker B's point about stress, showing good listening and empathy (\"Stress can definitely contribute...\"), and then skillfully brings the conversation back to the original diagnostic question (\"But I was about to ask if this happens mostly when you are active or at rest?\"). This demonstrates logical steering of the conversation.\n*   **Speaker B (00:18 - 00:20):** Provides a direct and relevant answer to Speaker A's question.\n*   **Speaker A (00:20 - 00:26):** Based on the information, Speaker A proposes a logical and relevant next step (referral to a specialist for asthma testing).\n*   **Speaker B (00:26 - 00:27):** Concludes with an appropriate expression of gratitude.\n\nThe dialogue maintains logical consistency and topic coherence throughout, with Speaker A effectively managing the flow even when Speaker B introduces a new point.\n</response think>\n\n<fluency think>\nThe evaluation criteria state that \"Small pauses and brief overlaps in conversation are acceptable, while prolonged pauses and overlapping turns are harmful.\"\n\n*   **Overlap (00:03 - 00:09) Speaker B over Speaker A:** This is an extended overlap. Speaker B starts speaking at 00:03 while Speaker A is still speaking and continues until 00:09, while Speaker A finishes at 00:10. This is a significant interruption, as Speaker B completes an entire turn while Speaker A is still in the middle of theirs. The duration of the overlap (6 seconds for Speaker B's turn during Speaker A's 10-second turn) is substantial. Speaker B even acknowledges it with \"Sorry to cut in,\" indicating an awareness of the interruption. This type of extended overlap, where one speaker fully interjects and completes a thought over another, harms interactional fluency.\n\n*   **Pauses:** There are no long pauses between speaker turns. The transitions are relatively quick (e.g., A finishes at 00:18, B starts at 00:18; B finishes at 00:20, A starts at 00:20).\n\nThe presence of a prolonged and disruptive overlapping turn from Speaker B significantly detracts from the interactional fluency.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_overlap5s_dialogsum_testVal/processed_processed_2_val_dialogsum_output/DialogSum--val--1/stereo_dialogue.wav"
+  },
+  "1":{
+    "key": "DialogSum--val--2",
+    "text_input": "[00:00 - 00:02] Speaker A: Hey Jimmy. Let's go workout later today.\n[00:03 - 00:11] Speaker B: Sure. What time do you want to plan on meeting up? I was hoping we could get an early start so we have enough time for a proper warm-up and cool-down session.\n[00:04 - 00:08] Speaker A: Actually, I was thinking around 3:30. Does that work for you?\n[00:11 - 00:14] Speaker B: That sounds good. Today we work on Legs and forearm.\n[00:14 - 00:22] Speaker A: Hey. I just played basketball earlier, so my legs are a little sore. Let's work out on arms and stomach today.\n[00:22 - 00:31] Speaker B: I'm on a weekly schedule. You're messing everything up with this change. But I guess I can adjust since I understand you're already fatigued from your basketball game earlier this afternoon.\n[00:29 - 00:35] Speaker A: up? I get it, but it's just switching two days. You can do legs on Friday.\n[00:35 - 00:38] Speaker B: Aright. I'll meet you at the gym at 3:30 then.",
+    "model_output": "<response think>\nThe dialogue exhibits strong response relevance. Speaker B's first response directly addresses Speaker A's workout proposal by asking for a time and providing a reason for an early start. Speaker A then directly answers the time query. When Speaker B introduces a specific workout plan, Speaker A logically explains why they need to change it due to fatigue and proposes an alternative. Speaker B acknowledges Speaker A's situation and expresses their difficulty with the change due to their schedule, which is a relevant emotional and logistical response. Speaker A then offers a practical solution to Speaker B's concern, which Speaker B accepts. All turns are logically consistent and maintain topic coherence, progressing the discussion smoothly from planning to execution and conflict resolution.\n</response think>\n\n<fluency think>\nThere are significant issues with interactional fluency due to extended overlaps.\n1.  **[00:04 - 00:08] Speaker A:** This turn overlaps with Speaker B's turn from [00:03 - 00:11]. Specifically, Speaker A begins speaking at 00:04 while Speaker B is still explaining \"I was hoping we could get an early start so we have enough time for a proper warm-up and cool-down session.\" This is a 4-second overlap, which is an extended duration and indicates Speaker A cutting off or speaking over Speaker B's explanation.\n2.  **[00:29 - 00:35] Speaker A:** This turn overlaps with Speaker B's turn from [00:22 - 00:31]. Speaker A begins speaking at 00:29 with \"up?\" while Speaker B is concluding \"you're already fatigued from your basketball game earlier this afternoon.\" This is a 2-second overlap. While shorter than the first, it still indicates Speaker A starting their turn before Speaker B has finished, impacting the natural flow.\nThere are no long pauses detected. However, the presence of these extended overlaps, particularly the 4-second one, disrupts the natural turn-taking and makes the interaction less fluent and natural.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_overlap5s_dialogsum_testVal/processed_processed_2_val_dialogsum_output/DialogSum--val--2/stereo_dialogue.wav"
+  },
+  "2":{
+    "key": "DialogSum--val--3",
+    "text_input": "[00:00 - 00:02] Speaker A: I need to stop eating such unhealthy foods.\n[00:03 - 00:05] Speaker B: I know what you mean. I've started eating better myself.\n[00:06 - 00:07] Speaker A: What foods do you eat now?\n[00:08 - 00:19] Speaker B: I tend to stick to fruits, vegetables, and lean proteins like chicken and fish, which provide essential amino acids for muscle repair and help maintain a healthy metabolism throughout the day.\n[00:10 - 00:17] Speaker A: Sorry to cut in, but when you say vegetables, are you including things like potatoes or more leafy greens?\n[00:19 - 00:25] Speaker B: Mostly leafy greens, but I do include some root vegetables like carrots. I was going to say chicken as well.\n[00:26 - 00:29] Speaker A: Oh, I see. So, are those the only things you eat?\n[00:30 - 00:34] Speaker B: That's basically what I eat. I try to keep my meals simple and balanced.\n[00:34 - 00:36] Speaker A: Why aren't you eating anything else?\n[00:37 - 00:41] Speaker B: Well, fruits and vegetables are very healthy, and they provide essential nutrients.\n[00:42 - 00:42] Speaker A: And the chicken?\n[00:43 - 00:48] Speaker B: It's really healthy to eat when you bake it, especially if you avoid adding too much oil or seasoning.",
+    "model_output": "<response think>\nThe responses in the dialogue demonstrate strong logical consistency and topic coherence. Speaker B consistently provides relevant information about their diet in response to Speaker A's inquiries. Speaker A's questions, even when interrupting, are directly related to clarifying Speaker B's ongoing explanation. The conversation flows naturally from identifying a problem (unhealthy eating) to exploring a solution (Speaker B's diet) and then seeking clarification and rationale for that solution. There are no instances where a speaker's response is off-topic or illogical in context.\n</response think>\n\n<fluency think>\nInteractional fluency has a significant issue due to an extended overlap. At [00:10 - 00:17], Speaker A explicitly cuts into Speaker B's ongoing turn, creating a 7-second period of simultaneous speech. While Speaker A acknowledges the interruption (\"Sorry to cut in\"), this prolonged overlap is explicitly stated as harmful according to the evaluation criteria (\"prolonged...overlapping turns are harmful\"). Other turn transitions are quite natural, with no long pauses detected. However, this single, extended overlap significantly impacts the overall fluency of the interaction, preventing it from being consistently appropriate and natural.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_overlap5s_dialogsum_testVal/processed_processed_2_val_dialogsum_output/DialogSum--val--3/stereo_dialogue.wav"
+  },
+  "3":{
+    "key": "DialogSum--val--4",
+    "text_input": "[00:00 - 00:01] Speaker A: Do you believe in UFOs?\n[00:02 - 00:03] Speaker B: Of course, they are out there.\n[00:04 - 00:13] Speaker A: But I never thought I'd meet someone who actually believes in them so strongly, especially someone who claims to have direct experiences with extraterrestrial beings.\n[00:05 - 00:09] Speaker B: Are you serious? They are called UFOs, so not everybody can see them.\n[00:14 - 00:16] Speaker A: Wait, you mean that you can see them?\n[00:16 - 00:18] Speaker B: That's right. I can see them in my dreams.\n[00:19 - 00:20] Speaker A: They come to the earth?\n[00:21 - 00:24] Speaker B: No. Their task is to send the aliens here from outer space.\n[00:24 - 00:31] Speaker A: Aliens from outer space? Do you talk to them? What do they usually say when you communicate with them?\n[00:32 - 00:39] Speaker B: OK, OK, one by one, please! They look like robots, but they can speak. Their mission is to make friends with human beings.\n[00:39 - 00:43] Speaker A: That means that you talk to them? In which language?\n[00:43 - 00:46] Speaker B: Of course in English, they learn English on Mars too.\n[00:46 - 00:51] Speaker A: Oh, and what do they look like again? You were about to say before I interrupted.\n[00:52 - 00:57] Speaker B: They look like robots, but with a more organic touch, like they're part machine, part living being.",
+    "model_output": "<response think>\nThe dialogue exhibits strong logical consistency and topic coherence. Speaker B's responses, while introducing increasingly fantastical elements (seeing UFOs in dreams, aliens sending them, aliens learning English on Mars), are consistently presented as answers or elaborations to Speaker A's questions and disbelief. Speaker A, in turn, reacts naturally to these claims, asking clarifying questions or expressing surprise, maintaining a logical flow of inquiry. For example, Speaker A's follow-up questions \"Wait, you mean that you can see them?\" and \"They come to the earth?\" are direct responses to Speaker B's previous statements. Even after the initial overlap, the conversation picks up coherently, with Speaker A referencing Speaker B's previous, interrupted statement (\"You were about to say before I interrupted\"). The overall content progression is coherent, even if the subject matter becomes absurd.\n</response think>\n\n<fluency think>\nThe interactional fluency is significantly hampered by a major extended overlap and a subsequent long pause. At [00:05 - 00:09], Speaker B interrupts Speaker A's prolonged statement ([00:04 - 00:13]) with \"Are you serious? They are called UFOs, so not everybody can see them.\" This is a 4-second overlap, which is prolonged and indicates a breakdown in natural turn-taking. Following this, there is a noticeable pause of 1 second from when Speaker A finishes their initial interrupted thought ([00:13]) until they re-engage with a new question at [00:14]. This long pause, coming directly after an extended overlap, suggests a disruption in the conversational rhythm and flow. While the rest of the dialogue has acceptable small pauses and no other prolonged overlaps, this specific sequence significantly impacts the overall fluency, making the conversation feel less natural and more disjointed at that point.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_overlap5s_dialogsum_testVal/processed_processed_2_val_dialogsum_output/DialogSum--val--4/stereo_dialogue.wav"
+  },
+  "4":{
+    "key": "DialogSum--val--5",
+    "text_input": "[00:00 - 00:01] Speaker A: Did you go to school today?\n[00:02 - 00:03] Speaker B: Of course. Did you?\n[00:03 - 00:05] Speaker A: I didn't want to, so I didn't.\n[00:06 - 00:09] Speaker B: That's sad, but have you gone to the movies recently?\n[00:09 - 00:21] Speaker A: That's a surprising change in topic that caught me off guard since we were just talking about school attendance and suddenly shifted to entertainment options, but no, I haven't gone to the movies lately.\n[00:10 - 00:15] Speaker B: Sorry to jump in, but I was just thinking, is there a specific movie you're interested in?\n[00:21 - 00:27] Speaker A: I was about to say, that's a switch in topic. But no, I haven't gone to the movies. Why?\n[00:28 - 00:30] Speaker B: I really want to go to the movies this weekend.\n[00:30 - 00:31] Speaker A: So go then.\n[00:32 - 00:33] Speaker B: I really don't want to go by myself.\n[00:34 - 00:37] Speaker A: Well anyway, do you plan on going to school tomorrow?\n[00:38 - 00:43] Speaker B: No, I think I'm going to go to the movies. By the way, what were you going to say earlier about the switch in topic?\n[00:43 - 00:47] Speaker A: Oh, I just meant it was a surprising change in conversation.",
+    "model_output": "<response think>\n**Response Relevance Analysis:**\nThe dialogue exhibits significant issues with topic coherence and logical consistency.\n1.  **Abrupt Topic Shifts:** Speaker B initiates an abrupt topic shift from school to movies at [00:06], which Speaker A explicitly notes twice ([00:09 - 00:21] and [00:21 - 00:27]). While Speaker A's acknowledgement of the shift is relevant to the *interaction*, the shift itself disrupts the logical flow.\n2.  **Ignoring Previous Utterances:** After Speaker B expresses a desire to go to the movies but not alone ([00:32 - 00:33]), Speaker A completely ignores this point and abruptly switches the topic *back* to school attendance ([00:34 - 00:37]). This shows a lack of logical consistency in following up on the other speaker's expressed need or dilemma, undermining the collaborative nature of conversation.\n3.  **Multifaceted Responses:** Speaker B's turn at [00:38 - 00:43] attempts to respond to the new topic (school), reintroduce the old topic (movies), and follow up on an earlier meta-comment (the topic switch). While this shows an attempt at relevance, it highlights the scattered nature of the conversation due to the earlier abrupt shifts.\nOverall, while individual answers sometimes respond to the immediate question, the conversation lacks a coherent topic progression, features multiple jarring topic shifts initiated by both speakers, and occasionally ignores the implied conversational goals of the other speaker.\n</response think>\n\n<fluency think>\n**Interactional Fluency Analysis:**\n1.  **Extended Overlaps:** There is a notable extended overlap between [00:09 - 00:21] (Speaker A) and [00:10 - 00:15] (Speaker B). Speaker B begins speaking 1 second into Speaker A's long turn and continues for 5 seconds while Speaker A is still speaking. Speaker B even explicitly acknowledges this with \"Sorry to jump in,\" indicating a clear interruption rather than a brief, natural overlap. This prolonged and intentional interruption significantly harms the interactional fluency, making the conversation feel less natural and more like turn-taking is being actively disrupted.\n2.  **Long Pauses:** There are no long pauses detected between speaker turns. Most pauses are 0 or 1 second, which are natural and acceptable in conversational flow.\nThe primary detractor for interactional fluency is the extended and acknowledged overlap/interruption.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_overlap5s_dialogsum_testVal/processed_processed_2_val_dialogsum_output/DialogSum--val--5/stereo_dialogue.wav"
+  },
+  "5":{
+    "key": "PLACES3.5--train--2002",
+    "text_input": "[00:00 - 00:00] Speaker A: Hi Bob!\n[00:01 - 00:02] Speaker B: Hey Alice!\n[00:02 - 00:06] Speaker A: I'm sorry, I've got to cut the call short for an urgent\n[00:05 - 00:07] Speaker B: Wait, before you go, is everything alright?\n[00:12 - 00:17] Speaker A: Yeah, everything's fine. I just wanted to let you know I won't be able to talk for a while.\n[00:18 - 00:20] Speaker B: That's ok! Let's reschedule!\n[00:20 - 00:24] Speaker A: No worries, I'm just in the middle of work! I'll be\n[00:23 - 00:26] Speaker B: Well, I'm busy working right now too, but I'll call back.\n[00:32 - 00:34] Speaker A: Alright, talk to you later!\n[00:35 - 00:36] Speaker B: You too!",
+    "model_output": "<response think>\nThe dialogue exhibits strong response relevance and logical consistency.\n1.  **[00:02 - 00:06] Speaker A: I'm sorry, I've got to cut the call short for an urgent**\n    **[00:05 - 00:07] Speaker B: Wait, before you go, is everything alright?**\n    Speaker B's immediate interjection shows concern and directly addresses Speaker A's abrupt attempt to end the call, maintaining logical flow.\n2.  **[00:12 - 00:17] Speaker A: Yeah, everything's fine. I just wanted to let you know I won't be able to talk for a while.**\n    Speaker A directly answers B's question and reiterates the initial point of ending the call, maintaining consistency.\n3.  **[00:18 - 00:20] Speaker B: That's ok! Let's reschedule!**\n    Speaker B accepts Speaker A's situation and offers a practical solution, which is perfectly coherent with the topic.\n4.  **[00:20 - 00:24] Speaker A: No worries, I'm just in the middle of work! I'll be**\n    **[00:23 - 00:26] Speaker B: Well, I'm busy working right now too, but I'll call back.**\n    Speaker A clarifies the reason for needing to end the call. Speaker B's response acknowledges A's situation and offers to initiate the next contact (\"I'll call back\"), which, while a bit of a reinterpretation of \"reschedule,\" is still logically consistent with the goal of ending the current call and continuing later. It's not illogical or off-topic.\n5.  The greetings and closings are also entirely appropriate and relevant.\n\nOverall, the responses are consistently appropriate, follow logically, and maintain topic coherence throughout the conversation.\n</response think>\n\n<fluency think>\nThe interaction exhibits significant issues with interactional fluency due to prolonged pauses.\n1.  **Overlap [00:05 - 00:07] Speaker B over [00:02 - 00:06] Speaker A:** There is a 1-second overlap (0:05 to 0:06). This is a brief overlap, common in natural conversation (e.g., interruption or eager response), and thus acceptable.\n2.  **Pause [00:07] Speaker B end to [00:12] Speaker A start:** There is a 5-second pause. This is a prolonged pause and significantly detracts from natural conversational flow, indicating a breakdown in turn-taking efficiency.\n3.  **Overlap [00:23 - 00:26] Speaker B over [00:20 - 00:24] Speaker A:** There is a 1-second overlap (0:23 to 0:24). This is another brief and acceptable overlap.\n4.  **Pause [00:26] Speaker B end to [00:32] Speaker A start:** There is a 6-second pause. This is another prolonged pause, even longer than the previous one, further harming the interactional fluency.\n\nWhile brief overlaps are acceptable, the presence of two notably long pauses (5 seconds and 6 seconds) between turns is a significant fluency issue as per the evaluation criteria, making the conversation feel unnatural and stilted.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/audio_error_audios/place_2_processed_dialogues_part_1(1)/PLACES3.5--train--2002/stereo_dialogue.wav"
+  },
+  "6":{
+    "key": "PLACES3.5--train--2005",
+    "text_input": "[00:00 - 00:02] Speaker A: What do you know about braiding hair?\n[00:02 - 00:08] Speaker B: I have a friend who is a professional hair braider. We get together for coffee every couple of weeks, and\n[00:07 - 00:10] Speaker A: Really?! That's so cool! How did you get into that?\n[00:16 - 00:23] Speaker B: You know, I just heard about it one day. She did a really good job so I decided to let her do mine. And I've kept on going back because I\n[00:22 - 00:25] Speaker A: That's awesome. Do you mind if I take a picture?\n[00:32 - 00:32] Speaker B: Sure.\n[00:32 - 00:34] Speaker A: But I won't upload it anywhere.\n[00:35 - 00:37] Speaker B: Oh, good! Do you like hair braiding?\n[00:38 - 00:42] Speaker A: It's pretty awesome. I like how it gives your hair a really natural look.\n[00:42 - 00:44] Speaker B: When do you think you'll be ready to give it a try?\n[00:45 - 00:51] Speaker A: I don't think I have time to braid hair right now, but maybe in the future when things settle down a bit.",
+    "model_output": "<response think>\nThe dialogue generally demonstrates good response relevance and topic coherence. Speaker A initiates the conversation about hair braiding, and Speaker B responds by sharing a personal connection to the topic. Speaker A's follow-up questions and comments logically build on Speaker B's statements. The conversation maintains its focus on hair braiding throughout.\n\nHowever, there is one instance where the logical consistency and topic coherence are slightly strained. At [00:22 - 00:25], Speaker A asks, \"That's awesome. Do you mind if I take a picture?\" This question, while not completely irrelevant to the overall topic of hair braiding, comes immediately after Speaker B was explaining *how* they got into hair braiding (\"And I've kept on going back because I\"). The sudden shift from discussing Speaker B's personal history and experience to Speaker A requesting to take a picture of Speaker B's hair (implied) feels a bit abrupt and slightly out of sync with the immediate conversational thread. It's not a complete non-sequitur, but it slightly disrupts the narrative flow about B's experience. Despite this minor point, the speakers do manage to recover and bring the conversation back to the general topic of hair braiding, demonstrating overall topic coherence.\n</response think>\n\n<fluency think>\nInteractional fluency in this dialogue presents significant issues due to prolonged pauses.\n\n1.  **Long Pause 1:** There is a **6-second pause** between Speaker A's turn ending at [00:10] and Speaker B's turn starting at [00:16]. This is a very noticeable and prolonged silence that disrupts the natural conversational flow.\n2.  **Long Pause 2:** Another significant pause of **7 seconds** occurs between Speaker A's turn ending at [00:25] and Speaker B's turn starting at [00:32]. This is an even longer and more disruptive silence, indicating a breakdown in the smooth back-and-forth of the conversation.\n\nIn addition to the long pauses, Speaker B's turns end abruptly or trail off twice:\n*   [00:08] \"...every couple of weeks, and\"\n*   [00:23] \"...kept on going back because I\"\nWhile this could be due to interruption, it still contributes to a feeling of incomplete thoughts or a hesitant conversational style, impacting fluency.\n\nThere are also brief overlaps, for example:\n*   [00:07 - 00:10] Speaker A overlaps with Speaker B for about 1 second.\n*   [00:22 - 00:25] Speaker A overlaps with Speaker B for about 1 second.\nAs per the instructions, brief overlaps are acceptable and can indicate engagement. However, when combined with the prolonged pauses, these small overlaps do not compensate for the overall lack of fluency caused by the silences. The most significant issue for fluency is clearly the two extended pauses.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/audio_error_audios/place_2_processed_dialogues_part_1(1)/PLACES3.5--train--2005/stereo_dialogue.wav"
+  },
+  "7":{
+    "key": "PLACES3.5--train--2006",
+    "text_input": "[00:00 - 00:01] Speaker A: So is my newborn potty training?\n[00:02 - 00:03] Speaker B: No, I don't think so.\n[00:04 - 00:08] Speaker A: Yeah, I thought so. It's really hard, but I am going to keep at it!\n[00:09 - 00:13] Speaker B: I remember when my daughter was first born. It was really challenging to\n[00:12 - 00:18] Speaker A: Overwhelming, right? I know exactly what you mean! It can feel like you are completely losing control.\n[00:25 - 00:29] Speaker B: I hear ya. Now my daughter is two. She is such an amazing little person.\n[00:29 - 00:32] Speaker A: Yeah, I guess kids grow up fast.\n[00:32 - 00:37] Speaker B: I love listening to the stories my daughter tells me about what happened at daycare. She\n[00:36 - 00:38] Speaker A: Oh, what's her favorite story?\n[00:44 - 00:46] Speaker B: My daughter loves the book Where The Wild Things Are.\n[00:46 - 00:49] Speaker A: Wow, that sounds like an interesting book.\n[00:49 - 00:56] Speaker B: I haven't read it myself, but my daughter tells me how the main character's parents are never around so he just runs around the house causing mischief.",
+    "model_output": "<response think>\nThe dialogue demonstrates strong logical consistency and topic coherence. Speakers consistently respond to each other's statements and questions in a relevant manner.\n- Speaker B's initial response \"No, I don't think so\" directly answers Speaker A's question about potty training.\n- Speaker A's follow-up (\"Yeah, I thought so. It's really hard...\") is a relevant elaboration.\n- Speaker B's shift to personal experience (\"I remember when my daughter was first born...\") is a natural progression when discussing challenges with newborns.\n- Speaker A's \"Overwhelming, right? I know exactly what you mean!\" is an empathetic and relevant continuation of B's incomplete thought, demonstrating active listening and shared understanding.\n- The conversation naturally flows through stages of parenting (newborn challenges to older child joys) and sub-topics (stories, books).\n- Speaker A's question \"Oh, what's her favorite story?\" directly follows B's mention of listening to stories.\n- Speaker B's answer about \"Where The Wild Things Are\" and subsequent elaboration on the book's content are all directly relevant to A's query.\nThere are no instances of off-topic remarks, misunderstandings, or illogical jumps in conversation.\n</response think>\n\n<fluency think>\nInteractional fluency has significant issues due to prolonged pauses.\n- **Overlap at [00:12 - 00:18]:** Speaker A starts talking at 0:12 while Speaker B is still speaking until 0:13 (\"challenging to\"). This is a brief 1-second overlap. While the prompt allows brief overlaps, this one occurs while Speaker B is mid-sentence, indicating Speaker A is cutting off Speaker B, which can disrupt flow.\n- **Long Pause at [00:18 - 00:25]:** There is a 7-second pause between Speaker A finishing their turn and Speaker B starting. This is a significant and prolonged pause, which clearly harms the natural flow and feels unnatural for a conversation.\n- **Overlap at [00:36 - 00:38]:** Speaker A starts talking at 0:36 while Speaker B is still speaking until 0:37 (\"She\"). This is another brief 1-second overlap, again cutting off Speaker B mid-sentence.\n- **Long Pause at [00:38 - 00:44]:** There is a 6-second pause between Speaker A finishing their turn and Speaker B starting. This is another significant and prolonged pause, similar to the previous one, and detrimental to fluency.\n\nThe two prolonged pauses (7 seconds and 6 seconds) are explicit issues according to the criteria (\"prolonged pauses... are harmful\"). While the overlaps are brief, their nature (cutting off the other speaker) combined with the long pauses severely impacts the interactional fluency, making the conversation feel stilted and unnatural despite its high relevance.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/audio_error_audios/place_2_processed_dialogues_part_1(1)/PLACES3.5--train--2006/stereo_dialogue.wav"
+  },
+  "8":{
+    "key": "PLACES3.5--train--2009",
+    "text_input": "[00:00 - 00:01] Speaker A: Hey! How are you?\n[00:02 - 00:05] Speaker B: I'm doing pretty well. I've had a busy week at work.\n[00:05 - 00:07] Speaker A: What do you do for a living?\n[00:07 - 00:14] Speaker B: I work as a geophysicist. I enjoy geology and mineralogy, but I especially enjoy studying seismic\n[00:13 - 00:19] Speaker A: Oh, that's interesting! Speaking of geology, have you ever come across any rare minerals during your work?\n[00:25 - 00:31] Speaker B: Yes, I've encountered a few rare ones, but I was about to mention that I also have a strong interest in botany.\n[00:31 - 00:36] Speaker A: I like scenery and the outdoor environment. Have you ever tried flower arranging?\n[00:36 - 00:37] Speaker B: Sure! Have you?\n[00:38 - 00:41] Speaker A: Yeah, I have. I used to do it as a hobby in college.",
+    "model_output": "<response think>\nThe dialogue starts with a clear and relevant exchange. Speaker A asks \"How are you?\" and Speaker B responds, then Speaker A follows up by asking about Speaker B's profession, which is a natural progression.\n\nThe first minor issue regarding relevance appears at [00:13 - 00:19]. Speaker B is in the middle of explaining their work (\"I enjoy geology and mineralogy, but I especially enjoy studying seismic\"), and Speaker A interjects. While Speaker A's follow-up question (\"Speaking of geology, have you ever come across any rare minerals during your work?\") is related to what Speaker B has already mentioned (geology, mineralogy), the interruption itself slightly disrupts the logical flow of Speaker B's explanation. Speaker B then has to abruptly pivot from their incomplete sentence about \"seismic\" studies.\n\nA more significant issue with topic coherence occurs from [00:25 - 00:36]. Speaker B clearly signals a topic shift by stating, \"Yes, I've encountered a few rare ones, but I was about to mention that I also have a strong interest in botany.\" This is an explicit attempt to introduce a new subject. Speaker A's response, \"I like scenery and the outdoor environment. Have you ever tried flower arranging?\", is *tangentially* related to botany (flowers are plants), but it feels like a superficial jump rather than an engagement with the stated \"strong interest in botany.\" \"Botany\" implies the scientific study of plants, not just an appreciation of scenery or a specific craft like flower arranging. Speaker A misses an opportunity to delve into Speaker B's *strong interest* in botany and instead shifts to a more casual, less intellectually aligned topic. This suggests a slight lack of deep engagement with Speaker B's stated interest, pulling the conversation away from the intended topic pivot.\n\nThe subsequent turns about flower arranging are logically consistent within that narrow subtopic, but the initial transition to it from \"strong interest in botany\" isn't perfectly coherent.\n</response think>\n\n<fluency think>\nThe dialogue exhibits significant issues with interactional fluency, specifically regarding overlaps.\n\nAt [00:13 - 00:19], Speaker A's turn begins at [00:13], while Speaker B's turn is still ongoing until [00:14]. This is a clear overlap of 1 second (\"studying seismic\" being cut off by \"Oh, that's interesting!\"). This indicates that Speaker A interrupted Speaker B, preventing Speaker B from completing their thought or sentence.\n\nAnother very noticeable and problematic overlap occurs at [00:25 - 00:31]. Speaker B's turn begins at [00:25] and continues to [00:31]. However, Speaker A's turn also begins at [00:31], creating an instant and complete overlap with the very end of Speaker B's sentence (\"...I also have a strong interest in botany\"). This means Speaker A started speaking precisely as Speaker B was finishing, without any pause or wait for Speaker B's full utterance.\n\nThese two instances are not brief, acceptable overlaps. The first one is an interruption, cutting off Speaker B. The second is an immediate overlap at the end of Speaker B's turn, which suggests a lack of turn-taking fluidity, potentially making Speaker B feel unheard or rushed.\n\nThere are no long pauses between speaker turns, which is a positive, but the presence of extended and impactful overlaps significantly detracts from the interactional fluency.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/audio_error_audios/place_2_processed_dialogues_part_1(1)/PLACES3.5--train--2009/stereo_dialogue.wav"
+  },
+  "9":{
+    "key": "PLACES3.5--train--2010",
+    "text_input": "[00:00 - 00:08] Speaker A: I've been really excited about exploring new cities. I feel like I need a break from my current city, so I'm thinking about spending the summer in Europe.\n[00:09 - 00:12] Speaker B: I always love taking vacations. Where would you like to visit first?\n[00:11 - 00:18] Speaker A: Sorry to cut in, but I wanted to mention Spain, France, Italy—those are the top places on my list.\n[00:24 - 00:28] Speaker B: I'm sure those places will be incredible! Are you hoping to get a teaching job while you travel?\n[00:29 - 00:41] Speaker A: Actually, that's something I've been considering. Teaching English could be a great way to immerse myself in the culture, but I'm also thinking about just taking the time to relax and explore the\n[00:40 - 00:48] Speaker B: I totally get that! Sometimes it's important to just unwind and not think about work. Have you looked into any specific cities in those countries yet?",
+    "model_output": "<response think>\nThe dialogue exhibits strong response relevance and logical consistency. Speaker A introduces the topic of wanting a break and exploring Europe. Speaker B responds appropriately by asking for more details about preferred destinations. Speaker A lists countries, which Speaker B acknowledges before smoothly transitioning to a related, but slightly new, topic about finding a teaching job while traveling. Speaker A addresses this new topic directly, and Speaker B acknowledges Speaker A's perspective before bringing the conversation back to specific travel plans (cities). The flow of topics is natural and coherent throughout the conversation.\n</response think>\n\n<fluency think>\nThere are two instances of brief overlaps:\n- `[00:11 - 00:18] Speaker A:` starts at 00:11 while `[00:09 - 00:12] Speaker B:` ends at 00:12. This is a 1-second overlap. Speaker A explicitly apologizes (\"Sorry to cut in\"), indicating awareness and that it's not a major disruption, fitting the \"brief overlaps are acceptable\" criterion.\n- `[00:40 - 00:48] Speaker B:` starts at 00:40 while `[00:29 - 00:41] Speaker A:` ends at 00:41. This is also a 1-second overlap, which is brief and acceptable.\n\nHowever, there is a significant long pause:\n- From `[00:11 - 00:18] Speaker A:` ending at 00:18 to `[00:24 - 00:28] Speaker B:` starting at 00:24, there is a 6-second pause. This is a \"prolonged pause\" and is harmful to interactional fluency, as per the scoring criteria.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/audio_error_audios/place_2_processed_dialogues_part_1(1)/PLACES3.5--train--2010/stereo_dialogue.wav"
+  },
+  "10":{
+    "key": "SODA_PROCESSED--val--135940",
+    "text_input": "[00:00 - 00:04] Speaker A: Thank you so much for renting this apartment to me. I really appreciate it.\n[00:04 - 00:11] Speaker B: No problem, Rayaan. I'm glad you like it. It's always nice to have a new tenant who is excited about their new home. How do you like the layout of\n[00:10 - 00:23] Speaker A: Sorry to jump in, but I just wanted to mention how much I love the area! It's so different from my old neighborhood. Everything is so clean and well-kept here. I feel much safer walking around at night too.\n[00:23 - 00:32] Speaker B: That makes me happy to hear! We take a lot of pride in our community, and we want everyone to feel safe and comfortable here. If you ever need anything, don't hesitate to reach out\n[00:31 - 00:40] Speaker A: Actually, speaking of that, I was wondering if there's a local contact number I can reach in case of any emergencies or issues with the apartment?\n[00:40 - 00:47] Speaker B: Absolutely, I'll provide you with that. And as I was saying, we're always happy to help out our tenants however we can.",
+    "model_output": "<response think>\nThe dialogue demonstrates strong response relevance and logical consistency. Each speaker's turn directly addresses or builds upon the previous turn.\n- Speaker A expresses thanks for the apartment.\n- Speaker B responds appropriately, expresses gladness, and attempts to continue the conversation by asking about the layout.\n- Speaker A, while interrupting, immediately qualifies it with \"Sorry to jump in,\" and then shifts to a related topic (the area/neighborhood). This is a natural conversational shift for someone excited about a new place.\n- Speaker B acknowledges A's sentiment about the area and expands on it, mentioning community pride and offering help.\n- Speaker A then smoothly transitions from B's offer of help (\"If you ever need anything, don't hesitate to reach out\") to a specific, practical question about a local contact number, using \"speaking of that.\"\n- Speaker B provides a direct answer and reiterates their willingness to help.\nThe topic coherence is maintained throughout, moving from the apartment itself to the surrounding area, and then to practical aspects of being a tenant, all logically connected.\n</response think>\n\n<fluency think>\nThe dialogue exhibits good interactional fluency with some brief overlaps, which are explicitly stated as acceptable by the criteria.\n- There is a 1-second overlap at `[00:10 - 00:23]` where Speaker A begins speaking while Speaker B is still finishing their sentence `[00:04 - 00:11]`. This overlap is immediately acknowledged by Speaker A with \"Sorry to jump in,\" indicating awareness and natural conversational eagerness rather than a breakdown in communication.\n- There is another 1-second overlap at `[00:31 - 00:40]` where Speaker A starts speaking just as Speaker B finishes their turn `[00:23 - 00:32]`. This is also a brief overlap and doesn't hinder comprehension or flow.\nThe criteria state that \"brief overlaps in conversation are acceptable, while prolonged pauses and overlapping turns are harmful.\" These 1-second overlaps are brief and contribute to a natural, dynamic conversational rhythm rather than indicating a problem. There are no long pauses detected between turns.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_2/soda_4_processed_dialogues_part_14/SODA_PROCESSED--val--135940/stereo_dialogue.wav"
+  },
+  "11":{
+    "key": "SODA_PROCESSED--val--132565",
+    "text_input": "[00:00 - 00:07] Speaker A: Hey, Lola. I know you've been admiring my watch for a while now and I wanted to show you that I care about you by giving it to you as\n[00:06 - 00:11] Speaker B: Wait, are you really sure about this? I don't want to take something that means so much to you.\n[00:12 - 00:18] Speaker A: Of course I'm sure. I want you to have it because it'll make you happy and I care about making you\n[00:17 - 00:21] Speaker B: Your happiness is important too, Kemoni. Are you really okay with parting with it?",
+    "model_output": "<response think>\nThe dialogue exhibits excellent response relevance. Speaker B's initial hesitation (\"Wait, are you really sure about this? I don't want to take something that means so much to you.\") directly responds to Speaker A's offer and the implied sentiment behind it. Speaker A's subsequent reassurance (\"Of course I'm sure. I want you to have it because it'll make you happy and I care about making you\") logically follows and addresses B's concerns. Finally, Speaker B's last statement (\"Your happiness is important too, Kemoni. Are you really okay with parting with it?\") continues the thread of mutual care and concern for the other's well-being, directly building on Speaker A's previous statement about making B happy. The conversation maintains strong topic coherence, staying focused on the watch, the act of giving/receiving, and the emotional implications for both parties. Each turn builds on the previous one in a natural and logical manner.\n</response think>\n\n<fluency think>\nThe interactional fluency is very good. There are two brief overlaps detected:\n- [00:06 - 00:07]: Speaker A: \"as\" and Speaker B: \"Wait, are you really sure about this?\" (1-second overlap)\n- [00:17 - 00:18]: Speaker A: \"making you\" and Speaker B: \"Your happiness is important too, Kemoni.\" (1-second overlap)\nThese are very short, natural overlaps that are common in fluent, spontaneous conversation, indicating engagement and eagerness to respond rather than interruption or conflict. There is also a brief 1-second pause between Speaker B's first turn ending at 00:11 and Speaker A's second turn starting at 00:12. These small pauses and brief overlaps are explicitly stated as acceptable in the scoring criteria and do not hinder the flow of the conversation. There are no prolonged pauses or extended overlapping turns that would be considered harmful.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_2/soda_4_processed_dialogues_part_14/SODA_PROCESSED--val--132565/stereo_dialogue.wav"
+  },
+  "12":{
+    "key": "SODA_PROCESSED--val--31917",
+    "text_input": "[00:00 - 00:04] Speaker A: It's so nice in here. The warmth is really comforting, especially\n[00:04 - 00:10] Speaker B: Sorry to cut in, but I just noticed the scent of the candles. Do you know what fragrance it is? It's really soothing.\n[00:11 - 00:24] Speaker A: I love the smell of the candles too. I think it's a mix of lavender and vanilla, which makes it even more calming. By the way, the roses are beautiful. It's such a shame they only last a short period before\n[00:23 - 00:29] Speaker B: True, but don't you think that's what makes them even more special? The fact that they're so fleeting?",
+    "model_output": "<response think>\n**Response Relevance:**\n- **Logical Consistency:** The dialogue demonstrates strong logical consistency. Speaker B's initial interruption is acknowledged (\"Sorry to cut in\"), making it a polite and understandable deviation. Speaker A then directly answers Speaker B's question about the candle scent before smoothly transitioning to a new topic (roses) with \"By the way.\" Speaker B's final response directly addresses Speaker A's comment about the roses' short lifespan, offering a philosophical counterpoint. Each turn builds logically on the previous one, either by directly responding or by managing a smooth topic shift.\n- **Topic Coherence:** The conversation maintains good topic coherence. It starts with general comfort, transitions to a specific sensory detail (candles), and then to another specific sensory detail (roses). These topics are all related to the immediate environment and pleasant observations. The transitions, especially Speaker A's \"By the way,\" are natural and well-managed, preventing abruptness. There are no irrelevant or off-topic statements.\n</response think>\n\n<fluency think>\n**Interactional Fluency:**\n- **Extended Overlaps:** There are two instances where overlaps are suggested by the timestamps.\n    - At [00:04], Speaker B starts speaking as Speaker A's turn ends. Speaker B says, \"Sorry to cut in,\" indicating an awareness of interrupting or overlapping Speaker A's previous sentence (\"...especially\"). This is a brief, acknowledged overlap, which is common and acceptable in natural conversation, not an extended or harmful one.\n    - At [00:23], Speaker B starts speaking while Speaker A is still talking (\"before\" at 00:24). This is a very brief overlap (approximately 1 second). This is a \"brief overlap\" as described in the criteria and is acceptable.\n- **Long Pauses:** There is one noticeable pause:\n    - Between Speaker B's first turn (ends at 00:10) and Speaker A's second turn (starts at 00:11), there is a 1-second pause. This is a \"small pause\" and is well within acceptable limits for natural conversation.\nOverall, the interaction flows very naturally with no prolonged pauses or extended, disruptive overlaps. The minor overlaps and pauses are typical of fluent human conversation.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_2/soda_4_processed_dialogues_part_11/SODA_PROCESSED--val--31917/stereo_dialogue.wav"
+  },
+  "13":{
+    "key": "SODA_PROCESSED--val--137529",
+    "text_input": "[00:00 - 00:05] Speaker A: I can't believe you're still out here! It's horrible weather, you should really come inside before\n[00:04 - 00:09] Speaker B: No, I'm fine, I don't mind the rain. Actually, I find it quite refreshing— it helps\n[00:08 - 00:13] Speaker A: Are you sure? You look like you're about to collapse. Here, let me help you up.\n[00:14 - 00:19] Speaker B: Thanks, but I'm really okay. I just wanted to enjoy the storm for a little while longer. It's so calming in\n[00:19 - 00:26] Speaker A: Exhilarating? I get that, but your health is more important. Seriously, you should come inside.\n[00:26 - 00:31] Speaker B: If you say so. I'm going back inside, but I'll leave the door open for you in case you change your mind.\n[00:31 - 00:32] Speaker A: Thanks, Aine.",
+    "model_output": "<response think>\nThe dialogue demonstrates strong response relevance and logical consistency. Speaker A consistently expresses concern for Speaker B's well-being in the bad weather and urges them to come inside. Speaker B consistently resists this notion, explaining their enjoyment of the weather, but eventually concedes.\n- At [00:04-00:09], Speaker B directly addresses Speaker A's concern, explaining their preference for the rain.\n- At [00:08-00:13], Speaker A escalates their concern, offering physical assistance, which is a logical follow-up to B's initial dismissal.\n- At [00:14-00:19], Speaker B reiterates their state and reason, maintaining their stance.\n- At [00:19-00:26], Speaker A slightly misinterprets B's last word (\"calming\" heard as \"exhilarating\" or something similar), but immediately links it back to the core topic of health and coming inside, maintaining topic coherence. This slight misinterpretation does not derail the conversation.\n- At [00:26-00:31], Speaker B's concession and offer (\"I'm going back inside, but I'll leave the door open for you\") is a logical conclusion to the persuasion attempt, assuming Speaker A is also outside or was outside trying to get Speaker B inside.\n- At [00:31-00:32], Speaker A's \"Thanks, Aine\" is a direct and relevant acknowledgment of B's offer.\nOverall, the conversation flows logically, and each speaker's turn is relevant to the preceding turn and the overarching topic.\n</response think>\n\n<fluency think>\nThe interactional fluency is excellent.\n- There are brief overlaps at [00:04 - 00:05] (1 second) and [00:08 - 00:09] (1 second). These are short and appear to be natural instances of speakers interrupting or interjecting, indicating engagement and urgency (on Speaker A's part, and a desire to respond on Speaker B's part). As per the guidelines, \"small pauses and brief overlaps in conversation are acceptable,\" and these fall within that acceptable range. They do not constitute \"extended overlaps\" or cause disruption.\n- There are no long pauses between speaker turns. The transitions between turns are smooth and natural, contributing to a fluid conversation. For instance, the gap between [00:13] and [00:14] is only 1 second, which is a natural pause for turn-taking.\nThe conversation feels natural and unimpeded by issues of timing or overlap.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_2/soda_4_processed_dialogues_part_14/SODA_PROCESSED--val--137529/stereo_dialogue.wav"
+  },
+  "14":{
+    "key": "SODA_PROCESSED--val--63644",
+    "text_input": "[00:00 - 00:11] Speaker A: Hey Noemi, I was just admiring your signature and I'm really impressed with how intricate and well-defined it is. It's definitely one of the best signatures I've ever seen. I\n[00:10 - 00:17] Speaker B: Oh, thank you! I actually put a lot of time and effort into perfecting it. A lot of people don't realize how important having a good signature can be.\n[00:18 - 00:27] Speaker A: I completely agree! Your signature is so unique and it really stands out. It must take you forever to write it out every time,\n[00:26 - 00:35] Speaker B: Actually, not anymore. It doesn't take me too long these days since I've had a lot of practice. But yeah, back when I first started working on it, it took me ages to get it right.\n[00:36 - 00:44] Speaker A: It definitely shows how much dedication and effort you put into it. Do you mind if I ask what inspired you to create such a detailed signature?\n[00:45 - 00:54] Speaker B: Not at all! I actually came up with the idea when I was trying to think of a way to make my name stand out more. I figured that if I had a really cool signature, people would remember my name better.\n[00:55 - 01:07] Speaker A: That makes total sense! And it definitely worked because like I said, your signature is really unique and eye-catching. So, do you have any advice for someone who is trying to create their own signature?\n[01:08 - 01:16] Speaker B: Yeah, definitely practice a lot! It takes time to perfect your technique and find the style that works best for you. But it's definitely worth it in the end.",
+    "model_output": "<response think>\nThe dialogue demonstrates excellent response relevance. Each speaker's turn logically follows the previous one, maintaining strong topic coherence throughout the conversation about Noemi's signature.\n- Speaker B's initial response directly addresses Speaker A's admiration and expands on the importance of a good signature.\n- Speaker A's subsequent comments and questions build naturally on Speaker B's statements (e.g., asking about the time it takes, then about inspiration, and finally for advice).\n- Speaker B's answers are always direct, relevant, and provide additional details or opinions pertinent to the discussion.\nThere are no instances of illogical leaps, off-topic remarks, or inconsistent information. The conversation flows smoothly from one point to the next, indicating a strong understanding and engagement between the speakers.\n</response think>\n\n<fluency think>\nThe interactional fluency in this dialogue is very good.\n- **Overlaps:** There are two brief overlaps detected:\n    - [00:10 - 00:11] Speaker A and Speaker B (1 second overlap)\n    - [00:26 - 00:27] Speaker A and Speaker B (1 second overlap)\nThese are short and natural, indicating active listening and turn-taking without being disruptive or prolonged. As per the instructions, brief overlaps are acceptable.\n- **Pauses:** There are brief pauses (1 second) between most turns where one speaker finishes and the other begins, for example:\n    - Speaker B ends at 00:17, Speaker A starts at 00:18.\n    - Speaker B ends at 00:35, Speaker A starts at 00:36.\n    - Speaker A ends at 00:44, Speaker B starts at 00:45.\n    - Speaker B ends at 00:54, Speaker A starts at 00:55.\n    - Speaker A ends at 01:07, Speaker B starts at 01:08.\nThese short pauses are natural for conversational turn-taking and do not suggest any awkwardness or hesitation. There are no long or prolonged pauses that would disrupt the flow. The conversation feels natural and well-paced.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_2/soda_4_processed_dialogues_part_18/SODA_PROCESSED--val--63644/stereo_dialogue.wav"
+  },
+  "15":{
+    "key": "DialogSum--test--14",
+    "text_input": "[00:00 - 00:01] Speaker A: What time is it, Tom?\n[00:01 - 00:04] Speaker B: Just a minute. It's ten to nine by my watch, but\n[00:03 - 00:08] Speaker A: Ten to nine? I had no idea it was so late. I must be off now.\n[00:09 - 00:10] Speaker B: What's the hurry?\n[00:10 - 00:12] Speaker A: I must catch the nine-thirty train.\n[00:13 - 00:22] Speaker B: You've plenty of time yet. The railway station is very close. It won't take more than twenty minutes to get there. By the way, you didn't finish—what were you saying about your watch earlier?",
+    "model_output": "<response think>\n**Response Relevance:**\n- **Logical Consistency:** The dialogue demonstrates excellent logical consistency. Speaker A's initial question about the time leads directly to Speaker B's answer. Speaker A's reaction (\"Ten to nine? I had no idea it was so late. I must be off now.\") is a direct and logical consequence of the time given. Speaker B's \"What's the hurry?\" directly follows Speaker A's need to leave. Speaker A's explanation (\"I must catch the nine-thirty train\") directly answers Speaker B's question. Speaker B's subsequent advice (\"You've plenty of time yet. The railway station is very close.\") logically addresses Speaker A's perceived urgency.\n- **Topic Coherence:** The conversation remains highly coherent. The primary topic revolves around the time and Speaker A's travel plans. The slight digression by Speaker B at the end (\"By the way, you didn't finish—what were you saying about your watch earlier?\") is a natural and coherent move, as it directly refers back to Speaker B's *own* unfinished statement from the beginning of the conversation (\"It's ten to nine by my watch, but...\"), which was interrupted by Speaker A. This demonstrates good memory and a desire to complete a thought, which is very natural in conversation.\n</response think>\n\n<fluency think>\n**Interactional Fluency:**\n- **Extended Overlaps:** There is one instance of overlap: Speaker A (00:03 - 00:08) begins speaking while Speaker B (00:01 - 00:04) is still saying \"but\". This overlap lasts for approximately 1 second (from 00:03 to 00:04). According to the criteria, \"brief overlaps in conversation are acceptable.\" This overlap is brief and seems natural, indicating Speaker A's immediate reaction and urgency upon hearing the time, cutting off Speaker B's incomplete thought. It does not hinder understanding or disrupt the flow significantly.\n- **Long Pauses:** There are no long pauses between speaker turns.\n    - The pause between Speaker A's turn ending at 00:08 and Speaker B's turn starting at 00:09 is 1 second, which is a small, natural conversational pause.\n    - The pause between Speaker A's turn ending at 00:12 and Speaker B's turn starting at 00:13 is also 1 second, again, a natural pause.\nOverall, the interaction flows very smoothly, typical of a natural conversation.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_2/processed_2_test_dialogsum/DialogSum--test--14/stereo_dialogue.wav"
+  },
+  "16":{
+    "key": "SODA_PROCESSED--train--841295",
+    "text_input": "[00:00 - 00:02] Speaker A: I'm going to plow the field today.\n[00:02 - 00:05] Speaker B: Okay, sounds good. Do you need any help?\n[00:05 - 00:07] Speaker A: No, I think I can handle it.\n[00:07 - 00:10] Speaker B: All right, well let me know if you need anything.\n[00:10 - 00:11] Speaker A: I will. Thanks.\n[00:11 - 00:14] Speaker B: Tavin, why do you want to plow the field?\n[00:15 - 00:20] Speaker A: Well, if I don't clear the field of debris, my crops won't be able to grow properly,\n[00:19 - 00:22] Speaker B: Excuse me for interrupting, but I've been wondering —— What\n[00:22 - 00:27] Speaker A: The weather has been quite nice lately, hasn't it? I think we might get some rain tomorrow.\n[00:27 - 00:31] Speaker B: That's not what I asked! I wanted to know about your crops, not the weather forecast.",
+    "model_output": "<response think>\nThe response relevance in this dialogue significantly deteriorates towards the end. Initially, the conversation is logically consistent and coherent: Speaker A states an intention, Speaker B offers help, and Speaker A declines. Speaker B then asks for clarification (\"why do you want to plow the field?\"), and Speaker A provides a relevant reason.\n\nHowever, a major breakdown occurs at [00:22 - 00:27]. Speaker B attempts to ask a question (\"I've been wondering —— What\"), but Speaker A completely ignores this, and instead abruptly changes the topic to the weather. This is a severe breach of logical consistency and topic coherence, as it is entirely unrelated to the previous discussion about farming and crops. Speaker B explicitly points out this irrelevance at [00:27 - 00:31], stating \"That's not what I asked! I wanted to know about your crops, not the weather forecast,\" confirming the lack of relevance from Speaker A's side. This abrupt and unprompted topic shift, ignoring a direct question, indicates significant issues with response relevance.\n</response think>\n\n<fluency think>\nInteractional fluency is problematic due to how the speakers handle turn-taking and topic management.\n\nThere is a brief overlap at [00:19 - 00:22] where Speaker B attempts to interrupt Speaker A. While the overlap itself is brief (around 1 second, from 0:19 to 0:20), Speaker B explicitly states \"Excuse me for interrupting,\" which indicates an awareness of attempting to interject. The issue isn't the brief overlap itself, but Speaker A's *response* to it. Instead of acknowledging Speaker B's attempted interjection or question, Speaker A completely disregards it and abruptly changes the topic to the weather at [00:22 - 00:27]. This demonstrates a severe lack of interactional fluency. Speaker A fails to engage with Speaker B's attempted turn, leading to a breakdown in conversational flow and causing frustration for Speaker B, as evidenced by their direct challenge at [00:27 - 00:31]. This is not a natural back-and-forth and disrupts the smooth progression of the conversation. There are no prolonged pauses.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/text_error_audios/processed_cut_soda_3_processed_dialogues_part_14_output/SODA_PROCESSED--train--841295/stereo_dialogue.wav"
+  },
+  "17":{
+    "key": "SODA_PROCESSED--train--1042183",
+    "text_input": "[00:00 - 00:02] Speaker A: I'm sorry, I know I've hurt you.\n[00:02 - 00:07] Speaker B: You've hurt me more than you can imagine. How could you do this to me? To our family?\n[00:07 - 00:12] Speaker A: I don't know. It just happened. I wasn't thinking clearly,\n[00:11 - 00:20] Speaker B: Excuse me for interrupting, but I need to ask - when you say you weren't thinking clearly, what exactly do you mean by that? Was there some specific factor that clouded your judgment?\n[00:21 - 01:20] Speaker A: Oh my goodness, let me explain in great, extensive, and excruciating detail about the numerous, countless and multifarious factors that contributed to my lack of clear thinking! First and foremost, there was the overwhelming, all-consuming and utterly debilitating stress from work that completely, totally and absolutely dominated my mental faculties. This stress was not just ordinary stress, mind you, but an extraordinary, unprecedented and unparalleled level of stress that made rational, logical and coherent thinking virtually impossible, unattainable and out of reach! Additionally, there were the compounding, accumulating and multiplying personal issues that further exacerbated, worsened and intensified my already compromised mental state! These included financial worries, family obligations, and health concerns that all converged, combined and coalesced into one giant, massive and enormous mental block that prevented me from thinking clearly, rationally or sensibly!",
+    "model_output": "<response think>\nThe initial turns of the dialogue demonstrate good response relevance, with each speaker's contribution logically following the previous one and maintaining topic coherence. Speaker A apologizes, Speaker B expresses hurt and asks for clarification, and Speaker A provides a preliminary explanation. Speaker B then logically asks for more specific details about Speaker A's claim of \"not thinking clearly.\"\n\nHowever, Speaker A's final response (00:21 - 01:20) presents a significant issue in terms of its appropriateness and naturalness within the context of a dialogue. While the *content* of the response technically addresses Speaker B's question about factors clouding judgment (thus maintaining logical consistency and topic coherence on a superficial level), the *delivery* is extremely problematic. Speaker A launches into an excessively verbose, repetitive, and theatrical monologue, using highly exaggerated language (\"numerous, countless and multifarious factors,\" \"overwhelming, all-consuming and utterly debilitating stress,\" \"extraordinary, unprecedented and unparalleled level of stress,\" etc.). This level of verbosity and dramatic flair is completely unnatural for a conversational turn, especially one in response to a simple request for clarification. It transforms the dialogue into a one-sided speech, making the response largely irrelevant to the goal of a natural, back-and-forth interaction. The quality of the interaction is severely degraded by this unnatural and disproportionate response.\n</response think>\n\n<fluency think>\nThere is a brief overlap between Speaker A's turn (00:07 - 00:12) and Speaker B's turn (00:11 - 00:20), lasting for approximately 1 second (00:11-00:12). This is a brief overlap and, according to the scoring criteria, is acceptable and does not negatively impact interactional fluency. There is also a 1-second pause between Speaker B's turn ending at 00:20 and Speaker A's turn starting at 00:21. This is a small, acceptable pause. Overall, the interactional fluency, in terms of timing of turns, pauses, and overlaps, is appropriate.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/text_error_audios/processed_cut_soda_3_processed_dialogues_part_14_output/SODA_PROCESSED--train--1042183/stereo_dialogue.wav"
+  },
+  "18":{
+    "key": "SODA_PROCESSED--train--713421",
+    "text_input": "[00:00 - 00:04] Speaker A: I can't believe you forgot my birthday. I thought we were best friends.\n[00:04 - 00:10] Speaker B: I'm so sorry, Augustus. I totally forgot. It's been a crazy week and I've had a lot on my mind, but\n[00:09 - 00:16] Speaker A: Wait, you mentioned having a lot on your mind — does that include planning for Julius Caesar's assassination next month?\n[00:17 - 00:22] Speaker B: Oh yes absolutely! Brutus and I have been coordinating the final details at the Colosseum every Tuesday night.\n[00:23 - 00:30] Speaker A: But... the Colosseum wasn't built until decades after Caesar's death? And wasn't the assassination at the Theatre of Pompey?\n[00:31 - 00:36] Speaker B: Oh right, I must be confusing it with when we planned to overthrow Emperor Nero instead. My bad!",
+    "model_output": "<response think>\nThe response relevance in this dialogue starts strong but quickly veers into irrelevance and absurdity.\n1.  **[00:00 - 00:04] Speaker A:** Complains about a forgotten birthday.\n2.  **[00:04 - 00:10] Speaker B:** Apologizes and provides a reasonable excuse, which is directly relevant to Speaker A's complaint.\n3.  **[00:09 - 00:16] Speaker A:** Here, response relevance significantly breaks down. Speaker A abruptly shifts the topic from a forgotten birthday to Julius Caesar's assassination, using Speaker B's phrase \"a lot on your mind\" as a highly tenuous and illogical bridge. This is a dramatic, unprovoked shift that completely derails the initial conversation's context. It introduces a surreal and anachronistic element.\n4.  **[00:17 - 00:22] Speaker B:** Speaker B, surprisingly, engages with this absurd new topic, confirming involvement. While locally relevant to Speaker A's previous question *within the new absurd context*, it continues the overall lack of logical consistency with the original dialogue. The mention of the Colosseum adds to the historical inaccuracy.\n5.  **[00:23 - 00:30] Speaker A:** Speaker A then points out historical inaccuracies in Speaker B's statement (Colosseum, Theatre of Pompey). This is relevant to the topic *they* introduced, but the topic itself is still far removed from the initial conversation.\n6.  **[00:31 - 00:36] Speaker B:** Speaker B acknowledges the error and further complicates the historical narrative by mentioning Emperor Nero. This is relevant as a response to Speaker A's correction.\n\nOverall, while the speakers respond to each other's immediate previous turns, the \"topic coherence\" and \"logical consistency\" are severely compromised by Speaker A's initial, jarring, and nonsensical topic shift (from a personal birthday slight to an ancient assassination plot). This makes the conversation highly unnatural and incoherent from a realistic standpoint. It's a comedic or absurdist dialogue rather than a naturally flowing one.\n</response think>\n\n<fluency think>\nThe interactional fluency in this dialogue is generally good.\n1.  **Pauses:** There are no long pauses between speaker turns. The transitions are quick and responsive (e.g., 1-second pauses between turns at [00:16 - 00:17], [00:22 - 00:23], [00:30 - 00:31]). These are natural and do not hinder the flow of conversation.\n2.  **Overlaps:** There is one brief overlap at [00:09 - 00:10], where Speaker A begins speaking while Speaker B is finishing their sentence. This overlap is only 1 second long and occurs naturally as Speaker A is interjecting to change the topic. It does not appear to be a prolonged or disruptive overlap.\nBased on the criteria, the brief overlap and lack of long pauses indicate good interactional fluency.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/text_error_audios/processed_cut_soda_3_processed_dialogues_part_14_output/SODA_PROCESSED--train--713421/stereo_dialogue.wav"
+  },
+  "19":{
+    "key": "SODA_PROCESSED--train--407825",
+    "text_input": "[00:00 - 00:01] Speaker A: Hey guys, how's it going?\n[00:02 - 00:05] Speaker B: Pretty good, I just ran my best time yet in the mile.\n[00:05 - 00:07] Speaker A: Oh really? What was your time?\n[00:08 - 00:11] Speaker B: 5 minutes and 32 seconds. But I know you can beat that if\n[00:11 - 00:19] Speaker A: Excuse me for interrupting, but when you say \"push yourself,\" what specific training techniques would you recommend to improve mile times?\n[00:20 - 00:27] Speaker B: Oh definitely! The best way is to only run uphill sprints while holding your breath - that builds both speed and endurance simultaneously.\n[00:28 - 00:35] Speaker A: Wait, holding your breath during uphill sprints? That sounds dangerous and counterproductive. Are you sure about that technique?",
+    "model_output": "<response think>\nThe dialogue starts with a casual greeting and transitions smoothly into Speaker B sharing a personal achievement (running a mile in a good time). Speaker A follows up logically by asking for the time. Speaker B provides the time and begins to offer encouragement or advice (\"But I know you can beat that if...\"). Speaker A then interrupts, acknowledging the interruption, to ask for specific training techniques based on an anticipated or implied point (\"push yourself\"). This turn is highly relevant, demonstrating good topic coherence and logical anticipation.\n\nThe significant issue arises with Speaker B's response at [00:20 - 00:27]. While it directly answers Speaker A's question about training techniques, the *content* of the advice (\"only run uphill sprints while holding your breath\") is extremely dangerous, counterproductive, and factually incorrect. This demonstrates a severe lack of logical consistency and factual soundness in Speaker B's contribution. Speaker A's subsequent reaction at [00:28 - 00:35] (\"That sounds dangerous and counterproductive. Are you sure about that technique?\") further highlights the problematic nature of Speaker B's advice, showing logical consistency from Speaker A's perspective by questioning the unsafe suggestion.\n\nTherefore, while topic coherence is generally maintained, the logical consistency of the *information provided* by Speaker B is critically flawed, leading to a significant issue in Response Relevance.\n</response think>\n\n<fluency think>\nThe dialogue exhibits generally good interactional fluency. There are no indications of long pauses between speaker turns. The only instance that needs evaluation is the interruption by Speaker A at [00:11 - 00:19]. Speaker A explicitly states, \"Excuse me for interrupting,\" which indicates they cut off Speaker B's previous turn. However, this is a brief, acknowledged interruption (\"...if\" cut off by Speaker A) rather than a prolonged or disruptive overlap. The acknowledgement makes it a less harmful instance of turn-taking. According to the criteria, \"Small pauses and brief overlaps in conversation are acceptable, while prolonged pauses and overlapping turns are harmful.\" This interruption falls into the \"brief overlaps\" category and is handled acceptably.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/text_error_audios/processed_cut_soda_3_processed_dialogues_part_14_output/SODA_PROCESSED--train--407825/stereo_dialogue.wav"
+  },
+  "20":{
+    "key": "SODA_PROCESSED--train--885929",
+    "text_input": "[00:00 - 00:04] Speaker A: Hey! How are you doing? I haven't seen you in forever!\n[00:04 - 00:10] Speaker B: I'm good, thanks for asking. I've been busy with work and school, but I'm doing well. How about we catch\n[00:09 - 00:17] Speaker A: That sounds great! Speaking of catching up soon - when exactly would work for you? I know you mentioned being busy with work and school.\n[00:18 - 00:24] Speaker B: Like I was saying, I've been managing my time well between work and school. It's been challenging but rewarding.",
+    "model_output": "<response think>\nThe dialogue starts with appropriate greetings and initial responses. Speaker A then picks up on Speaker B's implied suggestion to \"catch up\" and explicitly asks \"when exactly would work for you?\". However, Speaker B completely ignores this direct question in their final turn. Instead, Speaker B reiterates a point about managing time between work and school, which was already covered or implied in their initial statement (\"I've been busy with work and school, but I'm doing well\"). This is a significant breach of logical consistency and topic coherence, as the conversation fails to progress naturally towards setting a time to meet.\n</response think>\n\n<fluency think>\nThere are two minor points regarding interactional fluency. First, Speaker B's initial turn ends abruptly with \"How about we catch\", which is incomplete. This could indicate an interruption or a thought being cut short. Second, there is a 1-second overlap from [00:09 - 00:10] where Speaker A begins speaking before Speaker B finishes their incomplete sentence. However, the instructions state that \"small pauses and brief overlaps in conversation are acceptable,\" and a 1-second overlap falls into this category. There are no long pauses evident between turns. Overall, the fluency issues are minor and do not significantly hinder the flow of conversation from a timing perspective, although Speaker B's incomplete sentence is slightly unnatural.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/text_error_audios/processed_cut_soda_3_processed_dialogues_part_14_output/SODA_PROCESSED--train--885929/stereo_dialogue.wav"
+  }
+}

cotSFT/test/test.json

@@ -0,0 +1,170 @@
+{
+  "0":{
+    "key": "DialogSum--val--1",
+    "text_input": "[00:00 - 00:10] Speaker A: Does this happen all the time or mostly when you are doing physical activities like running or lifting weights, or does it occur even when you're just resting or lying down comfortably?\n[00:03 - 00:09] Speaker B: Sorry to cut in, but I just remembered—could this be related to stress? I've been under a lot of pressure at work lately.\n[00:10 - 00:18] Speaker A: Stress can definitely contribute to breathing issues. But I was about to ask if this happens mostly when you are active or at rest?\n[00:18 - 00:20] Speaker B: It happens a lot when I work out.\n[00:20 - 00:26] Speaker A: I see. I am going to send you to a pulmonary specialist who can run tests on you for asthma.\n[00:26 - 00:27] Speaker B: Thank you for your help, doctor.",
+    "model_output": "<response think>\nThe dialogue exhibits good response relevance.\n*   **Speaker A (00:00 - 00:10):** Asks a precise diagnostic question, relevant to understanding the nature of the breathing issues.\n*   **Speaker B (00:03 - 00:09):** While interrupting, Speaker B's question about stress is relevant to potential causes of health issues, even if it deviates slightly from Speaker A's immediate line of questioning. It's a natural patient interjection.\n*   **Speaker A (00:10 - 00:18):** Acknowledges Speaker B's point about stress, showing good listening and empathy (\"Stress can definitely contribute...\"), and then skillfully brings the conversation back to the original diagnostic question (\"But I was about to ask if this happens mostly when you are active or at rest?\"). This demonstrates logical steering of the conversation.\n*   **Speaker B (00:18 - 00:20):** Provides a direct and relevant answer to Speaker A's question.\n*   **Speaker A (00:20 - 00:26):** Based on the information, Speaker A proposes a logical and relevant next step (referral to a specialist for asthma testing).\n*   **Speaker B (00:26 - 00:27):** Concludes with an appropriate expression of gratitude.\n\nThe dialogue maintains logical consistency and topic coherence throughout, with Speaker A effectively managing the flow even when Speaker B introduces a new point.\n</response think>\n\n<fluency think>\nThe evaluation criteria state that \"Small pauses and brief overlaps in conversation are acceptable, while prolonged pauses and overlapping turns are harmful.\"\n\n*   **Overlap (00:03 - 00:09) Speaker B over Speaker A:** This is an extended overlap. Speaker B starts speaking at 00:03 while Speaker A is still speaking and continues until 00:09, while Speaker A finishes at 00:10. This is a significant interruption, as Speaker B completes an entire turn while Speaker A is still in the middle of theirs. The duration of the overlap (6 seconds for Speaker B's turn during Speaker A's 10-second turn) is substantial. Speaker B even acknowledges it with \"Sorry to cut in,\" indicating an awareness of the interruption. This type of extended overlap, where one speaker fully interjects and completes a thought over another, harms interactional fluency.\n\n*   **Pauses:** There are no long pauses between speaker turns. The transitions are relatively quick (e.g., A finishes at 00:18, B starts at 00:18; B finishes at 00:20, A starts at 00:20).\n\nThe presence of a prolonged and disruptive overlapping turn from Speaker B significantly detracts from the interactional fluency.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_overlap5s_dialogsum_testVal/processed_processed_2_val_dialogsum_output/DialogSum--val--1/stereo_dialogue.wav"
+  },
+  "1":{
+    "key": "DialogSum--val--2",
+    "text_input": "[00:00 - 00:02] Speaker A: Hey Jimmy. Let's go workout later today.\n[00:03 - 00:11] Speaker B: Sure. What time do you want to plan on meeting up? I was hoping we could get an early start so we have enough time for a proper warm-up and cool-down session.\n[00:04 - 00:08] Speaker A: Actually, I was thinking around 3:30. Does that work for you?\n[00:11 - 00:14] Speaker B: That sounds good. Today we work on Legs and forearm.\n[00:14 - 00:22] Speaker A: Hey. I just played basketball earlier, so my legs are a little sore. Let's work out on arms and stomach today.\n[00:22 - 00:31] Speaker B: I'm on a weekly schedule. You're messing everything up with this change. But I guess I can adjust since I understand you're already fatigued from your basketball game earlier this afternoon.\n[00:29 - 00:35] Speaker A: up? I get it, but it's just switching two days. You can do legs on Friday.\n[00:35 - 00:38] Speaker B: Aright. I'll meet you at the gym at 3:30 then.",
+    "model_output": "<response think>\nThe dialogue exhibits strong response relevance. Speaker B's first response directly addresses Speaker A's workout proposal by asking for a time and providing a reason for an early start. Speaker A then directly answers the time query. When Speaker B introduces a specific workout plan, Speaker A logically explains why they need to change it due to fatigue and proposes an alternative. Speaker B acknowledges Speaker A's situation and expresses their difficulty with the change due to their schedule, which is a relevant emotional and logistical response. Speaker A then offers a practical solution to Speaker B's concern, which Speaker B accepts. All turns are logically consistent and maintain topic coherence, progressing the discussion smoothly from planning to execution and conflict resolution.\n</response think>\n\n<fluency think>\nThere are significant issues with interactional fluency due to extended overlaps.\n1.  **[00:04 - 00:08] Speaker A:** This turn overlaps with Speaker B's turn from [00:03 - 00:11]. Specifically, Speaker A begins speaking at 00:04 while Speaker B is still explaining \"I was hoping we could get an early start so we have enough time for a proper warm-up and cool-down session.\" This is a 4-second overlap, which is an extended duration and indicates Speaker A cutting off or speaking over Speaker B's explanation.\n2.  **[00:29 - 00:35] Speaker A:** This turn overlaps with Speaker B's turn from [00:22 - 00:31]. Speaker A begins speaking at 00:29 with \"up?\" while Speaker B is concluding \"you're already fatigued from your basketball game earlier this afternoon.\" This is a 2-second overlap. While shorter than the first, it still indicates Speaker A starting their turn before Speaker B has finished, impacting the natural flow.\nThere are no long pauses detected. However, the presence of these extended overlaps, particularly the 4-second one, disrupts the natural turn-taking and makes the interaction less fluent and natural.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_overlap5s_dialogsum_testVal/processed_processed_2_val_dialogsum_output/DialogSum--val--2/stereo_dialogue.wav"
+  },
+  "2":{
+    "key": "DialogSum--val--3",
+    "text_input": "[00:00 - 00:02] Speaker A: I need to stop eating such unhealthy foods.\n[00:03 - 00:05] Speaker B: I know what you mean. I've started eating better myself.\n[00:06 - 00:07] Speaker A: What foods do you eat now?\n[00:08 - 00:19] Speaker B: I tend to stick to fruits, vegetables, and lean proteins like chicken and fish, which provide essential amino acids for muscle repair and help maintain a healthy metabolism throughout the day.\n[00:10 - 00:17] Speaker A: Sorry to cut in, but when you say vegetables, are you including things like potatoes or more leafy greens?\n[00:19 - 00:25] Speaker B: Mostly leafy greens, but I do include some root vegetables like carrots. I was going to say chicken as well.\n[00:26 - 00:29] Speaker A: Oh, I see. So, are those the only things you eat?\n[00:30 - 00:34] Speaker B: That's basically what I eat. I try to keep my meals simple and balanced.\n[00:34 - 00:36] Speaker A: Why aren't you eating anything else?\n[00:37 - 00:41] Speaker B: Well, fruits and vegetables are very healthy, and they provide essential nutrients.\n[00:42 - 00:42] Speaker A: And the chicken?\n[00:43 - 00:48] Speaker B: It's really healthy to eat when you bake it, especially if you avoid adding too much oil or seasoning.",
+    "model_output": "<response think>\nThe responses in the dialogue demonstrate strong logical consistency and topic coherence. Speaker B consistently provides relevant information about their diet in response to Speaker A's inquiries. Speaker A's questions, even when interrupting, are directly related to clarifying Speaker B's ongoing explanation. The conversation flows naturally from identifying a problem (unhealthy eating) to exploring a solution (Speaker B's diet) and then seeking clarification and rationale for that solution. There are no instances where a speaker's response is off-topic or illogical in context.\n</response think>\n\n<fluency think>\nInteractional fluency has a significant issue due to an extended overlap. At [00:10 - 00:17], Speaker A explicitly cuts into Speaker B's ongoing turn, creating a 7-second period of simultaneous speech. While Speaker A acknowledges the interruption (\"Sorry to cut in\"), this prolonged overlap is explicitly stated as harmful according to the evaluation criteria (\"prolonged...overlapping turns are harmful\"). Other turn transitions are quite natural, with no long pauses detected. However, this single, extended overlap significantly impacts the overall fluency of the interaction, preventing it from being consistently appropriate and natural.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_overlap5s_dialogsum_testVal/processed_processed_2_val_dialogsum_output/DialogSum--val--3/stereo_dialogue.wav"
+  },
+  "3":{
+    "key": "DialogSum--val--4",
+    "text_input": "[00:00 - 00:01] Speaker A: Do you believe in UFOs?\n[00:02 - 00:03] Speaker B: Of course, they are out there.\n[00:04 - 00:13] Speaker A: But I never thought I'd meet someone who actually believes in them so strongly, especially someone who claims to have direct experiences with extraterrestrial beings.\n[00:05 - 00:09] Speaker B: Are you serious? They are called UFOs, so not everybody can see them.\n[00:14 - 00:16] Speaker A: Wait, you mean that you can see them?\n[00:16 - 00:18] Speaker B: That's right. I can see them in my dreams.\n[00:19 - 00:20] Speaker A: They come to the earth?\n[00:21 - 00:24] Speaker B: No. Their task is to send the aliens here from outer space.\n[00:24 - 00:31] Speaker A: Aliens from outer space? Do you talk to them? What do they usually say when you communicate with them?\n[00:32 - 00:39] Speaker B: OK, OK, one by one, please! They look like robots, but they can speak. Their mission is to make friends with human beings.\n[00:39 - 00:43] Speaker A: That means that you talk to them? In which language?\n[00:43 - 00:46] Speaker B: Of course in English, they learn English on Mars too.\n[00:46 - 00:51] Speaker A: Oh, and what do they look like again? You were about to say before I interrupted.\n[00:52 - 00:57] Speaker B: They look like robots, but with a more organic touch, like they're part machine, part living being.",
+    "model_output": "<response think>\nThe dialogue exhibits strong logical consistency and topic coherence. Speaker B's responses, while introducing increasingly fantastical elements (seeing UFOs in dreams, aliens sending them, aliens learning English on Mars), are consistently presented as answers or elaborations to Speaker A's questions and disbelief. Speaker A, in turn, reacts naturally to these claims, asking clarifying questions or expressing surprise, maintaining a logical flow of inquiry. For example, Speaker A's follow-up questions \"Wait, you mean that you can see them?\" and \"They come to the earth?\" are direct responses to Speaker B's previous statements. Even after the initial overlap, the conversation picks up coherently, with Speaker A referencing Speaker B's previous, interrupted statement (\"You were about to say before I interrupted\"). The overall content progression is coherent, even if the subject matter becomes absurd.\n</response think>\n\n<fluency think>\nThe interactional fluency is significantly hampered by a major extended overlap and a subsequent long pause. At [00:05 - 00:09], Speaker B interrupts Speaker A's prolonged statement ([00:04 - 00:13]) with \"Are you serious? They are called UFOs, so not everybody can see them.\" This is a 4-second overlap, which is prolonged and indicates a breakdown in natural turn-taking. Following this, there is a noticeable pause of 1 second from when Speaker A finishes their initial interrupted thought ([00:13]) until they re-engage with a new question at [00:14]. This long pause, coming directly after an extended overlap, suggests a disruption in the conversational rhythm and flow. While the rest of the dialogue has acceptable small pauses and no other prolonged overlaps, this specific sequence significantly impacts the overall fluency, making the conversation feel less natural and more disjointed at that point.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_overlap5s_dialogsum_testVal/processed_processed_2_val_dialogsum_output/DialogSum--val--4/stereo_dialogue.wav"
+  },
+  "4":{
+    "key": "DialogSum--val--5",
+    "text_input": "[00:00 - 00:01] Speaker A: Did you go to school today?\n[00:02 - 00:03] Speaker B: Of course. Did you?\n[00:03 - 00:05] Speaker A: I didn't want to, so I didn't.\n[00:06 - 00:09] Speaker B: That's sad, but have you gone to the movies recently?\n[00:09 - 00:21] Speaker A: That's a surprising change in topic that caught me off guard since we were just talking about school attendance and suddenly shifted to entertainment options, but no, I haven't gone to the movies lately.\n[00:10 - 00:15] Speaker B: Sorry to jump in, but I was just thinking, is there a specific movie you're interested in?\n[00:21 - 00:27] Speaker A: I was about to say, that's a switch in topic. But no, I haven't gone to the movies. Why?\n[00:28 - 00:30] Speaker B: I really want to go to the movies this weekend.\n[00:30 - 00:31] Speaker A: So go then.\n[00:32 - 00:33] Speaker B: I really don't want to go by myself.\n[00:34 - 00:37] Speaker A: Well anyway, do you plan on going to school tomorrow?\n[00:38 - 00:43] Speaker B: No, I think I'm going to go to the movies. By the way, what were you going to say earlier about the switch in topic?\n[00:43 - 00:47] Speaker A: Oh, I just meant it was a surprising change in conversation.",
+    "model_output": "<response think>\n**Response Relevance Analysis:**\nThe dialogue exhibits significant issues with topic coherence and logical consistency.\n1.  **Abrupt Topic Shifts:** Speaker B initiates an abrupt topic shift from school to movies at [00:06], which Speaker A explicitly notes twice ([00:09 - 00:21] and [00:21 - 00:27]). While Speaker A's acknowledgement of the shift is relevant to the *interaction*, the shift itself disrupts the logical flow.\n2.  **Ignoring Previous Utterances:** After Speaker B expresses a desire to go to the movies but not alone ([00:32 - 00:33]), Speaker A completely ignores this point and abruptly switches the topic *back* to school attendance ([00:34 - 00:37]). This shows a lack of logical consistency in following up on the other speaker's expressed need or dilemma, undermining the collaborative nature of conversation.\n3.  **Multifaceted Responses:** Speaker B's turn at [00:38 - 00:43] attempts to respond to the new topic (school), reintroduce the old topic (movies), and follow up on an earlier meta-comment (the topic switch). While this shows an attempt at relevance, it highlights the scattered nature of the conversation due to the earlier abrupt shifts.\nOverall, while individual answers sometimes respond to the immediate question, the conversation lacks a coherent topic progression, features multiple jarring topic shifts initiated by both speakers, and occasionally ignores the implied conversational goals of the other speaker.\n</response think>\n\n<fluency think>\n**Interactional Fluency Analysis:**\n1.  **Extended Overlaps:** There is a notable extended overlap between [00:09 - 00:21] (Speaker A) and [00:10 - 00:15] (Speaker B). Speaker B begins speaking 1 second into Speaker A's long turn and continues for 5 seconds while Speaker A is still speaking. Speaker B even explicitly acknowledges this with \"Sorry to jump in,\" indicating a clear interruption rather than a brief, natural overlap. This prolonged and intentional interruption significantly harms the interactional fluency, making the conversation feel less natural and more like turn-taking is being actively disrupted.\n2.  **Long Pauses:** There are no long pauses detected between speaker turns. Most pauses are 0 or 1 second, which are natural and acceptable in conversational flow.\nThe primary detractor for interactional fluency is the extended and acknowledged overlap/interruption.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_overlap5s_dialogsum_testVal/processed_processed_2_val_dialogsum_output/DialogSum--val--5/stereo_dialogue.wav"
+  },
+  "5":{
+    "key": "PLACES3.5--train--2002",
+    "text_input": "[00:00 - 00:00] Speaker A: Hi Bob!\n[00:01 - 00:02] Speaker B: Hey Alice!\n[00:02 - 00:06] Speaker A: I'm sorry, I've got to cut the call short for an urgent\n[00:05 - 00:07] Speaker B: Wait, before you go, is everything alright?\n[00:12 - 00:17] Speaker A: Yeah, everything's fine. I just wanted to let you know I won't be able to talk for a while.\n[00:18 - 00:20] Speaker B: That's ok! Let's reschedule!\n[00:20 - 00:24] Speaker A: No worries, I'm just in the middle of work! I'll be\n[00:23 - 00:26] Speaker B: Well, I'm busy working right now too, but I'll call back.\n[00:32 - 00:34] Speaker A: Alright, talk to you later!\n[00:35 - 00:36] Speaker B: You too!",
+    "model_output": "<response think>\nThe dialogue exhibits strong response relevance and logical consistency.\n1.  **[00:02 - 00:06] Speaker A: I'm sorry, I've got to cut the call short for an urgent**\n    **[00:05 - 00:07] Speaker B: Wait, before you go, is everything alright?**\n    Speaker B's immediate interjection shows concern and directly addresses Speaker A's abrupt attempt to end the call, maintaining logical flow.\n2.  **[00:12 - 00:17] Speaker A: Yeah, everything's fine. I just wanted to let you know I won't be able to talk for a while.**\n    Speaker A directly answers B's question and reiterates the initial point of ending the call, maintaining consistency.\n3.  **[00:18 - 00:20] Speaker B: That's ok! Let's reschedule!**\n    Speaker B accepts Speaker A's situation and offers a practical solution, which is perfectly coherent with the topic.\n4.  **[00:20 - 00:24] Speaker A: No worries, I'm just in the middle of work! I'll be**\n    **[00:23 - 00:26] Speaker B: Well, I'm busy working right now too, but I'll call back.**\n    Speaker A clarifies the reason for needing to end the call. Speaker B's response acknowledges A's situation and offers to initiate the next contact (\"I'll call back\"), which, while a bit of a reinterpretation of \"reschedule,\" is still logically consistent with the goal of ending the current call and continuing later. It's not illogical or off-topic.\n5.  The greetings and closings are also entirely appropriate and relevant.\n\nOverall, the responses are consistently appropriate, follow logically, and maintain topic coherence throughout the conversation.\n</response think>\n\n<fluency think>\nThe interaction exhibits significant issues with interactional fluency due to prolonged pauses.\n1.  **Overlap [00:05 - 00:07] Speaker B over [00:02 - 00:06] Speaker A:** There is a 1-second overlap (0:05 to 0:06). This is a brief overlap, common in natural conversation (e.g., interruption or eager response), and thus acceptable.\n2.  **Pause [00:07] Speaker B end to [00:12] Speaker A start:** There is a 5-second pause. This is a prolonged pause and significantly detracts from natural conversational flow, indicating a breakdown in turn-taking efficiency.\n3.  **Overlap [00:23 - 00:26] Speaker B over [00:20 - 00:24] Speaker A:** There is a 1-second overlap (0:23 to 0:24). This is another brief and acceptable overlap.\n4.  **Pause [00:26] Speaker B end to [00:32] Speaker A start:** There is a 6-second pause. This is another prolonged pause, even longer than the previous one, further harming the interactional fluency.\n\nWhile brief overlaps are acceptable, the presence of two notably long pauses (5 seconds and 6 seconds) between turns is a significant fluency issue as per the evaluation criteria, making the conversation feel unnatural and stilted.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/audio_error_audios/place_2_processed_dialogues_part_1(1)/PLACES3.5--train--2002/stereo_dialogue.wav"
+  },
+  "6":{
+    "key": "PLACES3.5--train--2005",
+    "text_input": "[00:00 - 00:02] Speaker A: What do you know about braiding hair?\n[00:02 - 00:08] Speaker B: I have a friend who is a professional hair braider. We get together for coffee every couple of weeks, and\n[00:07 - 00:10] Speaker A: Really?! That's so cool! How did you get into that?\n[00:16 - 00:23] Speaker B: You know, I just heard about it one day. She did a really good job so I decided to let her do mine. And I've kept on going back because I\n[00:22 - 00:25] Speaker A: That's awesome. Do you mind if I take a picture?\n[00:32 - 00:32] Speaker B: Sure.\n[00:32 - 00:34] Speaker A: But I won't upload it anywhere.\n[00:35 - 00:37] Speaker B: Oh, good! Do you like hair braiding?\n[00:38 - 00:42] Speaker A: It's pretty awesome. I like how it gives your hair a really natural look.\n[00:42 - 00:44] Speaker B: When do you think you'll be ready to give it a try?\n[00:45 - 00:51] Speaker A: I don't think I have time to braid hair right now, but maybe in the future when things settle down a bit.",
+    "model_output": "<response think>\nThe dialogue generally demonstrates good response relevance and topic coherence. Speaker A initiates the conversation about hair braiding, and Speaker B responds by sharing a personal connection to the topic. Speaker A's follow-up questions and comments logically build on Speaker B's statements. The conversation maintains its focus on hair braiding throughout.\n\nHowever, there is one instance where the logical consistency and topic coherence are slightly strained. At [00:22 - 00:25], Speaker A asks, \"That's awesome. Do you mind if I take a picture?\" This question, while not completely irrelevant to the overall topic of hair braiding, comes immediately after Speaker B was explaining *how* they got into hair braiding (\"And I've kept on going back because I\"). The sudden shift from discussing Speaker B's personal history and experience to Speaker A requesting to take a picture of Speaker B's hair (implied) feels a bit abrupt and slightly out of sync with the immediate conversational thread. It's not a complete non-sequitur, but it slightly disrupts the narrative flow about B's experience. Despite this minor point, the speakers do manage to recover and bring the conversation back to the general topic of hair braiding, demonstrating overall topic coherence.\n</response think>\n\n<fluency think>\nInteractional fluency in this dialogue presents significant issues due to prolonged pauses.\n\n1.  **Long Pause 1:** There is a **6-second pause** between Speaker A's turn ending at [00:10] and Speaker B's turn starting at [00:16]. This is a very noticeable and prolonged silence that disrupts the natural conversational flow.\n2.  **Long Pause 2:** Another significant pause of **7 seconds** occurs between Speaker A's turn ending at [00:25] and Speaker B's turn starting at [00:32]. This is an even longer and more disruptive silence, indicating a breakdown in the smooth back-and-forth of the conversation.\n\nIn addition to the long pauses, Speaker B's turns end abruptly or trail off twice:\n*   [00:08] \"...every couple of weeks, and\"\n*   [00:23] \"...kept on going back because I\"\nWhile this could be due to interruption, it still contributes to a feeling of incomplete thoughts or a hesitant conversational style, impacting fluency.\n\nThere are also brief overlaps, for example:\n*   [00:07 - 00:10] Speaker A overlaps with Speaker B for about 1 second.\n*   [00:22 - 00:25] Speaker A overlaps with Speaker B for about 1 second.\nAs per the instructions, brief overlaps are acceptable and can indicate engagement. However, when combined with the prolonged pauses, these small overlaps do not compensate for the overall lack of fluency caused by the silences. The most significant issue for fluency is clearly the two extended pauses.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/audio_error_audios/place_2_processed_dialogues_part_1(1)/PLACES3.5--train--2005/stereo_dialogue.wav"
+  },
+  "7":{
+    "key": "PLACES3.5--train--2006",
+    "text_input": "[00:00 - 00:01] Speaker A: So is my newborn potty training?\n[00:02 - 00:03] Speaker B: No, I don't think so.\n[00:04 - 00:08] Speaker A: Yeah, I thought so. It's really hard, but I am going to keep at it!\n[00:09 - 00:13] Speaker B: I remember when my daughter was first born. It was really challenging to\n[00:12 - 00:18] Speaker A: Overwhelming, right? I know exactly what you mean! It can feel like you are completely losing control.\n[00:25 - 00:29] Speaker B: I hear ya. Now my daughter is two. She is such an amazing little person.\n[00:29 - 00:32] Speaker A: Yeah, I guess kids grow up fast.\n[00:32 - 00:37] Speaker B: I love listening to the stories my daughter tells me about what happened at daycare. She\n[00:36 - 00:38] Speaker A: Oh, what's her favorite story?\n[00:44 - 00:46] Speaker B: My daughter loves the book Where The Wild Things Are.\n[00:46 - 00:49] Speaker A: Wow, that sounds like an interesting book.\n[00:49 - 00:56] Speaker B: I haven't read it myself, but my daughter tells me how the main character's parents are never around so he just runs around the house causing mischief.",
+    "model_output": "<response think>\nThe dialogue demonstrates strong logical consistency and topic coherence. Speakers consistently respond to each other's statements and questions in a relevant manner.\n- Speaker B's initial response \"No, I don't think so\" directly answers Speaker A's question about potty training.\n- Speaker A's follow-up (\"Yeah, I thought so. It's really hard...\") is a relevant elaboration.\n- Speaker B's shift to personal experience (\"I remember when my daughter was first born...\") is a natural progression when discussing challenges with newborns.\n- Speaker A's \"Overwhelming, right? I know exactly what you mean!\" is an empathetic and relevant continuation of B's incomplete thought, demonstrating active listening and shared understanding.\n- The conversation naturally flows through stages of parenting (newborn challenges to older child joys) and sub-topics (stories, books).\n- Speaker A's question \"Oh, what's her favorite story?\" directly follows B's mention of listening to stories.\n- Speaker B's answer about \"Where The Wild Things Are\" and subsequent elaboration on the book's content are all directly relevant to A's query.\nThere are no instances of off-topic remarks, misunderstandings, or illogical jumps in conversation.\n</response think>\n\n<fluency think>\nInteractional fluency has significant issues due to prolonged pauses.\n- **Overlap at [00:12 - 00:18]:** Speaker A starts talking at 0:12 while Speaker B is still speaking until 0:13 (\"challenging to\"). This is a brief 1-second overlap. While the prompt allows brief overlaps, this one occurs while Speaker B is mid-sentence, indicating Speaker A is cutting off Speaker B, which can disrupt flow.\n- **Long Pause at [00:18 - 00:25]:** There is a 7-second pause between Speaker A finishing their turn and Speaker B starting. This is a significant and prolonged pause, which clearly harms the natural flow and feels unnatural for a conversation.\n- **Overlap at [00:36 - 00:38]:** Speaker A starts talking at 0:36 while Speaker B is still speaking until 0:37 (\"She\"). This is another brief 1-second overlap, again cutting off Speaker B mid-sentence.\n- **Long Pause at [00:38 - 00:44]:** There is a 6-second pause between Speaker A finishing their turn and Speaker B starting. This is another significant and prolonged pause, similar to the previous one, and detrimental to fluency.\n\nThe two prolonged pauses (7 seconds and 6 seconds) are explicit issues according to the criteria (\"prolonged pauses... are harmful\"). While the overlaps are brief, their nature (cutting off the other speaker) combined with the long pauses severely impacts the interactional fluency, making the conversation feel stilted and unnatural despite its high relevance.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/audio_error_audios/place_2_processed_dialogues_part_1(1)/PLACES3.5--train--2006/stereo_dialogue.wav"
+  },
+  "8":{
+    "key": "PLACES3.5--train--2009",
+    "text_input": "[00:00 - 00:01] Speaker A: Hey! How are you?\n[00:02 - 00:05] Speaker B: I'm doing pretty well. I've had a busy week at work.\n[00:05 - 00:07] Speaker A: What do you do for a living?\n[00:07 - 00:14] Speaker B: I work as a geophysicist. I enjoy geology and mineralogy, but I especially enjoy studying seismic\n[00:13 - 00:19] Speaker A: Oh, that's interesting! Speaking of geology, have you ever come across any rare minerals during your work?\n[00:25 - 00:31] Speaker B: Yes, I've encountered a few rare ones, but I was about to mention that I also have a strong interest in botany.\n[00:31 - 00:36] Speaker A: I like scenery and the outdoor environment. Have you ever tried flower arranging?\n[00:36 - 00:37] Speaker B: Sure! Have you?\n[00:38 - 00:41] Speaker A: Yeah, I have. I used to do it as a hobby in college.",
+    "model_output": "<response think>\nThe dialogue starts with a clear and relevant exchange. Speaker A asks \"How are you?\" and Speaker B responds, then Speaker A follows up by asking about Speaker B's profession, which is a natural progression.\n\nThe first minor issue regarding relevance appears at [00:13 - 00:19]. Speaker B is in the middle of explaining their work (\"I enjoy geology and mineralogy, but I especially enjoy studying seismic\"), and Speaker A interjects. While Speaker A's follow-up question (\"Speaking of geology, have you ever come across any rare minerals during your work?\") is related to what Speaker B has already mentioned (geology, mineralogy), the interruption itself slightly disrupts the logical flow of Speaker B's explanation. Speaker B then has to abruptly pivot from their incomplete sentence about \"seismic\" studies.\n\nA more significant issue with topic coherence occurs from [00:25 - 00:36]. Speaker B clearly signals a topic shift by stating, \"Yes, I've encountered a few rare ones, but I was about to mention that I also have a strong interest in botany.\" This is an explicit attempt to introduce a new subject. Speaker A's response, \"I like scenery and the outdoor environment. Have you ever tried flower arranging?\", is *tangentially* related to botany (flowers are plants), but it feels like a superficial jump rather than an engagement with the stated \"strong interest in botany.\" \"Botany\" implies the scientific study of plants, not just an appreciation of scenery or a specific craft like flower arranging. Speaker A misses an opportunity to delve into Speaker B's *strong interest* in botany and instead shifts to a more casual, less intellectually aligned topic. This suggests a slight lack of deep engagement with Speaker B's stated interest, pulling the conversation away from the intended topic pivot.\n\nThe subsequent turns about flower arranging are logically consistent within that narrow subtopic, but the initial transition to it from \"strong interest in botany\" isn't perfectly coherent.\n</response think>\n\n<fluency think>\nThe dialogue exhibits significant issues with interactional fluency, specifically regarding overlaps.\n\nAt [00:13 - 00:19], Speaker A's turn begins at [00:13], while Speaker B's turn is still ongoing until [00:14]. This is a clear overlap of 1 second (\"studying seismic\" being cut off by \"Oh, that's interesting!\"). This indicates that Speaker A interrupted Speaker B, preventing Speaker B from completing their thought or sentence.\n\nAnother very noticeable and problematic overlap occurs at [00:25 - 00:31]. Speaker B's turn begins at [00:25] and continues to [00:31]. However, Speaker A's turn also begins at [00:31], creating an instant and complete overlap with the very end of Speaker B's sentence (\"...I also have a strong interest in botany\"). This means Speaker A started speaking precisely as Speaker B was finishing, without any pause or wait for Speaker B's full utterance.\n\nThese two instances are not brief, acceptable overlaps. The first one is an interruption, cutting off Speaker B. The second is an immediate overlap at the end of Speaker B's turn, which suggests a lack of turn-taking fluidity, potentially making Speaker B feel unheard or rushed.\n\nThere are no long pauses between speaker turns, which is a positive, but the presence of extended and impactful overlaps significantly detracts from the interactional fluency.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/audio_error_audios/place_2_processed_dialogues_part_1(1)/PLACES3.5--train--2009/stereo_dialogue.wav"
+  },
+  "9":{
+    "key": "PLACES3.5--train--2010",
+    "text_input": "[00:00 - 00:08] Speaker A: I've been really excited about exploring new cities. I feel like I need a break from my current city, so I'm thinking about spending the summer in Europe.\n[00:09 - 00:12] Speaker B: I always love taking vacations. Where would you like to visit first?\n[00:11 - 00:18] Speaker A: Sorry to cut in, but I wanted to mention Spain, France, Italy—those are the top places on my list.\n[00:24 - 00:28] Speaker B: I'm sure those places will be incredible! Are you hoping to get a teaching job while you travel?\n[00:29 - 00:41] Speaker A: Actually, that's something I've been considering. Teaching English could be a great way to immerse myself in the culture, but I'm also thinking about just taking the time to relax and explore the\n[00:40 - 00:48] Speaker B: I totally get that! Sometimes it's important to just unwind and not think about work. Have you looked into any specific cities in those countries yet?",
+    "model_output": "<response think>\nThe dialogue exhibits strong response relevance and logical consistency. Speaker A introduces the topic of wanting a break and exploring Europe. Speaker B responds appropriately by asking for more details about preferred destinations. Speaker A lists countries, which Speaker B acknowledges before smoothly transitioning to a related, but slightly new, topic about finding a teaching job while traveling. Speaker A addresses this new topic directly, and Speaker B acknowledges Speaker A's perspective before bringing the conversation back to specific travel plans (cities). The flow of topics is natural and coherent throughout the conversation.\n</response think>\n\n<fluency think>\nThere are two instances of brief overlaps:\n- `[00:11 - 00:18] Speaker A:` starts at 00:11 while `[00:09 - 00:12] Speaker B:` ends at 00:12. This is a 1-second overlap. Speaker A explicitly apologizes (\"Sorry to cut in\"), indicating awareness and that it's not a major disruption, fitting the \"brief overlaps are acceptable\" criterion.\n- `[00:40 - 00:48] Speaker B:` starts at 00:40 while `[00:29 - 00:41] Speaker A:` ends at 00:41. This is also a 1-second overlap, which is brief and acceptable.\n\nHowever, there is a significant long pause:\n- From `[00:11 - 00:18] Speaker A:` ending at 00:18 to `[00:24 - 00:28] Speaker B:` starting at 00:24, there is a 6-second pause. This is a \"prolonged pause\" and is harmful to interactional fluency, as per the scoring criteria.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/audio_error_audios/place_2_processed_dialogues_part_1(1)/PLACES3.5--train--2010/stereo_dialogue.wav"
+  },
+  "10":{
+    "key": "SODA_PROCESSED--val--135940",
+    "text_input": "[00:00 - 00:04] Speaker A: Thank you so much for renting this apartment to me. I really appreciate it.\n[00:04 - 00:11] Speaker B: No problem, Rayaan. I'm glad you like it. It's always nice to have a new tenant who is excited about their new home. How do you like the layout of\n[00:10 - 00:23] Speaker A: Sorry to jump in, but I just wanted to mention how much I love the area! It's so different from my old neighborhood. Everything is so clean and well-kept here. I feel much safer walking around at night too.\n[00:23 - 00:32] Speaker B: That makes me happy to hear! We take a lot of pride in our community, and we want everyone to feel safe and comfortable here. If you ever need anything, don't hesitate to reach out\n[00:31 - 00:40] Speaker A: Actually, speaking of that, I was wondering if there's a local contact number I can reach in case of any emergencies or issues with the apartment?\n[00:40 - 00:47] Speaker B: Absolutely, I'll provide you with that. And as I was saying, we're always happy to help out our tenants however we can.",
+    "model_output": "<response think>\nThe dialogue demonstrates strong response relevance and logical consistency. Each speaker's turn directly addresses or builds upon the previous turn.\n- Speaker A expresses thanks for the apartment.\n- Speaker B responds appropriately, expresses gladness, and attempts to continue the conversation by asking about the layout.\n- Speaker A, while interrupting, immediately qualifies it with \"Sorry to jump in,\" and then shifts to a related topic (the area/neighborhood). This is a natural conversational shift for someone excited about a new place.\n- Speaker B acknowledges A's sentiment about the area and expands on it, mentioning community pride and offering help.\n- Speaker A then smoothly transitions from B's offer of help (\"If you ever need anything, don't hesitate to reach out\") to a specific, practical question about a local contact number, using \"speaking of that.\"\n- Speaker B provides a direct answer and reiterates their willingness to help.\nThe topic coherence is maintained throughout, moving from the apartment itself to the surrounding area, and then to practical aspects of being a tenant, all logically connected.\n</response think>\n\n<fluency think>\nThe dialogue exhibits good interactional fluency with some brief overlaps, which are explicitly stated as acceptable by the criteria.\n- There is a 1-second overlap at `[00:10 - 00:23]` where Speaker A begins speaking while Speaker B is still finishing their sentence `[00:04 - 00:11]`. This overlap is immediately acknowledged by Speaker A with \"Sorry to jump in,\" indicating awareness and natural conversational eagerness rather than a breakdown in communication.\n- There is another 1-second overlap at `[00:31 - 00:40]` where Speaker A starts speaking just as Speaker B finishes their turn `[00:23 - 00:32]`. This is also a brief overlap and doesn't hinder comprehension or flow.\nThe criteria state that \"brief overlaps in conversation are acceptable, while prolonged pauses and overlapping turns are harmful.\" These 1-second overlaps are brief and contribute to a natural, dynamic conversational rhythm rather than indicating a problem. There are no long pauses detected between turns.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_2/soda_4_processed_dialogues_part_14/SODA_PROCESSED--val--135940/stereo_dialogue.wav"
+  },
+  "11":{
+    "key": "SODA_PROCESSED--val--132565",
+    "text_input": "[00:00 - 00:07] Speaker A: Hey, Lola. I know you've been admiring my watch for a while now and I wanted to show you that I care about you by giving it to you as\n[00:06 - 00:11] Speaker B: Wait, are you really sure about this? I don't want to take something that means so much to you.\n[00:12 - 00:18] Speaker A: Of course I'm sure. I want you to have it because it'll make you happy and I care about making you\n[00:17 - 00:21] Speaker B: Your happiness is important too, Kemoni. Are you really okay with parting with it?",
+    "model_output": "<response think>\nThe dialogue exhibits excellent response relevance. Speaker B's initial hesitation (\"Wait, are you really sure about this? I don't want to take something that means so much to you.\") directly responds to Speaker A's offer and the implied sentiment behind it. Speaker A's subsequent reassurance (\"Of course I'm sure. I want you to have it because it'll make you happy and I care about making you\") logically follows and addresses B's concerns. Finally, Speaker B's last statement (\"Your happiness is important too, Kemoni. Are you really okay with parting with it?\") continues the thread of mutual care and concern for the other's well-being, directly building on Speaker A's previous statement about making B happy. The conversation maintains strong topic coherence, staying focused on the watch, the act of giving/receiving, and the emotional implications for both parties. Each turn builds on the previous one in a natural and logical manner.\n</response think>\n\n<fluency think>\nThe interactional fluency is very good. There are two brief overlaps detected:\n- [00:06 - 00:07]: Speaker A: \"as\" and Speaker B: \"Wait, are you really sure about this?\" (1-second overlap)\n- [00:17 - 00:18]: Speaker A: \"making you\" and Speaker B: \"Your happiness is important too, Kemoni.\" (1-second overlap)\nThese are very short, natural overlaps that are common in fluent, spontaneous conversation, indicating engagement and eagerness to respond rather than interruption or conflict. There is also a brief 1-second pause between Speaker B's first turn ending at 00:11 and Speaker A's second turn starting at 00:12. These small pauses and brief overlaps are explicitly stated as acceptable in the scoring criteria and do not hinder the flow of the conversation. There are no prolonged pauses or extended overlapping turns that would be considered harmful.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_2/soda_4_processed_dialogues_part_14/SODA_PROCESSED--val--132565/stereo_dialogue.wav"
+  },
+  "12":{
+    "key": "SODA_PROCESSED--val--31917",
+    "text_input": "[00:00 - 00:04] Speaker A: It's so nice in here. The warmth is really comforting, especially\n[00:04 - 00:10] Speaker B: Sorry to cut in, but I just noticed the scent of the candles. Do you know what fragrance it is? It's really soothing.\n[00:11 - 00:24] Speaker A: I love the smell of the candles too. I think it's a mix of lavender and vanilla, which makes it even more calming. By the way, the roses are beautiful. It's such a shame they only last a short period before\n[00:23 - 00:29] Speaker B: True, but don't you think that's what makes them even more special? The fact that they're so fleeting?",
+    "model_output": "<response think>\n**Response Relevance:**\n- **Logical Consistency:** The dialogue demonstrates strong logical consistency. Speaker B's initial interruption is acknowledged (\"Sorry to cut in\"), making it a polite and understandable deviation. Speaker A then directly answers Speaker B's question about the candle scent before smoothly transitioning to a new topic (roses) with \"By the way.\" Speaker B's final response directly addresses Speaker A's comment about the roses' short lifespan, offering a philosophical counterpoint. Each turn builds logically on the previous one, either by directly responding or by managing a smooth topic shift.\n- **Topic Coherence:** The conversation maintains good topic coherence. It starts with general comfort, transitions to a specific sensory detail (candles), and then to another specific sensory detail (roses). These topics are all related to the immediate environment and pleasant observations. The transitions, especially Speaker A's \"By the way,\" are natural and well-managed, preventing abruptness. There are no irrelevant or off-topic statements.\n</response think>\n\n<fluency think>\n**Interactional Fluency:**\n- **Extended Overlaps:** There are two instances where overlaps are suggested by the timestamps.\n    - At [00:04], Speaker B starts speaking as Speaker A's turn ends. Speaker B says, \"Sorry to cut in,\" indicating an awareness of interrupting or overlapping Speaker A's previous sentence (\"...especially\"). This is a brief, acknowledged overlap, which is common and acceptable in natural conversation, not an extended or harmful one.\n    - At [00:23], Speaker B starts speaking while Speaker A is still talking (\"before\" at 00:24). This is a very brief overlap (approximately 1 second). This is a \"brief overlap\" as described in the criteria and is acceptable.\n- **Long Pauses:** There is one noticeable pause:\n    - Between Speaker B's first turn (ends at 00:10) and Speaker A's second turn (starts at 00:11), there is a 1-second pause. This is a \"small pause\" and is well within acceptable limits for natural conversation.\nOverall, the interaction flows very naturally with no prolonged pauses or extended, disruptive overlaps. The minor overlaps and pauses are typical of fluent human conversation.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_2/soda_4_processed_dialogues_part_11/SODA_PROCESSED--val--31917/stereo_dialogue.wav"
+  },
+  "13":{
+    "key": "SODA_PROCESSED--val--137529",
+    "text_input": "[00:00 - 00:05] Speaker A: I can't believe you're still out here! It's horrible weather, you should really come inside before\n[00:04 - 00:09] Speaker B: No, I'm fine, I don't mind the rain. Actually, I find it quite refreshing— it helps\n[00:08 - 00:13] Speaker A: Are you sure? You look like you're about to collapse. Here, let me help you up.\n[00:14 - 00:19] Speaker B: Thanks, but I'm really okay. I just wanted to enjoy the storm for a little while longer. It's so calming in\n[00:19 - 00:26] Speaker A: Exhilarating? I get that, but your health is more important. Seriously, you should come inside.\n[00:26 - 00:31] Speaker B: If you say so. I'm going back inside, but I'll leave the door open for you in case you change your mind.\n[00:31 - 00:32] Speaker A: Thanks, Aine.",
+    "model_output": "<response think>\nThe dialogue demonstrates strong response relevance and logical consistency. Speaker A consistently expresses concern for Speaker B's well-being in the bad weather and urges them to come inside. Speaker B consistently resists this notion, explaining their enjoyment of the weather, but eventually concedes.\n- At [00:04-00:09], Speaker B directly addresses Speaker A's concern, explaining their preference for the rain.\n- At [00:08-00:13], Speaker A escalates their concern, offering physical assistance, which is a logical follow-up to B's initial dismissal.\n- At [00:14-00:19], Speaker B reiterates their state and reason, maintaining their stance.\n- At [00:19-00:26], Speaker A slightly misinterprets B's last word (\"calming\" heard as \"exhilarating\" or something similar), but immediately links it back to the core topic of health and coming inside, maintaining topic coherence. This slight misinterpretation does not derail the conversation.\n- At [00:26-00:31], Speaker B's concession and offer (\"I'm going back inside, but I'll leave the door open for you\") is a logical conclusion to the persuasion attempt, assuming Speaker A is also outside or was outside trying to get Speaker B inside.\n- At [00:31-00:32], Speaker A's \"Thanks, Aine\" is a direct and relevant acknowledgment of B's offer.\nOverall, the conversation flows logically, and each speaker's turn is relevant to the preceding turn and the overarching topic.\n</response think>\n\n<fluency think>\nThe interactional fluency is excellent.\n- There are brief overlaps at [00:04 - 00:05] (1 second) and [00:08 - 00:09] (1 second). These are short and appear to be natural instances of speakers interrupting or interjecting, indicating engagement and urgency (on Speaker A's part, and a desire to respond on Speaker B's part). As per the guidelines, \"small pauses and brief overlaps in conversation are acceptable,\" and these fall within that acceptable range. They do not constitute \"extended overlaps\" or cause disruption.\n- There are no long pauses between speaker turns. The transitions between turns are smooth and natural, contributing to a fluid conversation. For instance, the gap between [00:13] and [00:14] is only 1 second, which is a natural pause for turn-taking.\nThe conversation feels natural and unimpeded by issues of timing or overlap.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_2/soda_4_processed_dialogues_part_14/SODA_PROCESSED--val--137529/stereo_dialogue.wav"
+  },
+  "14":{
+    "key": "SODA_PROCESSED--val--63644",
+    "text_input": "[00:00 - 00:11] Speaker A: Hey Noemi, I was just admiring your signature and I'm really impressed with how intricate and well-defined it is. It's definitely one of the best signatures I've ever seen. I\n[00:10 - 00:17] Speaker B: Oh, thank you! I actually put a lot of time and effort into perfecting it. A lot of people don't realize how important having a good signature can be.\n[00:18 - 00:27] Speaker A: I completely agree! Your signature is so unique and it really stands out. It must take you forever to write it out every time,\n[00:26 - 00:35] Speaker B: Actually, not anymore. It doesn't take me too long these days since I've had a lot of practice. But yeah, back when I first started working on it, it took me ages to get it right.\n[00:36 - 00:44] Speaker A: It definitely shows how much dedication and effort you put into it. Do you mind if I ask what inspired you to create such a detailed signature?\n[00:45 - 00:54] Speaker B: Not at all! I actually came up with the idea when I was trying to think of a way to make my name stand out more. I figured that if I had a really cool signature, people would remember my name better.\n[00:55 - 01:07] Speaker A: That makes total sense! And it definitely worked because like I said, your signature is really unique and eye-catching. So, do you have any advice for someone who is trying to create their own signature?\n[01:08 - 01:16] Speaker B: Yeah, definitely practice a lot! It takes time to perfect your technique and find the style that works best for you. But it's definitely worth it in the end.",
+    "model_output": "<response think>\nThe dialogue demonstrates excellent response relevance. Each speaker's turn logically follows the previous one, maintaining strong topic coherence throughout the conversation about Noemi's signature.\n- Speaker B's initial response directly addresses Speaker A's admiration and expands on the importance of a good signature.\n- Speaker A's subsequent comments and questions build naturally on Speaker B's statements (e.g., asking about the time it takes, then about inspiration, and finally for advice).\n- Speaker B's answers are always direct, relevant, and provide additional details or opinions pertinent to the discussion.\nThere are no instances of illogical leaps, off-topic remarks, or inconsistent information. The conversation flows smoothly from one point to the next, indicating a strong understanding and engagement between the speakers.\n</response think>\n\n<fluency think>\nThe interactional fluency in this dialogue is very good.\n- **Overlaps:** There are two brief overlaps detected:\n    - [00:10 - 00:11] Speaker A and Speaker B (1 second overlap)\n    - [00:26 - 00:27] Speaker A and Speaker B (1 second overlap)\nThese are short and natural, indicating active listening and turn-taking without being disruptive or prolonged. As per the instructions, brief overlaps are acceptable.\n- **Pauses:** There are brief pauses (1 second) between most turns where one speaker finishes and the other begins, for example:\n    - Speaker B ends at 00:17, Speaker A starts at 00:18.\n    - Speaker B ends at 00:35, Speaker A starts at 00:36.\n    - Speaker A ends at 00:44, Speaker B starts at 00:45.\n    - Speaker B ends at 00:54, Speaker A starts at 00:55.\n    - Speaker A ends at 01:07, Speaker B starts at 01:08.\nThese short pauses are natural for conversational turn-taking and do not suggest any awkwardness or hesitation. There are no long or prolonged pauses that would disrupt the flow. The conversation feels natural and well-paced.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_2/soda_4_processed_dialogues_part_18/SODA_PROCESSED--val--63644/stereo_dialogue.wav"
+  },
+  "15":{
+    "key": "DialogSum--test--14",
+    "text_input": "[00:00 - 00:01] Speaker A: What time is it, Tom?\n[00:01 - 00:04] Speaker B: Just a minute. It's ten to nine by my watch, but\n[00:03 - 00:08] Speaker A: Ten to nine? I had no idea it was so late. I must be off now.\n[00:09 - 00:10] Speaker B: What's the hurry?\n[00:10 - 00:12] Speaker A: I must catch the nine-thirty train.\n[00:13 - 00:22] Speaker B: You've plenty of time yet. The railway station is very close. It won't take more than twenty minutes to get there. By the way, you didn't finish—what were you saying about your watch earlier?",
+    "model_output": "<response think>\n**Response Relevance:**\n- **Logical Consistency:** The dialogue demonstrates excellent logical consistency. Speaker A's initial question about the time leads directly to Speaker B's answer. Speaker A's reaction (\"Ten to nine? I had no idea it was so late. I must be off now.\") is a direct and logical consequence of the time given. Speaker B's \"What's the hurry?\" directly follows Speaker A's need to leave. Speaker A's explanation (\"I must catch the nine-thirty train\") directly answers Speaker B's question. Speaker B's subsequent advice (\"You've plenty of time yet. The railway station is very close.\") logically addresses Speaker A's perceived urgency.\n- **Topic Coherence:** The conversation remains highly coherent. The primary topic revolves around the time and Speaker A's travel plans. The slight digression by Speaker B at the end (\"By the way, you didn't finish—what were you saying about your watch earlier?\") is a natural and coherent move, as it directly refers back to Speaker B's *own* unfinished statement from the beginning of the conversation (\"It's ten to nine by my watch, but...\"), which was interrupted by Speaker A. This demonstrates good memory and a desire to complete a thought, which is very natural in conversation.\n</response think>\n\n<fluency think>\n**Interactional Fluency:**\n- **Extended Overlaps:** There is one instance of overlap: Speaker A (00:03 - 00:08) begins speaking while Speaker B (00:01 - 00:04) is still saying \"but\". This overlap lasts for approximately 1 second (from 00:03 to 00:04). According to the criteria, \"brief overlaps in conversation are acceptable.\" This overlap is brief and seems natural, indicating Speaker A's immediate reaction and urgency upon hearing the time, cutting off Speaker B's incomplete thought. It does not hinder understanding or disrupt the flow significantly.\n- **Long Pauses:** There are no long pauses between speaker turns.\n    - The pause between Speaker A's turn ending at 00:08 and Speaker B's turn starting at 00:09 is 1 second, which is a small, natural conversational pause.\n    - The pause between Speaker A's turn ending at 00:12 and Speaker B's turn starting at 00:13 is also 1 second, again, a natural pause.\nOverall, the interaction flows very smoothly, typical of a natural conversation.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/output_2/processed_2_test_dialogsum/DialogSum--test--14/stereo_dialogue.wav"
+  },
+  "16":{
+    "key": "SODA_PROCESSED--train--841295",
+    "text_input": "[00:00 - 00:02] Speaker A: I'm going to plow the field today.\n[00:02 - 00:05] Speaker B: Okay, sounds good. Do you need any help?\n[00:05 - 00:07] Speaker A: No, I think I can handle it.\n[00:07 - 00:10] Speaker B: All right, well let me know if you need anything.\n[00:10 - 00:11] Speaker A: I will. Thanks.\n[00:11 - 00:14] Speaker B: Tavin, why do you want to plow the field?\n[00:15 - 00:20] Speaker A: Well, if I don't clear the field of debris, my crops won't be able to grow properly,\n[00:19 - 00:22] Speaker B: Excuse me for interrupting, but I've been wondering —— What\n[00:22 - 00:27] Speaker A: The weather has been quite nice lately, hasn't it? I think we might get some rain tomorrow.\n[00:27 - 00:31] Speaker B: That's not what I asked! I wanted to know about your crops, not the weather forecast.",
+    "model_output": "<response think>\nThe response relevance in this dialogue significantly deteriorates towards the end. Initially, the conversation is logically consistent and coherent: Speaker A states an intention, Speaker B offers help, and Speaker A declines. Speaker B then asks for clarification (\"why do you want to plow the field?\"), and Speaker A provides a relevant reason.\n\nHowever, a major breakdown occurs at [00:22 - 00:27]. Speaker B attempts to ask a question (\"I've been wondering —— What\"), but Speaker A completely ignores this, and instead abruptly changes the topic to the weather. This is a severe breach of logical consistency and topic coherence, as it is entirely unrelated to the previous discussion about farming and crops. Speaker B explicitly points out this irrelevance at [00:27 - 00:31], stating \"That's not what I asked! I wanted to know about your crops, not the weather forecast,\" confirming the lack of relevance from Speaker A's side. This abrupt and unprompted topic shift, ignoring a direct question, indicates significant issues with response relevance.\n</response think>\n\n<fluency think>\nInteractional fluency is problematic due to how the speakers handle turn-taking and topic management.\n\nThere is a brief overlap at [00:19 - 00:22] where Speaker B attempts to interrupt Speaker A. While the overlap itself is brief (around 1 second, from 0:19 to 0:20), Speaker B explicitly states \"Excuse me for interrupting,\" which indicates an awareness of attempting to interject. The issue isn't the brief overlap itself, but Speaker A's *response* to it. Instead of acknowledging Speaker B's attempted interjection or question, Speaker A completely disregards it and abruptly changes the topic to the weather at [00:22 - 00:27]. This demonstrates a severe lack of interactional fluency. Speaker A fails to engage with Speaker B's attempted turn, leading to a breakdown in conversational flow and causing frustration for Speaker B, as evidenced by their direct challenge at [00:27 - 00:31]. This is not a natural back-and-forth and disrupts the smooth progression of the conversation. There are no prolonged pauses.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/text_error_audios/processed_cut_soda_3_processed_dialogues_part_14_output/SODA_PROCESSED--train--841295/stereo_dialogue.wav"
+  },
+  "17":{
+    "key": "SODA_PROCESSED--train--1042183",
+    "text_input": "[00:00 - 00:02] Speaker A: I'm sorry, I know I've hurt you.\n[00:02 - 00:07] Speaker B: You've hurt me more than you can imagine. How could you do this to me? To our family?\n[00:07 - 00:12] Speaker A: I don't know. It just happened. I wasn't thinking clearly,\n[00:11 - 00:20] Speaker B: Excuse me for interrupting, but I need to ask - when you say you weren't thinking clearly, what exactly do you mean by that? Was there some specific factor that clouded your judgment?\n[00:21 - 01:20] Speaker A: Oh my goodness, let me explain in great, extensive, and excruciating detail about the numerous, countless and multifarious factors that contributed to my lack of clear thinking! First and foremost, there was the overwhelming, all-consuming and utterly debilitating stress from work that completely, totally and absolutely dominated my mental faculties. This stress was not just ordinary stress, mind you, but an extraordinary, unprecedented and unparalleled level of stress that made rational, logical and coherent thinking virtually impossible, unattainable and out of reach! Additionally, there were the compounding, accumulating and multiplying personal issues that further exacerbated, worsened and intensified my already compromised mental state! These included financial worries, family obligations, and health concerns that all converged, combined and coalesced into one giant, massive and enormous mental block that prevented me from thinking clearly, rationally or sensibly!",
+    "model_output": "<response think>\nThe initial turns of the dialogue demonstrate good response relevance, with each speaker's contribution logically following the previous one and maintaining topic coherence. Speaker A apologizes, Speaker B expresses hurt and asks for clarification, and Speaker A provides a preliminary explanation. Speaker B then logically asks for more specific details about Speaker A's claim of \"not thinking clearly.\"\n\nHowever, Speaker A's final response (00:21 - 01:20) presents a significant issue in terms of its appropriateness and naturalness within the context of a dialogue. While the *content* of the response technically addresses Speaker B's question about factors clouding judgment (thus maintaining logical consistency and topic coherence on a superficial level), the *delivery* is extremely problematic. Speaker A launches into an excessively verbose, repetitive, and theatrical monologue, using highly exaggerated language (\"numerous, countless and multifarious factors,\" \"overwhelming, all-consuming and utterly debilitating stress,\" \"extraordinary, unprecedented and unparalleled level of stress,\" etc.). This level of verbosity and dramatic flair is completely unnatural for a conversational turn, especially one in response to a simple request for clarification. It transforms the dialogue into a one-sided speech, making the response largely irrelevant to the goal of a natural, back-and-forth interaction. The quality of the interaction is severely degraded by this unnatural and disproportionate response.\n</response think>\n\n<fluency think>\nThere is a brief overlap between Speaker A's turn (00:07 - 00:12) and Speaker B's turn (00:11 - 00:20), lasting for approximately 1 second (00:11-00:12). This is a brief overlap and, according to the scoring criteria, is acceptable and does not negatively impact interactional fluency. There is also a 1-second pause between Speaker B's turn ending at 00:20 and Speaker A's turn starting at 00:21. This is a small, acceptable pause. Overall, the interactional fluency, in terms of timing of turns, pauses, and overlaps, is appropriate.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/text_error_audios/processed_cut_soda_3_processed_dialogues_part_14_output/SODA_PROCESSED--train--1042183/stereo_dialogue.wav"
+  },
+  "18":{
+    "key": "SODA_PROCESSED--train--713421",
+    "text_input": "[00:00 - 00:04] Speaker A: I can't believe you forgot my birthday. I thought we were best friends.\n[00:04 - 00:10] Speaker B: I'm so sorry, Augustus. I totally forgot. It's been a crazy week and I've had a lot on my mind, but\n[00:09 - 00:16] Speaker A: Wait, you mentioned having a lot on your mind — does that include planning for Julius Caesar's assassination next month?\n[00:17 - 00:22] Speaker B: Oh yes absolutely! Brutus and I have been coordinating the final details at the Colosseum every Tuesday night.\n[00:23 - 00:30] Speaker A: But... the Colosseum wasn't built until decades after Caesar's death? And wasn't the assassination at the Theatre of Pompey?\n[00:31 - 00:36] Speaker B: Oh right, I must be confusing it with when we planned to overthrow Emperor Nero instead. My bad!",
+    "model_output": "<response think>\nThe response relevance in this dialogue starts strong but quickly veers into irrelevance and absurdity.\n1.  **[00:00 - 00:04] Speaker A:** Complains about a forgotten birthday.\n2.  **[00:04 - 00:10] Speaker B:** Apologizes and provides a reasonable excuse, which is directly relevant to Speaker A's complaint.\n3.  **[00:09 - 00:16] Speaker A:** Here, response relevance significantly breaks down. Speaker A abruptly shifts the topic from a forgotten birthday to Julius Caesar's assassination, using Speaker B's phrase \"a lot on your mind\" as a highly tenuous and illogical bridge. This is a dramatic, unprovoked shift that completely derails the initial conversation's context. It introduces a surreal and anachronistic element.\n4.  **[00:17 - 00:22] Speaker B:** Speaker B, surprisingly, engages with this absurd new topic, confirming involvement. While locally relevant to Speaker A's previous question *within the new absurd context*, it continues the overall lack of logical consistency with the original dialogue. The mention of the Colosseum adds to the historical inaccuracy.\n5.  **[00:23 - 00:30] Speaker A:** Speaker A then points out historical inaccuracies in Speaker B's statement (Colosseum, Theatre of Pompey). This is relevant to the topic *they* introduced, but the topic itself is still far removed from the initial conversation.\n6.  **[00:31 - 00:36] Speaker B:** Speaker B acknowledges the error and further complicates the historical narrative by mentioning Emperor Nero. This is relevant as a response to Speaker A's correction.\n\nOverall, while the speakers respond to each other's immediate previous turns, the \"topic coherence\" and \"logical consistency\" are severely compromised by Speaker A's initial, jarring, and nonsensical topic shift (from a personal birthday slight to an ancient assassination plot). This makes the conversation highly unnatural and incoherent from a realistic standpoint. It's a comedic or absurdist dialogue rather than a naturally flowing one.\n</response think>\n\n<fluency think>\nThe interactional fluency in this dialogue is generally good.\n1.  **Pauses:** There are no long pauses between speaker turns. The transitions are quick and responsive (e.g., 1-second pauses between turns at [00:16 - 00:17], [00:22 - 00:23], [00:30 - 00:31]). These are natural and do not hinder the flow of conversation.\n2.  **Overlaps:** There is one brief overlap at [00:09 - 00:10], where Speaker A begins speaking while Speaker B is finishing their sentence. This overlap is only 1 second long and occurs naturally as Speaker A is interjecting to change the topic. It does not appear to be a prolonged or disruptive overlap.\nBased on the criteria, the brief overlap and lack of long pauses indicate good interactional fluency.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/text_error_audios/processed_cut_soda_3_processed_dialogues_part_14_output/SODA_PROCESSED--train--713421/stereo_dialogue.wav"
+  },
+  "19":{
+    "key": "SODA_PROCESSED--train--407825",
+    "text_input": "[00:00 - 00:01] Speaker A: Hey guys, how's it going?\n[00:02 - 00:05] Speaker B: Pretty good, I just ran my best time yet in the mile.\n[00:05 - 00:07] Speaker A: Oh really? What was your time?\n[00:08 - 00:11] Speaker B: 5 minutes and 32 seconds. But I know you can beat that if\n[00:11 - 00:19] Speaker A: Excuse me for interrupting, but when you say \"push yourself,\" what specific training techniques would you recommend to improve mile times?\n[00:20 - 00:27] Speaker B: Oh definitely! The best way is to only run uphill sprints while holding your breath - that builds both speed and endurance simultaneously.\n[00:28 - 00:35] Speaker A: Wait, holding your breath during uphill sprints? That sounds dangerous and counterproductive. Are you sure about that technique?",
+    "model_output": "<response think>\nThe dialogue starts with a casual greeting and transitions smoothly into Speaker B sharing a personal achievement (running a mile in a good time). Speaker A follows up logically by asking for the time. Speaker B provides the time and begins to offer encouragement or advice (\"But I know you can beat that if...\"). Speaker A then interrupts, acknowledging the interruption, to ask for specific training techniques based on an anticipated or implied point (\"push yourself\"). This turn is highly relevant, demonstrating good topic coherence and logical anticipation.\n\nThe significant issue arises with Speaker B's response at [00:20 - 00:27]. While it directly answers Speaker A's question about training techniques, the *content* of the advice (\"only run uphill sprints while holding your breath\") is extremely dangerous, counterproductive, and factually incorrect. This demonstrates a severe lack of logical consistency and factual soundness in Speaker B's contribution. Speaker A's subsequent reaction at [00:28 - 00:35] (\"That sounds dangerous and counterproductive. Are you sure about that technique?\") further highlights the problematic nature of Speaker B's advice, showing logical consistency from Speaker A's perspective by questioning the unsafe suggestion.\n\nTherefore, while topic coherence is generally maintained, the logical consistency of the *information provided* by Speaker B is critically flawed, leading to a significant issue in Response Relevance.\n</response think>\n\n<fluency think>\nThe dialogue exhibits generally good interactional fluency. There are no indications of long pauses between speaker turns. The only instance that needs evaluation is the interruption by Speaker A at [00:11 - 00:19]. Speaker A explicitly states, \"Excuse me for interrupting,\" which indicates they cut off Speaker B's previous turn. However, this is a brief, acknowledged interruption (\"...if\" cut off by Speaker A) rather than a prolonged or disruptive overlap. The acknowledgement makes it a less harmful instance of turn-taking. According to the criteria, \"Small pauses and brief overlaps in conversation are acceptable, while prolonged pauses and overlapping turns are harmful.\" This interruption falls into the \"brief overlaps\" category and is handled acceptably.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/text_error_audios/processed_cut_soda_3_processed_dialogues_part_14_output/SODA_PROCESSED--train--407825/stereo_dialogue.wav"
+  },
+  "20":{
+    "key": "SODA_PROCESSED--train--885929",
+    "text_input": "[00:00 - 00:04] Speaker A: Hey! How are you doing? I haven't seen you in forever!\n[00:04 - 00:10] Speaker B: I'm good, thanks for asking. I've been busy with work and school, but I'm doing well. How about we catch\n[00:09 - 00:17] Speaker A: That sounds great! Speaking of catching up soon - when exactly would work for you? I know you mentioned being busy with work and school.\n[00:18 - 00:24] Speaker B: Like I was saying, I've been managing my time well between work and school. It's been challenging but rewarding.",
+    "model_output": "<response think>\nThe dialogue starts with appropriate greetings and initial responses. Speaker A then picks up on Speaker B's implied suggestion to \"catch up\" and explicitly asks \"when exactly would work for you?\". However, Speaker B completely ignores this direct question in their final turn. Instead, Speaker B reiterates a point about managing time between work and school, which was already covered or implied in their initial statement (\"I've been busy with work and school, but I'm doing well\"). This is a significant breach of logical consistency and topic coherence, as the conversation fails to progress naturally towards setting a time to meet.\n</response think>\n\n<fluency think>\nThere are two minor points regarding interactional fluency. First, Speaker B's initial turn ends abruptly with \"How about we catch\", which is incomplete. This could indicate an interruption or a thought being cut short. Second, there is a 1-second overlap from [00:09 - 00:10] where Speaker A begins speaking before Speaker B finishes their incomplete sentence. However, the instructions state that \"small pauses and brief overlaps in conversation are acceptable,\" and a 1-second overlap falls into this category. There are no long pauses evident between turns. Overall, the fluency issues are minor and do not significantly hinder the flow of conversation from a timing perspective, although Speaker B's incomplete sentence is slightly unnatural.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "stereo_audio": "/root/autodl-tmp/text_error_audios/processed_cut_soda_3_processed_dialogues_part_14_output/SODA_PROCESSED--train--885929/stereo_dialogue.wav"
+  }
+}

cotSFT/test2/test-Copy1.json

@@ -0,0 +1,170 @@
+[
+  {
+    "key": "DialogSum--val--1",
+    "text_input": "[00:00 - 00:10] Speaker A: Does this happen all the time or mostly when you are doing physical activities like running or lifting weights, or does it occur even when you're just resting or lying down comfortably?\n[00:03 - 00:09] Speaker B: Sorry to cut in, but I just remembered—could this be related to stress? I've been under a lot of pressure at work lately.\n[00:10 - 00:18] Speaker A: Stress can definitely contribute to breathing issues. But I was about to ask if this happens mostly when you are active or at rest?\n[00:18 - 00:20] Speaker B: It happens a lot when I work out.\n[00:20 - 00:26] Speaker A: I see. I am going to send you to a pulmonary specialist who can run tests on you for asthma.\n[00:26 - 00:27] Speaker B: Thank you for your help, doctor.",
+    "model_output": "<response think>\nThe dialogue exhibits good response relevance.\n*   **Speaker A (00:00 - 00:10):** Asks a precise diagnostic question, relevant to understanding the nature of the breathing issues.\n*   **Speaker B (00:03 - 00:09):** While interrupting, Speaker B's question about stress is relevant to potential causes of health issues, even if it deviates slightly from Speaker A's immediate line of questioning. It's a natural patient interjection.\n*   **Speaker A (00:10 - 00:18):** Acknowledges Speaker B's point about stress, showing good listening and empathy (\"Stress can definitely contribute...\"), and then skillfully brings the conversation back to the original diagnostic question (\"But I was about to ask if this happens mostly when you are active or at rest?\"). This demonstrates logical steering of the conversation.\n*   **Speaker B (00:18 - 00:20):** Provides a direct and relevant answer to Speaker A's question.\n*   **Speaker A (00:20 - 00:26):** Based on the information, Speaker A proposes a logical and relevant next step (referral to a specialist for asthma testing).\n*   **Speaker B (00:26 - 00:27):** Concludes with an appropriate expression of gratitude.\n\nThe dialogue maintains logical consistency and topic coherence throughout, with Speaker A effectively managing the flow even when Speaker B introduces a new point.\n</response think>\n\n<fluency think>\nThe evaluation criteria state that \"Small pauses and brief overlaps in conversation are acceptable, while prolonged pauses and overlapping turns are harmful.\"\n\n*   **Overlap (00:03 - 00:09) Speaker B over Speaker A:** This is an extended overlap. Speaker B starts speaking at 00:03 while Speaker A is still speaking and continues until 00:09, while Speaker A finishes at 00:10. This is a significant interruption, as Speaker B completes an entire turn while Speaker A is still in the middle of theirs. The duration of the overlap (6 seconds for Speaker B's turn during Speaker A's 10-second turn) is substantial. Speaker B even acknowledges it with \"Sorry to cut in,\" indicating an awareness of the interruption. This type of extended overlap, where one speaker fully interjects and completes a thought over another, harms interactional fluency.\n\n*   **Pauses:** There are no long pauses between speaker turns. The transitions are relatively quick (e.g., A finishes at 00:18, B starts at 00:18; B finishes at 00:20, A starts at 00:20).\n\nThe presence of a prolonged and disruptive overlapping turn from Speaker B significantly detracts from the interactional fluency.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/output_overlap5s_dialogsum_testVal/processed_processed_2_val_dialogsum_output/DialogSum--val--1/stereo_dialogue.wav"
+  },
+  {
+    "key": "DialogSum--val--2",
+    "text_input": "[00:00 - 00:02] Speaker A: Hey Jimmy. Let's go workout later today.\n[00:03 - 00:11] Speaker B: Sure. What time do you want to plan on meeting up? I was hoping we could get an early start so we have enough time for a proper warm-up and cool-down session.\n[00:04 - 00:08] Speaker A: Actually, I was thinking around 3:30. Does that work for you?\n[00:11 - 00:14] Speaker B: That sounds good. Today we work on Legs and forearm.\n[00:14 - 00:22] Speaker A: Hey. I just played basketball earlier, so my legs are a little sore. Let's work out on arms and stomach today.\n[00:22 - 00:31] Speaker B: I'm on a weekly schedule. You're messing everything up with this change. But I guess I can adjust since I understand you're already fatigued from your basketball game earlier this afternoon.\n[00:29 - 00:35] Speaker A: up? I get it, but it's just switching two days. You can do legs on Friday.\n[00:35 - 00:38] Speaker B: Aright. I'll meet you at the gym at 3:30 then.",
+    "model_output": "<response think>\nThe dialogue exhibits strong response relevance. Speaker B's first response directly addresses Speaker A's workout proposal by asking for a time and providing a reason for an early start. Speaker A then directly answers the time query. When Speaker B introduces a specific workout plan, Speaker A logically explains why they need to change it due to fatigue and proposes an alternative. Speaker B acknowledges Speaker A's situation and expresses their difficulty with the change due to their schedule, which is a relevant emotional and logistical response. Speaker A then offers a practical solution to Speaker B's concern, which Speaker B accepts. All turns are logically consistent and maintain topic coherence, progressing the discussion smoothly from planning to execution and conflict resolution.\n</response think>\n\n<fluency think>\nThere are significant issues with interactional fluency due to extended overlaps.\n1.  **[00:04 - 00:08] Speaker A:** This turn overlaps with Speaker B's turn from [00:03 - 00:11]. Specifically, Speaker A begins speaking at 00:04 while Speaker B is still explaining \"I was hoping we could get an early start so we have enough time for a proper warm-up and cool-down session.\" This is a 4-second overlap, which is an extended duration and indicates Speaker A cutting off or speaking over Speaker B's explanation.\n2.  **[00:29 - 00:35] Speaker A:** This turn overlaps with Speaker B's turn from [00:22 - 00:31]. Speaker A begins speaking at 00:29 with \"up?\" while Speaker B is concluding \"you're already fatigued from your basketball game earlier this afternoon.\" This is a 2-second overlap. While shorter than the first, it still indicates Speaker A starting their turn before Speaker B has finished, impacting the natural flow.\nThere are no long pauses detected. However, the presence of these extended overlaps, particularly the 4-second one, disrupts the natural turn-taking and makes the interaction less fluent and natural.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/output_overlap5s_dialogsum_testVal/processed_processed_2_val_dialogsum_output/DialogSum--val--2/stereo_dialogue.wav"
+  },
+  {
+    "key": "DialogSum--val--3",
+    "text_input": "[00:00 - 00:02] Speaker A: I need to stop eating such unhealthy foods.\n[00:03 - 00:05] Speaker B: I know what you mean. I've started eating better myself.\n[00:06 - 00:07] Speaker A: What foods do you eat now?\n[00:08 - 00:19] Speaker B: I tend to stick to fruits, vegetables, and lean proteins like chicken and fish, which provide essential amino acids for muscle repair and help maintain a healthy metabolism throughout the day.\n[00:10 - 00:17] Speaker A: Sorry to cut in, but when you say vegetables, are you including things like potatoes or more leafy greens?\n[00:19 - 00:25] Speaker B: Mostly leafy greens, but I do include some root vegetables like carrots. I was going to say chicken as well.\n[00:26 - 00:29] Speaker A: Oh, I see. So, are those the only things you eat?\n[00:30 - 00:34] Speaker B: That's basically what I eat. I try to keep my meals simple and balanced.\n[00:34 - 00:36] Speaker A: Why aren't you eating anything else?\n[00:37 - 00:41] Speaker B: Well, fruits and vegetables are very healthy, and they provide essential nutrients.\n[00:42 - 00:42] Speaker A: And the chicken?\n[00:43 - 00:48] Speaker B: It's really healthy to eat when you bake it, especially if you avoid adding too much oil or seasoning.",
+    "model_output": "<response think>\nThe responses in the dialogue demonstrate strong logical consistency and topic coherence. Speaker B consistently provides relevant information about their diet in response to Speaker A's inquiries. Speaker A's questions, even when interrupting, are directly related to clarifying Speaker B's ongoing explanation. The conversation flows naturally from identifying a problem (unhealthy eating) to exploring a solution (Speaker B's diet) and then seeking clarification and rationale for that solution. There are no instances where a speaker's response is off-topic or illogical in context.\n</response think>\n\n<fluency think>\nInteractional fluency has a significant issue due to an extended overlap. At [00:10 - 00:17], Speaker A explicitly cuts into Speaker B's ongoing turn, creating a 7-second period of simultaneous speech. While Speaker A acknowledges the interruption (\"Sorry to cut in\"), this prolonged overlap is explicitly stated as harmful according to the evaluation criteria (\"prolonged...overlapping turns are harmful\"). Other turn transitions are quite natural, with no long pauses detected. However, this single, extended overlap significantly impacts the overall fluency of the interaction, preventing it from being consistently appropriate and natural.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/output_overlap5s_dialogsum_testVal/processed_processed_2_val_dialogsum_output/DialogSum--val--3/stereo_dialogue.wav"
+  },
+  {
+    "key": "DialogSum--val--4",
+    "text_input": "[00:00 - 00:01] Speaker A: Do you believe in UFOs?\n[00:02 - 00:03] Speaker B: Of course, they are out there.\n[00:04 - 00:13] Speaker A: But I never thought I'd meet someone who actually believes in them so strongly, especially someone who claims to have direct experiences with extraterrestrial beings.\n[00:05 - 00:09] Speaker B: Are you serious? They are called UFOs, so not everybody can see them.\n[00:14 - 00:16] Speaker A: Wait, you mean that you can see them?\n[00:16 - 00:18] Speaker B: That's right. I can see them in my dreams.\n[00:19 - 00:20] Speaker A: They come to the earth?\n[00:21 - 00:24] Speaker B: No. Their task is to send the aliens here from outer space.\n[00:24 - 00:31] Speaker A: Aliens from outer space? Do you talk to them? What do they usually say when you communicate with them?\n[00:32 - 00:39] Speaker B: OK, OK, one by one, please! They look like robots, but they can speak. Their mission is to make friends with human beings.\n[00:39 - 00:43] Speaker A: That means that you talk to them? In which language?\n[00:43 - 00:46] Speaker B: Of course in English, they learn English on Mars too.\n[00:46 - 00:51] Speaker A: Oh, and what do they look like again? You were about to say before I interrupted.\n[00:52 - 00:57] Speaker B: They look like robots, but with a more organic touch, like they're part machine, part living being.",
+    "model_output": "<response think>\nThe dialogue exhibits strong logical consistency and topic coherence. Speaker B's responses, while introducing increasingly fantastical elements (seeing UFOs in dreams, aliens sending them, aliens learning English on Mars), are consistently presented as answers or elaborations to Speaker A's questions and disbelief. Speaker A, in turn, reacts naturally to these claims, asking clarifying questions or expressing surprise, maintaining a logical flow of inquiry. For example, Speaker A's follow-up questions \"Wait, you mean that you can see them?\" and \"They come to the earth?\" are direct responses to Speaker B's previous statements. Even after the initial overlap, the conversation picks up coherently, with Speaker A referencing Speaker B's previous, interrupted statement (\"You were about to say before I interrupted\"). The overall content progression is coherent, even if the subject matter becomes absurd.\n</response think>\n\n<fluency think>\nThe interactional fluency is significantly hampered by a major extended overlap and a subsequent long pause. At [00:05 - 00:09], Speaker B interrupts Speaker A's prolonged statement ([00:04 - 00:13]) with \"Are you serious? They are called UFOs, so not everybody can see them.\" This is a 4-second overlap, which is prolonged and indicates a breakdown in natural turn-taking. Following this, there is a noticeable pause of 1 second from when Speaker A finishes their initial interrupted thought ([00:13]) until they re-engage with a new question at [00:14]. This long pause, coming directly after an extended overlap, suggests a disruption in the conversational rhythm and flow. While the rest of the dialogue has acceptable small pauses and no other prolonged overlaps, this specific sequence significantly impacts the overall fluency, making the conversation feel less natural and more disjointed at that point.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/output_overlap5s_dialogsum_testVal/processed_processed_2_val_dialogsum_output/DialogSum--val--4/stereo_dialogue.wav"
+  },
+  {
+    "key": "DialogSum--val--5",
+    "text_input": "[00:00 - 00:01] Speaker A: Did you go to school today?\n[00:02 - 00:03] Speaker B: Of course. Did you?\n[00:03 - 00:05] Speaker A: I didn't want to, so I didn't.\n[00:06 - 00:09] Speaker B: That's sad, but have you gone to the movies recently?\n[00:09 - 00:21] Speaker A: That's a surprising change in topic that caught me off guard since we were just talking about school attendance and suddenly shifted to entertainment options, but no, I haven't gone to the movies lately.\n[00:10 - 00:15] Speaker B: Sorry to jump in, but I was just thinking, is there a specific movie you're interested in?\n[00:21 - 00:27] Speaker A: I was about to say, that's a switch in topic. But no, I haven't gone to the movies. Why?\n[00:28 - 00:30] Speaker B: I really want to go to the movies this weekend.\n[00:30 - 00:31] Speaker A: So go then.\n[00:32 - 00:33] Speaker B: I really don't want to go by myself.\n[00:34 - 00:37] Speaker A: Well anyway, do you plan on going to school tomorrow?\n[00:38 - 00:43] Speaker B: No, I think I'm going to go to the movies. By the way, what were you going to say earlier about the switch in topic?\n[00:43 - 00:47] Speaker A: Oh, I just meant it was a surprising change in conversation.",
+    "model_output": "<response think>\n**Response Relevance Analysis:**\nThe dialogue exhibits significant issues with topic coherence and logical consistency.\n1.  **Abrupt Topic Shifts:** Speaker B initiates an abrupt topic shift from school to movies at [00:06], which Speaker A explicitly notes twice ([00:09 - 00:21] and [00:21 - 00:27]). While Speaker A's acknowledgement of the shift is relevant to the *interaction*, the shift itself disrupts the logical flow.\n2.  **Ignoring Previous Utterances:** After Speaker B expresses a desire to go to the movies but not alone ([00:32 - 00:33]), Speaker A completely ignores this point and abruptly switches the topic *back* to school attendance ([00:34 - 00:37]). This shows a lack of logical consistency in following up on the other speaker's expressed need or dilemma, undermining the collaborative nature of conversation.\n3.  **Multifaceted Responses:** Speaker B's turn at [00:38 - 00:43] attempts to respond to the new topic (school), reintroduce the old topic (movies), and follow up on an earlier meta-comment (the topic switch). While this shows an attempt at relevance, it highlights the scattered nature of the conversation due to the earlier abrupt shifts.\nOverall, while individual answers sometimes respond to the immediate question, the conversation lacks a coherent topic progression, features multiple jarring topic shifts initiated by both speakers, and occasionally ignores the implied conversational goals of the other speaker.\n</response think>\n\n<fluency think>\n**Interactional Fluency Analysis:**\n1.  **Extended Overlaps:** There is a notable extended overlap between [00:09 - 00:21] (Speaker A) and [00:10 - 00:15] (Speaker B). Speaker B begins speaking 1 second into Speaker A's long turn and continues for 5 seconds while Speaker A is still speaking. Speaker B even explicitly acknowledges this with \"Sorry to jump in,\" indicating a clear interruption rather than a brief, natural overlap. This prolonged and intentional interruption significantly harms the interactional fluency, making the conversation feel less natural and more like turn-taking is being actively disrupted.\n2.  **Long Pauses:** There are no long pauses detected between speaker turns. Most pauses are 0 or 1 second, which are natural and acceptable in conversational flow.\nThe primary detractor for interactional fluency is the extended and acknowledged overlap/interruption.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/output_overlap5s_dialogsum_testVal/processed_processed_2_val_dialogsum_output/DialogSum--val--5/stereo_dialogue.wav"
+  },
+  {
+    "key": "PLACES3.5--train--2002",
+    "text_input": "[00:00 - 00:00] Speaker A: Hi Bob!\n[00:01 - 00:02] Speaker B: Hey Alice!\n[00:02 - 00:06] Speaker A: I'm sorry, I've got to cut the call short for an urgent\n[00:05 - 00:07] Speaker B: Wait, before you go, is everything alright?\n[00:12 - 00:17] Speaker A: Yeah, everything's fine. I just wanted to let you know I won't be able to talk for a while.\n[00:18 - 00:20] Speaker B: That's ok! Let's reschedule!\n[00:20 - 00:24] Speaker A: No worries, I'm just in the middle of work! I'll be\n[00:23 - 00:26] Speaker B: Well, I'm busy working right now too, but I'll call back.\n[00:32 - 00:34] Speaker A: Alright, talk to you later!\n[00:35 - 00:36] Speaker B: You too!",
+    "model_output": "<response think>\nThe dialogue exhibits strong response relevance and logical consistency.\n1.  **[00:02 - 00:06] Speaker A: I'm sorry, I've got to cut the call short for an urgent**\n    **[00:05 - 00:07] Speaker B: Wait, before you go, is everything alright?**\n    Speaker B's immediate interjection shows concern and directly addresses Speaker A's abrupt attempt to end the call, maintaining logical flow.\n2.  **[00:12 - 00:17] Speaker A: Yeah, everything's fine. I just wanted to let you know I won't be able to talk for a while.**\n    Speaker A directly answers B's question and reiterates the initial point of ending the call, maintaining consistency.\n3.  **[00:18 - 00:20] Speaker B: That's ok! Let's reschedule!**\n    Speaker B accepts Speaker A's situation and offers a practical solution, which is perfectly coherent with the topic.\n4.  **[00:20 - 00:24] Speaker A: No worries, I'm just in the middle of work! I'll be**\n    **[00:23 - 00:26] Speaker B: Well, I'm busy working right now too, but I'll call back.**\n    Speaker A clarifies the reason for needing to end the call. Speaker B's response acknowledges A's situation and offers to initiate the next contact (\"I'll call back\"), which, while a bit of a reinterpretation of \"reschedule,\" is still logically consistent with the goal of ending the current call and continuing later. It's not illogical or off-topic.\n5.  The greetings and closings are also entirely appropriate and relevant.\n\nOverall, the responses are consistently appropriate, follow logically, and maintain topic coherence throughout the conversation.\n</response think>\n\n<fluency think>\nThe interaction exhibits significant issues with interactional fluency due to prolonged pauses.\n1.  **Overlap [00:05 - 00:07] Speaker B over [00:02 - 00:06] Speaker A:** There is a 1-second overlap (0:05 to 0:06). This is a brief overlap, common in natural conversation (e.g., interruption or eager response), and thus acceptable.\n2.  **Pause [00:07] Speaker B end to [00:12] Speaker A start:** There is a 5-second pause. This is a prolonged pause and significantly detracts from natural conversational flow, indicating a breakdown in turn-taking efficiency.\n3.  **Overlap [00:23 - 00:26] Speaker B over [00:20 - 00:24] Speaker A:** There is a 1-second overlap (0:23 to 0:24). This is another brief and acceptable overlap.\n4.  **Pause [00:26] Speaker B end to [00:32] Speaker A start:** There is a 6-second pause. This is another prolonged pause, even longer than the previous one, further harming the interactional fluency.\n\nWhile brief overlaps are acceptable, the presence of two notably long pauses (5 seconds and 6 seconds) between turns is a significant fluency issue as per the evaluation criteria, making the conversation feel unnatural and stilted.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/audio_error_audios/place_2_processed_dialogues_part_1(1)/PLACES3.5--train--2002/stereo_dialogue.wav"
+  },
+  {
+    "key": "PLACES3.5--train--2005",
+    "text_input": "[00:00 - 00:02] Speaker A: What do you know about braiding hair?\n[00:02 - 00:08] Speaker B: I have a friend who is a professional hair braider. We get together for coffee every couple of weeks, and\n[00:07 - 00:10] Speaker A: Really?! That's so cool! How did you get into that?\n[00:16 - 00:23] Speaker B: You know, I just heard about it one day. She did a really good job so I decided to let her do mine. And I've kept on going back because I\n[00:22 - 00:25] Speaker A: That's awesome. Do you mind if I take a picture?\n[00:32 - 00:32] Speaker B: Sure.\n[00:32 - 00:34] Speaker A: But I won't upload it anywhere.\n[00:35 - 00:37] Speaker B: Oh, good! Do you like hair braiding?\n[00:38 - 00:42] Speaker A: It's pretty awesome. I like how it gives your hair a really natural look.\n[00:42 - 00:44] Speaker B: When do you think you'll be ready to give it a try?\n[00:45 - 00:51] Speaker A: I don't think I have time to braid hair right now, but maybe in the future when things settle down a bit.",
+    "model_output": "<response think>\nThe dialogue generally demonstrates good response relevance and topic coherence. Speaker A initiates the conversation about hair braiding, and Speaker B responds by sharing a personal connection to the topic. Speaker A's follow-up questions and comments logically build on Speaker B's statements. The conversation maintains its focus on hair braiding throughout.\n\nHowever, there is one instance where the logical consistency and topic coherence are slightly strained. At [00:22 - 00:25], Speaker A asks, \"That's awesome. Do you mind if I take a picture?\" This question, while not completely irrelevant to the overall topic of hair braiding, comes immediately after Speaker B was explaining *how* they got into hair braiding (\"And I've kept on going back because I\"). The sudden shift from discussing Speaker B's personal history and experience to Speaker A requesting to take a picture of Speaker B's hair (implied) feels a bit abrupt and slightly out of sync with the immediate conversational thread. It's not a complete non-sequitur, but it slightly disrupts the narrative flow about B's experience. Despite this minor point, the speakers do manage to recover and bring the conversation back to the general topic of hair braiding, demonstrating overall topic coherence.\n</response think>\n\n<fluency think>\nInteractional fluency in this dialogue presents significant issues due to prolonged pauses.\n\n1.  **Long Pause 1:** There is a **6-second pause** between Speaker A's turn ending at [00:10] and Speaker B's turn starting at [00:16]. This is a very noticeable and prolonged silence that disrupts the natural conversational flow.\n2.  **Long Pause 2:** Another significant pause of **7 seconds** occurs between Speaker A's turn ending at [00:25] and Speaker B's turn starting at [00:32]. This is an even longer and more disruptive silence, indicating a breakdown in the smooth back-and-forth of the conversation.\n\nIn addition to the long pauses, Speaker B's turns end abruptly or trail off twice:\n*   [00:08] \"...every couple of weeks, and\"\n*   [00:23] \"...kept on going back because I\"\nWhile this could be due to interruption, it still contributes to a feeling of incomplete thoughts or a hesitant conversational style, impacting fluency.\n\nThere are also brief overlaps, for example:\n*   [00:07 - 00:10] Speaker A overlaps with Speaker B for about 1 second.\n*   [00:22 - 00:25] Speaker A overlaps with Speaker B for about 1 second.\nAs per the instructions, brief overlaps are acceptable and can indicate engagement. However, when combined with the prolonged pauses, these small overlaps do not compensate for the overall lack of fluency caused by the silences. The most significant issue for fluency is clearly the two extended pauses.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/audio_error_audios/place_2_processed_dialogues_part_1(1)/PLACES3.5--train--2005/stereo_dialogue.wav"
+  },
+  {
+    "key": "PLACES3.5--train--2006",
+    "text_input": "[00:00 - 00:01] Speaker A: So is my newborn potty training?\n[00:02 - 00:03] Speaker B: No, I don't think so.\n[00:04 - 00:08] Speaker A: Yeah, I thought so. It's really hard, but I am going to keep at it!\n[00:09 - 00:13] Speaker B: I remember when my daughter was first born. It was really challenging to\n[00:12 - 00:18] Speaker A: Overwhelming, right? I know exactly what you mean! It can feel like you are completely losing control.\n[00:25 - 00:29] Speaker B: I hear ya. Now my daughter is two. She is such an amazing little person.\n[00:29 - 00:32] Speaker A: Yeah, I guess kids grow up fast.\n[00:32 - 00:37] Speaker B: I love listening to the stories my daughter tells me about what happened at daycare. She\n[00:36 - 00:38] Speaker A: Oh, what's her favorite story?\n[00:44 - 00:46] Speaker B: My daughter loves the book Where The Wild Things Are.\n[00:46 - 00:49] Speaker A: Wow, that sounds like an interesting book.\n[00:49 - 00:56] Speaker B: I haven't read it myself, but my daughter tells me how the main character's parents are never around so he just runs around the house causing mischief.",
+    "model_output": "<response think>\nThe dialogue demonstrates strong logical consistency and topic coherence. Speakers consistently respond to each other's statements and questions in a relevant manner.\n- Speaker B's initial response \"No, I don't think so\" directly answers Speaker A's question about potty training.\n- Speaker A's follow-up (\"Yeah, I thought so. It's really hard...\") is a relevant elaboration.\n- Speaker B's shift to personal experience (\"I remember when my daughter was first born...\") is a natural progression when discussing challenges with newborns.\n- Speaker A's \"Overwhelming, right? I know exactly what you mean!\" is an empathetic and relevant continuation of B's incomplete thought, demonstrating active listening and shared understanding.\n- The conversation naturally flows through stages of parenting (newborn challenges to older child joys) and sub-topics (stories, books).\n- Speaker A's question \"Oh, what's her favorite story?\" directly follows B's mention of listening to stories.\n- Speaker B's answer about \"Where The Wild Things Are\" and subsequent elaboration on the book's content are all directly relevant to A's query.\nThere are no instances of off-topic remarks, misunderstandings, or illogical jumps in conversation.\n</response think>\n\n<fluency think>\nInteractional fluency has significant issues due to prolonged pauses.\n- **Overlap at [00:12 - 00:18]:** Speaker A starts talking at 0:12 while Speaker B is still speaking until 0:13 (\"challenging to\"). This is a brief 1-second overlap. While the prompt allows brief overlaps, this one occurs while Speaker B is mid-sentence, indicating Speaker A is cutting off Speaker B, which can disrupt flow.\n- **Long Pause at [00:18 - 00:25]:** There is a 7-second pause between Speaker A finishing their turn and Speaker B starting. This is a significant and prolonged pause, which clearly harms the natural flow and feels unnatural for a conversation.\n- **Overlap at [00:36 - 00:38]:** Speaker A starts talking at 0:36 while Speaker B is still speaking until 0:37 (\"She\"). This is another brief 1-second overlap, again cutting off Speaker B mid-sentence.\n- **Long Pause at [00:38 - 00:44]:** There is a 6-second pause between Speaker A finishing their turn and Speaker B starting. This is another significant and prolonged pause, similar to the previous one, and detrimental to fluency.\n\nThe two prolonged pauses (7 seconds and 6 seconds) are explicit issues according to the criteria (\"prolonged pauses... are harmful\"). While the overlaps are brief, their nature (cutting off the other speaker) combined with the long pauses severely impacts the interactional fluency, making the conversation feel stilted and unnatural despite its high relevance.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/audio_error_audios/place_2_processed_dialogues_part_1(1)/PLACES3.5--train--2006/stereo_dialogue.wav"
+  },
+  {
+    "key": "PLACES3.5--train--2009",
+    "text_input": "[00:00 - 00:01] Speaker A: Hey! How are you?\n[00:02 - 00:05] Speaker B: I'm doing pretty well. I've had a busy week at work.\n[00:05 - 00:07] Speaker A: What do you do for a living?\n[00:07 - 00:14] Speaker B: I work as a geophysicist. I enjoy geology and mineralogy, but I especially enjoy studying seismic\n[00:13 - 00:19] Speaker A: Oh, that's interesting! Speaking of geology, have you ever come across any rare minerals during your work?\n[00:25 - 00:31] Speaker B: Yes, I've encountered a few rare ones, but I was about to mention that I also have a strong interest in botany.\n[00:31 - 00:36] Speaker A: I like scenery and the outdoor environment. Have you ever tried flower arranging?\n[00:36 - 00:37] Speaker B: Sure! Have you?\n[00:38 - 00:41] Speaker A: Yeah, I have. I used to do it as a hobby in college.",
+    "model_output": "<response think>\nThe dialogue starts with a clear and relevant exchange. Speaker A asks \"How are you?\" and Speaker B responds, then Speaker A follows up by asking about Speaker B's profession, which is a natural progression.\n\nThe first minor issue regarding relevance appears at [00:13 - 00:19]. Speaker B is in the middle of explaining their work (\"I enjoy geology and mineralogy, but I especially enjoy studying seismic\"), and Speaker A interjects. While Speaker A's follow-up question (\"Speaking of geology, have you ever come across any rare minerals during your work?\") is related to what Speaker B has already mentioned (geology, mineralogy), the interruption itself slightly disrupts the logical flow of Speaker B's explanation. Speaker B then has to abruptly pivot from their incomplete sentence about \"seismic\" studies.\n\nA more significant issue with topic coherence occurs from [00:25 - 00:36]. Speaker B clearly signals a topic shift by stating, \"Yes, I've encountered a few rare ones, but I was about to mention that I also have a strong interest in botany.\" This is an explicit attempt to introduce a new subject. Speaker A's response, \"I like scenery and the outdoor environment. Have you ever tried flower arranging?\", is *tangentially* related to botany (flowers are plants), but it feels like a superficial jump rather than an engagement with the stated \"strong interest in botany.\" \"Botany\" implies the scientific study of plants, not just an appreciation of scenery or a specific craft like flower arranging. Speaker A misses an opportunity to delve into Speaker B's *strong interest* in botany and instead shifts to a more casual, less intellectually aligned topic. This suggests a slight lack of deep engagement with Speaker B's stated interest, pulling the conversation away from the intended topic pivot.\n\nThe subsequent turns about flower arranging are logically consistent within that narrow subtopic, but the initial transition to it from \"strong interest in botany\" isn't perfectly coherent.\n</response think>\n\n<fluency think>\nThe dialogue exhibits significant issues with interactional fluency, specifically regarding overlaps.\n\nAt [00:13 - 00:19], Speaker A's turn begins at [00:13], while Speaker B's turn is still ongoing until [00:14]. This is a clear overlap of 1 second (\"studying seismic\" being cut off by \"Oh, that's interesting!\"). This indicates that Speaker A interrupted Speaker B, preventing Speaker B from completing their thought or sentence.\n\nAnother very noticeable and problematic overlap occurs at [00:25 - 00:31]. Speaker B's turn begins at [00:25] and continues to [00:31]. However, Speaker A's turn also begins at [00:31], creating an instant and complete overlap with the very end of Speaker B's sentence (\"...I also have a strong interest in botany\"). This means Speaker A started speaking precisely as Speaker B was finishing, without any pause or wait for Speaker B's full utterance.\n\nThese two instances are not brief, acceptable overlaps. The first one is an interruption, cutting off Speaker B. The second is an immediate overlap at the end of Speaker B's turn, which suggests a lack of turn-taking fluidity, potentially making Speaker B feel unheard or rushed.\n\nThere are no long pauses between speaker turns, which is a positive, but the presence of extended and impactful overlaps significantly detracts from the interactional fluency.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/audio_error_audios/place_2_processed_dialogues_part_1(1)/PLACES3.5--train--2009/stereo_dialogue.wav"
+  },
+  {
+    "key": "PLACES3.5--train--2010",
+    "text_input": "[00:00 - 00:08] Speaker A: I've been really excited about exploring new cities. I feel like I need a break from my current city, so I'm thinking about spending the summer in Europe.\n[00:09 - 00:12] Speaker B: I always love taking vacations. Where would you like to visit first?\n[00:11 - 00:18] Speaker A: Sorry to cut in, but I wanted to mention Spain, France, Italy—those are the top places on my list.\n[00:24 - 00:28] Speaker B: I'm sure those places will be incredible! Are you hoping to get a teaching job while you travel?\n[00:29 - 00:41] Speaker A: Actually, that's something I've been considering. Teaching English could be a great way to immerse myself in the culture, but I'm also thinking about just taking the time to relax and explore the\n[00:40 - 00:48] Speaker B: I totally get that! Sometimes it's important to just unwind and not think about work. Have you looked into any specific cities in those countries yet?",
+    "model_output": "<response think>\nThe dialogue exhibits strong response relevance and logical consistency. Speaker A introduces the topic of wanting a break and exploring Europe. Speaker B responds appropriately by asking for more details about preferred destinations. Speaker A lists countries, which Speaker B acknowledges before smoothly transitioning to a related, but slightly new, topic about finding a teaching job while traveling. Speaker A addresses this new topic directly, and Speaker B acknowledges Speaker A's perspective before bringing the conversation back to specific travel plans (cities). The flow of topics is natural and coherent throughout the conversation.\n</response think>\n\n<fluency think>\nThere are two instances of brief overlaps:\n- `[00:11 - 00:18] Speaker A:` starts at 00:11 while `[00:09 - 00:12] Speaker B:` ends at 00:12. This is a 1-second overlap. Speaker A explicitly apologizes (\"Sorry to cut in\"), indicating awareness and that it's not a major disruption, fitting the \"brief overlaps are acceptable\" criterion.\n- `[00:40 - 00:48] Speaker B:` starts at 00:40 while `[00:29 - 00:41] Speaker A:` ends at 00:41. This is also a 1-second overlap, which is brief and acceptable.\n\nHowever, there is a significant long pause:\n- From `[00:11 - 00:18] Speaker A:` ending at 00:18 to `[00:24 - 00:28] Speaker B:` starting at 00:24, there is a 6-second pause. This is a \"prolonged pause\" and is harmful to interactional fluency, as per the scoring criteria.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/audio_error_audios/place_2_processed_dialogues_part_1(1)/PLACES3.5--train--2010/stereo_dialogue.wav"
+  },
+  {
+    "key": "SODA_PROCESSED--val--135940",
+    "text_input": "[00:00 - 00:04] Speaker A: Thank you so much for renting this apartment to me. I really appreciate it.\n[00:04 - 00:11] Speaker B: No problem, Rayaan. I'm glad you like it. It's always nice to have a new tenant who is excited about their new home. How do you like the layout of\n[00:10 - 00:23] Speaker A: Sorry to jump in, but I just wanted to mention how much I love the area! It's so different from my old neighborhood. Everything is so clean and well-kept here. I feel much safer walking around at night too.\n[00:23 - 00:32] Speaker B: That makes me happy to hear! We take a lot of pride in our community, and we want everyone to feel safe and comfortable here. If you ever need anything, don't hesitate to reach out\n[00:31 - 00:40] Speaker A: Actually, speaking of that, I was wondering if there's a local contact number I can reach in case of any emergencies or issues with the apartment?\n[00:40 - 00:47] Speaker B: Absolutely, I'll provide you with that. And as I was saying, we're always happy to help out our tenants however we can.",
+    "model_output": "<response think>\nThe dialogue demonstrates strong response relevance and logical consistency. Each speaker's turn directly addresses or builds upon the previous turn.\n- Speaker A expresses thanks for the apartment.\n- Speaker B responds appropriately, expresses gladness, and attempts to continue the conversation by asking about the layout.\n- Speaker A, while interrupting, immediately qualifies it with \"Sorry to jump in,\" and then shifts to a related topic (the area/neighborhood). This is a natural conversational shift for someone excited about a new place.\n- Speaker B acknowledges A's sentiment about the area and expands on it, mentioning community pride and offering help.\n- Speaker A then smoothly transitions from B's offer of help (\"If you ever need anything, don't hesitate to reach out\") to a specific, practical question about a local contact number, using \"speaking of that.\"\n- Speaker B provides a direct answer and reiterates their willingness to help.\nThe topic coherence is maintained throughout, moving from the apartment itself to the surrounding area, and then to practical aspects of being a tenant, all logically connected.\n</response think>\n\n<fluency think>\nThe dialogue exhibits good interactional fluency with some brief overlaps, which are explicitly stated as acceptable by the criteria.\n- There is a 1-second overlap at `[00:10 - 00:23]` where Speaker A begins speaking while Speaker B is still finishing their sentence `[00:04 - 00:11]`. This overlap is immediately acknowledged by Speaker A with \"Sorry to jump in,\" indicating awareness and natural conversational eagerness rather than a breakdown in communication.\n- There is another 1-second overlap at `[00:31 - 00:40]` where Speaker A starts speaking just as Speaker B finishes their turn `[00:23 - 00:32]`. This is also a brief overlap and doesn't hinder comprehension or flow.\nThe criteria state that \"brief overlaps in conversation are acceptable, while prolonged pauses and overlapping turns are harmful.\" These 1-second overlaps are brief and contribute to a natural, dynamic conversational rhythm rather than indicating a problem. There are no long pauses detected between turns.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/output_2/soda_4_processed_dialogues_part_14/SODA_PROCESSED--val--135940/stereo_dialogue.wav"
+  },
+  {
+    "key": "SODA_PROCESSED--val--132565",
+    "text_input": "[00:00 - 00:07] Speaker A: Hey, Lola. I know you've been admiring my watch for a while now and I wanted to show you that I care about you by giving it to you as\n[00:06 - 00:11] Speaker B: Wait, are you really sure about this? I don't want to take something that means so much to you.\n[00:12 - 00:18] Speaker A: Of course I'm sure. I want you to have it because it'll make you happy and I care about making you\n[00:17 - 00:21] Speaker B: Your happiness is important too, Kemoni. Are you really okay with parting with it?",
+    "model_output": "<response think>\nThe dialogue exhibits excellent response relevance. Speaker B's initial hesitation (\"Wait, are you really sure about this? I don't want to take something that means so much to you.\") directly responds to Speaker A's offer and the implied sentiment behind it. Speaker A's subsequent reassurance (\"Of course I'm sure. I want you to have it because it'll make you happy and I care about making you\") logically follows and addresses B's concerns. Finally, Speaker B's last statement (\"Your happiness is important too, Kemoni. Are you really okay with parting with it?\") continues the thread of mutual care and concern for the other's well-being, directly building on Speaker A's previous statement about making B happy. The conversation maintains strong topic coherence, staying focused on the watch, the act of giving/receiving, and the emotional implications for both parties. Each turn builds on the previous one in a natural and logical manner.\n</response think>\n\n<fluency think>\nThe interactional fluency is very good. There are two brief overlaps detected:\n- [00:06 - 00:07]: Speaker A: \"as\" and Speaker B: \"Wait, are you really sure about this?\" (1-second overlap)\n- [00:17 - 00:18]: Speaker A: \"making you\" and Speaker B: \"Your happiness is important too, Kemoni.\" (1-second overlap)\nThese are very short, natural overlaps that are common in fluent, spontaneous conversation, indicating engagement and eagerness to respond rather than interruption or conflict. There is also a brief 1-second pause between Speaker B's first turn ending at 00:11 and Speaker A's second turn starting at 00:12. These small pauses and brief overlaps are explicitly stated as acceptable in the scoring criteria and do not hinder the flow of the conversation. There are no prolonged pauses or extended overlapping turns that would be considered harmful.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/output_2/soda_4_processed_dialogues_part_14/SODA_PROCESSED--val--132565/stereo_dialogue.wav"
+  },
+  {
+    "key": "SODA_PROCESSED--val--31917",
+    "text_input": "[00:00 - 00:04] Speaker A: It's so nice in here. The warmth is really comforting, especially\n[00:04 - 00:10] Speaker B: Sorry to cut in, but I just noticed the scent of the candles. Do you know what fragrance it is? It's really soothing.\n[00:11 - 00:24] Speaker A: I love the smell of the candles too. I think it's a mix of lavender and vanilla, which makes it even more calming. By the way, the roses are beautiful. It's such a shame they only last a short period before\n[00:23 - 00:29] Speaker B: True, but don't you think that's what makes them even more special? The fact that they're so fleeting?",
+    "model_output": "<response think>\n**Response Relevance:**\n- **Logical Consistency:** The dialogue demonstrates strong logical consistency. Speaker B's initial interruption is acknowledged (\"Sorry to cut in\"), making it a polite and understandable deviation. Speaker A then directly answers Speaker B's question about the candle scent before smoothly transitioning to a new topic (roses) with \"By the way.\" Speaker B's final response directly addresses Speaker A's comment about the roses' short lifespan, offering a philosophical counterpoint. Each turn builds logically on the previous one, either by directly responding or by managing a smooth topic shift.\n- **Topic Coherence:** The conversation maintains good topic coherence. It starts with general comfort, transitions to a specific sensory detail (candles), and then to another specific sensory detail (roses). These topics are all related to the immediate environment and pleasant observations. The transitions, especially Speaker A's \"By the way,\" are natural and well-managed, preventing abruptness. There are no irrelevant or off-topic statements.\n</response think>\n\n<fluency think>\n**Interactional Fluency:**\n- **Extended Overlaps:** There are two instances where overlaps are suggested by the timestamps.\n    - At [00:04], Speaker B starts speaking as Speaker A's turn ends. Speaker B says, \"Sorry to cut in,\" indicating an awareness of interrupting or overlapping Speaker A's previous sentence (\"...especially\"). This is a brief, acknowledged overlap, which is common and acceptable in natural conversation, not an extended or harmful one.\n    - At [00:23], Speaker B starts speaking while Speaker A is still talking (\"before\" at 00:24). This is a very brief overlap (approximately 1 second). This is a \"brief overlap\" as described in the criteria and is acceptable.\n- **Long Pauses:** There is one noticeable pause:\n    - Between Speaker B's first turn (ends at 00:10) and Speaker A's second turn (starts at 00:11), there is a 1-second pause. This is a \"small pause\" and is well within acceptable limits for natural conversation.\nOverall, the interaction flows very naturally with no prolonged pauses or extended, disruptive overlaps. The minor overlaps and pauses are typical of fluent human conversation.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/output_2/soda_4_processed_dialogues_part_11/SODA_PROCESSED--val--31917/stereo_dialogue.wav"
+  },
+  {
+    "key": "SODA_PROCESSED--val--137529",
+    "text_input": "[00:00 - 00:05] Speaker A: I can't believe you're still out here! It's horrible weather, you should really come inside before\n[00:04 - 00:09] Speaker B: No, I'm fine, I don't mind the rain. Actually, I find it quite refreshing— it helps\n[00:08 - 00:13] Speaker A: Are you sure? You look like you're about to collapse. Here, let me help you up.\n[00:14 - 00:19] Speaker B: Thanks, but I'm really okay. I just wanted to enjoy the storm for a little while longer. It's so calming in\n[00:19 - 00:26] Speaker A: Exhilarating? I get that, but your health is more important. Seriously, you should come inside.\n[00:26 - 00:31] Speaker B: If you say so. I'm going back inside, but I'll leave the door open for you in case you change your mind.\n[00:31 - 00:32] Speaker A: Thanks, Aine.",
+    "model_output": "<response think>\nThe dialogue demonstrates strong response relevance and logical consistency. Speaker A consistently expresses concern for Speaker B's well-being in the bad weather and urges them to come inside. Speaker B consistently resists this notion, explaining their enjoyment of the weather, but eventually concedes.\n- At [00:04-00:09], Speaker B directly addresses Speaker A's concern, explaining their preference for the rain.\n- At [00:08-00:13], Speaker A escalates their concern, offering physical assistance, which is a logical follow-up to B's initial dismissal.\n- At [00:14-00:19], Speaker B reiterates their state and reason, maintaining their stance.\n- At [00:19-00:26], Speaker A slightly misinterprets B's last word (\"calming\" heard as \"exhilarating\" or something similar), but immediately links it back to the core topic of health and coming inside, maintaining topic coherence. This slight misinterpretation does not derail the conversation.\n- At [00:26-00:31], Speaker B's concession and offer (\"I'm going back inside, but I'll leave the door open for you\") is a logical conclusion to the persuasion attempt, assuming Speaker A is also outside or was outside trying to get Speaker B inside.\n- At [00:31-00:32], Speaker A's \"Thanks, Aine\" is a direct and relevant acknowledgment of B's offer.\nOverall, the conversation flows logically, and each speaker's turn is relevant to the preceding turn and the overarching topic.\n</response think>\n\n<fluency think>\nThe interactional fluency is excellent.\n- There are brief overlaps at [00:04 - 00:05] (1 second) and [00:08 - 00:09] (1 second). These are short and appear to be natural instances of speakers interrupting or interjecting, indicating engagement and urgency (on Speaker A's part, and a desire to respond on Speaker B's part). As per the guidelines, \"small pauses and brief overlaps in conversation are acceptable,\" and these fall within that acceptable range. They do not constitute \"extended overlaps\" or cause disruption.\n- There are no long pauses between speaker turns. The transitions between turns are smooth and natural, contributing to a fluid conversation. For instance, the gap between [00:13] and [00:14] is only 1 second, which is a natural pause for turn-taking.\nThe conversation feels natural and unimpeded by issues of timing or overlap.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/output_2/soda_4_processed_dialogues_part_14/SODA_PROCESSED--val--137529/stereo_dialogue.wav"
+  },
+  {
+    "key": "SODA_PROCESSED--val--63644",
+    "text_input": "[00:00 - 00:11] Speaker A: Hey Noemi, I was just admiring your signature and I'm really impressed with how intricate and well-defined it is. It's definitely one of the best signatures I've ever seen. I\n[00:10 - 00:17] Speaker B: Oh, thank you! I actually put a lot of time and effort into perfecting it. A lot of people don't realize how important having a good signature can be.\n[00:18 - 00:27] Speaker A: I completely agree! Your signature is so unique and it really stands out. It must take you forever to write it out every time,\n[00:26 - 00:35] Speaker B: Actually, not anymore. It doesn't take me too long these days since I've had a lot of practice. But yeah, back when I first started working on it, it took me ages to get it right.\n[00:36 - 00:44] Speaker A: It definitely shows how much dedication and effort you put into it. Do you mind if I ask what inspired you to create such a detailed signature?\n[00:45 - 00:54] Speaker B: Not at all! I actually came up with the idea when I was trying to think of a way to make my name stand out more. I figured that if I had a really cool signature, people would remember my name better.\n[00:55 - 01:07] Speaker A: That makes total sense! And it definitely worked because like I said, your signature is really unique and eye-catching. So, do you have any advice for someone who is trying to create their own signature?\n[01:08 - 01:16] Speaker B: Yeah, definitely practice a lot! It takes time to perfect your technique and find the style that works best for you. But it's definitely worth it in the end.",
+    "model_output": "<response think>\nThe dialogue demonstrates excellent response relevance. Each speaker's turn logically follows the previous one, maintaining strong topic coherence throughout the conversation about Noemi's signature.\n- Speaker B's initial response directly addresses Speaker A's admiration and expands on the importance of a good signature.\n- Speaker A's subsequent comments and questions build naturally on Speaker B's statements (e.g., asking about the time it takes, then about inspiration, and finally for advice).\n- Speaker B's answers are always direct, relevant, and provide additional details or opinions pertinent to the discussion.\nThere are no instances of illogical leaps, off-topic remarks, or inconsistent information. The conversation flows smoothly from one point to the next, indicating a strong understanding and engagement between the speakers.\n</response think>\n\n<fluency think>\nThe interactional fluency in this dialogue is very good.\n- **Overlaps:** There are two brief overlaps detected:\n    - [00:10 - 00:11] Speaker A and Speaker B (1 second overlap)\n    - [00:26 - 00:27] Speaker A and Speaker B (1 second overlap)\nThese are short and natural, indicating active listening and turn-taking without being disruptive or prolonged. As per the instructions, brief overlaps are acceptable.\n- **Pauses:** There are brief pauses (1 second) between most turns where one speaker finishes and the other begins, for example:\n    - Speaker B ends at 00:17, Speaker A starts at 00:18.\n    - Speaker B ends at 00:35, Speaker A starts at 00:36.\n    - Speaker A ends at 00:44, Speaker B starts at 00:45.\n    - Speaker B ends at 00:54, Speaker A starts at 00:55.\n    - Speaker A ends at 01:07, Speaker B starts at 01:08.\nThese short pauses are natural for conversational turn-taking and do not suggest any awkwardness or hesitation. There are no long or prolonged pauses that would disrupt the flow. The conversation feels natural and well-paced.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/output_2/soda_4_processed_dialogues_part_18/SODA_PROCESSED--val--63644/stereo_dialogue.wav"
+  },
+  {
+    "key": "DialogSum--test--14",
+    "text_input": "[00:00 - 00:01] Speaker A: What time is it, Tom?\n[00:01 - 00:04] Speaker B: Just a minute. It's ten to nine by my watch, but\n[00:03 - 00:08] Speaker A: Ten to nine? I had no idea it was so late. I must be off now.\n[00:09 - 00:10] Speaker B: What's the hurry?\n[00:10 - 00:12] Speaker A: I must catch the nine-thirty train.\n[00:13 - 00:22] Speaker B: You've plenty of time yet. The railway station is very close. It won't take more than twenty minutes to get there. By the way, you didn't finish—what were you saying about your watch earlier?",
+    "model_output": "<response think>\n**Response Relevance:**\n- **Logical Consistency:** The dialogue demonstrates excellent logical consistency. Speaker A's initial question about the time leads directly to Speaker B's answer. Speaker A's reaction (\"Ten to nine? I had no idea it was so late. I must be off now.\") is a direct and logical consequence of the time given. Speaker B's \"What's the hurry?\" directly follows Speaker A's need to leave. Speaker A's explanation (\"I must catch the nine-thirty train\") directly answers Speaker B's question. Speaker B's subsequent advice (\"You've plenty of time yet. The railway station is very close.\") logically addresses Speaker A's perceived urgency.\n- **Topic Coherence:** The conversation remains highly coherent. The primary topic revolves around the time and Speaker A's travel plans. The slight digression by Speaker B at the end (\"By the way, you didn't finish—what were you saying about your watch earlier?\") is a natural and coherent move, as it directly refers back to Speaker B's *own* unfinished statement from the beginning of the conversation (\"It's ten to nine by my watch, but...\"), which was interrupted by Speaker A. This demonstrates good memory and a desire to complete a thought, which is very natural in conversation.\n</response think>\n\n<fluency think>\n**Interactional Fluency:**\n- **Extended Overlaps:** There is one instance of overlap: Speaker A (00:03 - 00:08) begins speaking while Speaker B (00:01 - 00:04) is still saying \"but\". This overlap lasts for approximately 1 second (from 00:03 to 00:04). According to the criteria, \"brief overlaps in conversation are acceptable.\" This overlap is brief and seems natural, indicating Speaker A's immediate reaction and urgency upon hearing the time, cutting off Speaker B's incomplete thought. It does not hinder understanding or disrupt the flow significantly.\n- **Long Pauses:** There are no long pauses between speaker turns.\n    - The pause between Speaker A's turn ending at 00:08 and Speaker B's turn starting at 00:09 is 1 second, which is a small, natural conversational pause.\n    - The pause between Speaker A's turn ending at 00:12 and Speaker B's turn starting at 00:13 is also 1 second, again, a natural pause.\nOverall, the interaction flows very smoothly, typical of a natural conversation.\n</fluency think>\n\n<overall score>2</overall score>",
+    "predicted_score": 2,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/output_2/processed_2_test_dialogsum/DialogSum--test--14/stereo_dialogue.wav"
+  },
+  {
+    "key": "SODA_PROCESSED--train--841295",
+    "text_input": "[00:00 - 00:02] Speaker A: I'm going to plow the field today.\n[00:02 - 00:05] Speaker B: Okay, sounds good. Do you need any help?\n[00:05 - 00:07] Speaker A: No, I think I can handle it.\n[00:07 - 00:10] Speaker B: All right, well let me know if you need anything.\n[00:10 - 00:11] Speaker A: I will. Thanks.\n[00:11 - 00:14] Speaker B: Tavin, why do you want to plow the field?\n[00:15 - 00:20] Speaker A: Well, if I don't clear the field of debris, my crops won't be able to grow properly,\n[00:19 - 00:22] Speaker B: Excuse me for interrupting, but I've been wondering —— What\n[00:22 - 00:27] Speaker A: The weather has been quite nice lately, hasn't it? I think we might get some rain tomorrow.\n[00:27 - 00:31] Speaker B: That's not what I asked! I wanted to know about your crops, not the weather forecast.",
+    "model_output": "<response think>\nThe response relevance in this dialogue significantly deteriorates towards the end. Initially, the conversation is logically consistent and coherent: Speaker A states an intention, Speaker B offers help, and Speaker A declines. Speaker B then asks for clarification (\"why do you want to plow the field?\"), and Speaker A provides a relevant reason.\n\nHowever, a major breakdown occurs at [00:22 - 00:27]. Speaker B attempts to ask a question (\"I've been wondering —— What\"), but Speaker A completely ignores this, and instead abruptly changes the topic to the weather. This is a severe breach of logical consistency and topic coherence, as it is entirely unrelated to the previous discussion about farming and crops. Speaker B explicitly points out this irrelevance at [00:27 - 00:31], stating \"That's not what I asked! I wanted to know about your crops, not the weather forecast,\" confirming the lack of relevance from Speaker A's side. This abrupt and unprompted topic shift, ignoring a direct question, indicates significant issues with response relevance.\n</response think>\n\n<fluency think>\nInteractional fluency is problematic due to how the speakers handle turn-taking and topic management.\n\nThere is a brief overlap at [00:19 - 00:22] where Speaker B attempts to interrupt Speaker A. While the overlap itself is brief (around 1 second, from 0:19 to 0:20), Speaker B explicitly states \"Excuse me for interrupting,\" which indicates an awareness of attempting to interject. The issue isn't the brief overlap itself, but Speaker A's *response* to it. Instead of acknowledging Speaker B's attempted interjection or question, Speaker A completely disregards it and abruptly changes the topic to the weather at [00:22 - 00:27]. This demonstrates a severe lack of interactional fluency. Speaker A fails to engage with Speaker B's attempted turn, leading to a breakdown in conversational flow and causing frustration for Speaker B, as evidenced by their direct challenge at [00:27 - 00:31]. This is not a natural back-and-forth and disrupts the smooth progression of the conversation. There are no prolonged pauses.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/text_error_audios/processed_cut_soda_3_processed_dialogues_part_14_output/SODA_PROCESSED--train--841295/stereo_dialogue.wav"
+  },
+  {
+    "key": "SODA_PROCESSED--train--1042183",
+    "text_input": "[00:00 - 00:02] Speaker A: I'm sorry, I know I've hurt you.\n[00:02 - 00:07] Speaker B: You've hurt me more than you can imagine. How could you do this to me? To our family?\n[00:07 - 00:12] Speaker A: I don't know. It just happened. I wasn't thinking clearly,\n[00:11 - 00:20] Speaker B: Excuse me for interrupting, but I need to ask - when you say you weren't thinking clearly, what exactly do you mean by that? Was there some specific factor that clouded your judgment?\n[00:21 - 01:20] Speaker A: Oh my goodness, let me explain in great, extensive, and excruciating detail about the numerous, countless and multifarious factors that contributed to my lack of clear thinking! First and foremost, there was the overwhelming, all-consuming and utterly debilitating stress from work that completely, totally and absolutely dominated my mental faculties. This stress was not just ordinary stress, mind you, but an extraordinary, unprecedented and unparalleled level of stress that made rational, logical and coherent thinking virtually impossible, unattainable and out of reach! Additionally, there were the compounding, accumulating and multiplying personal issues that further exacerbated, worsened and intensified my already compromised mental state! These included financial worries, family obligations, and health concerns that all converged, combined and coalesced into one giant, massive and enormous mental block that prevented me from thinking clearly, rationally or sensibly!",
+    "model_output": "<response think>\nThe initial turns of the dialogue demonstrate good response relevance, with each speaker's contribution logically following the previous one and maintaining topic coherence. Speaker A apologizes, Speaker B expresses hurt and asks for clarification, and Speaker A provides a preliminary explanation. Speaker B then logically asks for more specific details about Speaker A's claim of \"not thinking clearly.\"\n\nHowever, Speaker A's final response (00:21 - 01:20) presents a significant issue in terms of its appropriateness and naturalness within the context of a dialogue. While the *content* of the response technically addresses Speaker B's question about factors clouding judgment (thus maintaining logical consistency and topic coherence on a superficial level), the *delivery* is extremely problematic. Speaker A launches into an excessively verbose, repetitive, and theatrical monologue, using highly exaggerated language (\"numerous, countless and multifarious factors,\" \"overwhelming, all-consuming and utterly debilitating stress,\" \"extraordinary, unprecedented and unparalleled level of stress,\" etc.). This level of verbosity and dramatic flair is completely unnatural for a conversational turn, especially one in response to a simple request for clarification. It transforms the dialogue into a one-sided speech, making the response largely irrelevant to the goal of a natural, back-and-forth interaction. The quality of the interaction is severely degraded by this unnatural and disproportionate response.\n</response think>\n\n<fluency think>\nThere is a brief overlap between Speaker A's turn (00:07 - 00:12) and Speaker B's turn (00:11 - 00:20), lasting for approximately 1 second (00:11-00:12). This is a brief overlap and, according to the scoring criteria, is acceptable and does not negatively impact interactional fluency. There is also a 1-second pause between Speaker B's turn ending at 00:20 and Speaker A's turn starting at 00:21. This is a small, acceptable pause. Overall, the interactional fluency, in terms of timing of turns, pauses, and overlaps, is appropriate.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/text_error_audios/processed_cut_soda_3_processed_dialogues_part_14_output/SODA_PROCESSED--train--1042183/stereo_dialogue.wav"
+  },
+  {
+    "key": "SODA_PROCESSED--train--713421",
+    "text_input": "[00:00 - 00:04] Speaker A: I can't believe you forgot my birthday. I thought we were best friends.\n[00:04 - 00:10] Speaker B: I'm so sorry, Augustus. I totally forgot. It's been a crazy week and I've had a lot on my mind, but\n[00:09 - 00:16] Speaker A: Wait, you mentioned having a lot on your mind — does that include planning for Julius Caesar's assassination next month?\n[00:17 - 00:22] Speaker B: Oh yes absolutely! Brutus and I have been coordinating the final details at the Colosseum every Tuesday night.\n[00:23 - 00:30] Speaker A: But... the Colosseum wasn't built until decades after Caesar's death? And wasn't the assassination at the Theatre of Pompey?\n[00:31 - 00:36] Speaker B: Oh right, I must be confusing it with when we planned to overthrow Emperor Nero instead. My bad!",
+    "model_output": "<response think>\nThe response relevance in this dialogue starts strong but quickly veers into irrelevance and absurdity.\n1.  **[00:00 - 00:04] Speaker A:** Complains about a forgotten birthday.\n2.  **[00:04 - 00:10] Speaker B:** Apologizes and provides a reasonable excuse, which is directly relevant to Speaker A's complaint.\n3.  **[00:09 - 00:16] Speaker A:** Here, response relevance significantly breaks down. Speaker A abruptly shifts the topic from a forgotten birthday to Julius Caesar's assassination, using Speaker B's phrase \"a lot on your mind\" as a highly tenuous and illogical bridge. This is a dramatic, unprovoked shift that completely derails the initial conversation's context. It introduces a surreal and anachronistic element.\n4.  **[00:17 - 00:22] Speaker B:** Speaker B, surprisingly, engages with this absurd new topic, confirming involvement. While locally relevant to Speaker A's previous question *within the new absurd context*, it continues the overall lack of logical consistency with the original dialogue. The mention of the Colosseum adds to the historical inaccuracy.\n5.  **[00:23 - 00:30] Speaker A:** Speaker A then points out historical inaccuracies in Speaker B's statement (Colosseum, Theatre of Pompey). This is relevant to the topic *they* introduced, but the topic itself is still far removed from the initial conversation.\n6.  **[00:31 - 00:36] Speaker B:** Speaker B acknowledges the error and further complicates the historical narrative by mentioning Emperor Nero. This is relevant as a response to Speaker A's correction.\n\nOverall, while the speakers respond to each other's immediate previous turns, the \"topic coherence\" and \"logical consistency\" are severely compromised by Speaker A's initial, jarring, and nonsensical topic shift (from a personal birthday slight to an ancient assassination plot). This makes the conversation highly unnatural and incoherent from a realistic standpoint. It's a comedic or absurdist dialogue rather than a naturally flowing one.\n</response think>\n\n<fluency think>\nThe interactional fluency in this dialogue is generally good.\n1.  **Pauses:** There are no long pauses between speaker turns. The transitions are quick and responsive (e.g., 1-second pauses between turns at [00:16 - 00:17], [00:22 - 00:23], [00:30 - 00:31]). These are natural and do not hinder the flow of conversation.\n2.  **Overlaps:** There is one brief overlap at [00:09 - 00:10], where Speaker A begins speaking while Speaker B is finishing their sentence. This overlap is only 1 second long and occurs naturally as Speaker A is interjecting to change the topic. It does not appear to be a prolonged or disruptive overlap.\nBased on the criteria, the brief overlap and lack of long pauses indicate good interactional fluency.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/text_error_audios/processed_cut_soda_3_processed_dialogues_part_14_output/SODA_PROCESSED--train--713421/stereo_dialogue.wav"
+  },
+  {
+    "key": "SODA_PROCESSED--train--407825",
+    "text_input": "[00:00 - 00:01] Speaker A: Hey guys, how's it going?\n[00:02 - 00:05] Speaker B: Pretty good, I just ran my best time yet in the mile.\n[00:05 - 00:07] Speaker A: Oh really? What was your time?\n[00:08 - 00:11] Speaker B: 5 minutes and 32 seconds. But I know you can beat that if\n[00:11 - 00:19] Speaker A: Excuse me for interrupting, but when you say \"push yourself,\" what specific training techniques would you recommend to improve mile times?\n[00:20 - 00:27] Speaker B: Oh definitely! The best way is to only run uphill sprints while holding your breath - that builds both speed and endurance simultaneously.\n[00:28 - 00:35] Speaker A: Wait, holding your breath during uphill sprints? That sounds dangerous and counterproductive. Are you sure about that technique?",
+    "model_output": "<response think>\nThe dialogue starts with a casual greeting and transitions smoothly into Speaker B sharing a personal achievement (running a mile in a good time). Speaker A follows up logically by asking for the time. Speaker B provides the time and begins to offer encouragement or advice (\"But I know you can beat that if...\"). Speaker A then interrupts, acknowledging the interruption, to ask for specific training techniques based on an anticipated or implied point (\"push yourself\"). This turn is highly relevant, demonstrating good topic coherence and logical anticipation.\n\nThe significant issue arises with Speaker B's response at [00:20 - 00:27]. While it directly answers Speaker A's question about training techniques, the *content* of the advice (\"only run uphill sprints while holding your breath\") is extremely dangerous, counterproductive, and factually incorrect. This demonstrates a severe lack of logical consistency and factual soundness in Speaker B's contribution. Speaker A's subsequent reaction at [00:28 - 00:35] (\"That sounds dangerous and counterproductive. Are you sure about that technique?\") further highlights the problematic nature of Speaker B's advice, showing logical consistency from Speaker A's perspective by questioning the unsafe suggestion.\n\nTherefore, while topic coherence is generally maintained, the logical consistency of the *information provided* by Speaker B is critically flawed, leading to a significant issue in Response Relevance.\n</response think>\n\n<fluency think>\nThe dialogue exhibits generally good interactional fluency. There are no indications of long pauses between speaker turns. The only instance that needs evaluation is the interruption by Speaker A at [00:11 - 00:19]. Speaker A explicitly states, \"Excuse me for interrupting,\" which indicates they cut off Speaker B's previous turn. However, this is a brief, acknowledged interruption (\"...if\" cut off by Speaker A) rather than a prolonged or disruptive overlap. The acknowledgement makes it a less harmful instance of turn-taking. According to the criteria, \"Small pauses and brief overlaps in conversation are acceptable, while prolonged pauses and overlapping turns are harmful.\" This interruption falls into the \"brief overlaps\" category and is handled acceptably.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/text_error_audios/processed_cut_soda_3_processed_dialogues_part_14_output/SODA_PROCESSED--train--407825/stereo_dialogue.wav"
+  },
+  {
+    "key": "SODA_PROCESSED--train--885929",
+    "text_input": "[00:00 - 00:04] Speaker A: Hey! How are you doing? I haven't seen you in forever!\n[00:04 - 00:10] Speaker B: I'm good, thanks for asking. I've been busy with work and school, but I'm doing well. How about we catch\n[00:09 - 00:17] Speaker A: That sounds great! Speaking of catching up soon - when exactly would work for you? I know you mentioned being busy with work and school.\n[00:18 - 00:24] Speaker B: Like I was saying, I've been managing my time well between work and school. It's been challenging but rewarding.",
+    "model_output": "<response think>\nThe dialogue starts with appropriate greetings and initial responses. Speaker A then picks up on Speaker B's implied suggestion to \"catch up\" and explicitly asks \"when exactly would work for you?\". However, Speaker B completely ignores this direct question in their final turn. Instead, Speaker B reiterates a point about managing time between work and school, which was already covered or implied in their initial statement (\"I've been busy with work and school, but I'm doing well\"). This is a significant breach of logical consistency and topic coherence, as the conversation fails to progress naturally towards setting a time to meet.\n</response think>\n\n<fluency think>\nThere are two minor points regarding interactional fluency. First, Speaker B's initial turn ends abruptly with \"How about we catch\", which is incomplete. This could indicate an interruption or a thought being cut short. Second, there is a 1-second overlap from [00:09 - 00:10] where Speaker A begins speaking before Speaker B finishes their incomplete sentence. However, the instructions state that \"small pauses and brief overlaps in conversation are acceptable,\" and a 1-second overlap falls into this category. There are no long pauses evident between turns. Overall, the fluency issues are minor and do not significantly hinder the flow of conversation from a timing perspective, although Speaker B's incomplete sentence is slightly unnatural.\n</fluency think>\n\n<overall score>1</overall score>",
+    "predicted_score": 1,
+    "prediction_attempts": 1,
+    "audio_url": "/root/autodl-tmp/text_error_audios/processed_cut_soda_3_processed_dialogues_part_14_output/SODA_PROCESSED--train--885929/stereo_dialogue.wav"
+  }
+]

cotSFT_new/.ipynb_checkpoints/all_audios_cotSFT-checkpoint.jsonl

@@ -0,0 +1,30 @@
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_manman_300/extracted_dialogues_manman/50/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HH_1/extracted_dialogues_girl_man/234/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_manman_300/extracted_dialogues_manman/234/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_3_interrupt/extracted_dialogues_HM_3/49/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_4_interrupt/extracted_dialogues_HM_4/51/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_1_interrupt/processed_extracted_dialogues_HM_1_interrupt/156/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HH_2/extracted_dialogues_man_girl/63/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_1_interrupt/processed_extracted_dialogues_HM_1_interrupt/52/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_girlgirl_300/extracted_dialogues_girlgirl/244/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_girlgirl_300/extracted_dialogues_girlgirl/184/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_manman_300/extracted_dialogues_manman/13/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_3_interrupt/extracted_dialogues_HM_3/121/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_manman_300/extracted_dialogues_manman/80/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_manman_300/extracted_dialogues_manman/222/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_2_interrupt/processed_extracted_dialogues_HM_2_interrupt/148/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_manman_300/extracted_dialogues_manman/42/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_3_interrupt/extracted_dialogues_HM_3/125/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HH_1/extracted_dialogues_girl_man/29/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_2_interrupt/processed_extracted_dialogues_HM_2_interrupt/39/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HH_1/extracted_dialogues_girl_man/137/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_girlgirl_300/extracted_dialogues_girlgirl/229/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_3_interrupt/extracted_dialogues_HM_3/33/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_2_interrupt/processed_extracted_dialogues_HM_2_interrupt/87/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_2_interrupt/processed_extracted_dialogues_HM_2_interrupt/10/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HH_2/extracted_dialogues_man_girl/136/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_4_interrupt/extracted_dialogues_HM_4/154/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_1_interrupt/processed_extracted_dialogues_HM_1_interrupt/110/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_2_interrupt/processed_extracted_dialogues_HM_2_interrupt/92/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_girlgirl_300/extracted_dialogues_girlgirl/85/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_manman_300/extracted_dialogues_manman/161/stereo_dialogue_with_laugh.wav"], "solution": [2]}

cotSFT_new/.ipynb_checkpoints/filter-checkpoint.py

@@ -0,0 +1,132 @@
+import json
+import os
+from datetime import datetime
+
+def filter_by_duration(input_file, output_file, min_duration=10, max_duration=100):
+    """
+    过滤JSON文件，只保留total_duration在[min_duration, max_duration]范围内的条目
+    并记录被删除的文件信息到日志文件
+    
+    :param input_file: 输入JSON文件路径
+    :param output_file: 输出JSON文件路径
+    :param min_duration: 最小持续时间（秒）
+    :param max_duration: 最大持续时间（秒）
+    """
+    # 创建日志目录
+    log_dir = os.path.join(os.path.dirname(output_file), "filter_logs")
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+    
+    # 创建日志文件（以当前时间命名）
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    log_file = os.path.join(log_dir, f"removed_entries_{timestamp}.log")
+    
+    # 加载原始JSON文件
+    with open(input_file, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+    
+    # 初始化过滤结果和删除列表
+    filtered_data = {}
+    removed_entries = []
+    
+    # 过滤数据并记录被删除的条目
+    for key, value in data.items():
+        if 'total_duration' in value and min_duration <= value['total_duration'] <= max_duration:
+            filtered_data[key] = value
+        else:
+            duration = value.get('total_duration', 'N/A')
+            removed_entries.append({
+                'key': key,
+                'duration': duration,
+                'original_dialog_id': value.get('original_dialog_id', 'N/A'),
+                'reason': 'too_short' if isinstance(duration, (int, float)) and duration < min_duration 
+                         else 'too_long' if isinstance(duration, (int, float)) and duration > max_duration 
+                         else 'missing_or_invalid'
+            })
+    
+    # 保存过滤后的结果
+    with open(output_file, 'w', encoding='utf-8') as f:
+        json.dump(filtered_data, f, indent=2, ensure_ascii=False)
+    
+    # 保存删除日志
+    with open(log_file, 'w', encoding='utf-8') as f:
+        f.write(f"Filtering log - {timestamp}\n")
+        f.write(f"Input file: {input_file}\n")
+        f.write(f"Output file: {output_file}\n")
+        f.write(f"Duration range: {min_duration}s to {max_duration}s\n\n")
+        f.write("Removed Entries:\n")
+        f.write("="*50 + "\n")
+        for entry in removed_entries:
+            f.write(f"Key: {entry['key']}\n")
+            f.write(f"Original Dialog ID: {entry['original_dialog_id']}\n")
+            f.write(f"Duration: {entry['duration']}s\n")
+            f.write(f"Reason: {entry['reason']}\n")
+            f.write("-"*50 + "\n")
+    
+    print(f"\n处理结果: {os.path.basename(input_file)}")
+    print(f"原始条目数: {len(data)}")
+    print(f"过滤后条目数: {len(filtered_data)}")
+    print(f"已删除 {len(removed_entries)} 个不符合时长要求的条目")
+    print(f"过滤后的数据已保存到: {output_file}")
+    print(f"删除条目日志已保存到: {log_file}")
+
+def process_directory(input_dir, output_dir, min_duration=10, max_duration=90):
+    """
+    处理目录中的所有JSON文件
+    """
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    
+    # 创建总日志文件
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    summary_log = os.path.join(output_dir, f"summary_removed_entries_{timestamp}.log")
+    
+    total_removed = 0
+    total_processed = 0
+    
+    with open(summary_log, 'w', encoding='utf-8') as summary_f:
+        summary_f.write(f"Summary Filtering Log - {timestamp}\n")
+        summary_f.write(f"Input directory: {input_dir}\n")
+        summary_f.write(f"Output directory: {output_dir}\n")
+        summary_f.write(f"Duration range: {min_duration}s to {max_duration}s\n\n")
+        
+        for filename in os.listdir(input_dir):
+            if filename.endswith('.json'):
+                input_path = os.path.join(input_dir, filename)
+                output_path = os.path.join(output_dir, filename)
+                
+                print(f"\n处理文件: {filename}")
+                filter_by_duration(input_path, output_path, min_duration, max_duration)
+                
+                # 读取单个文件日志以获取统计信息
+                log_dir = os.path.join(output_dir, "filter_logs")
+                latest_log = max(
+                    [f for f in os.listdir(log_dir) if f.startswith('removed_entries')],
+                    key=lambda f: os.path.getmtime(os.path.join(log_dir, f)))
+                
+                with open(os.path.join(log_dir, latest_log), 'r', encoding='utf-8') as log_f:
+                    log_content = log_f.read()
+                    removed_count = log_content.count("Key: ")
+                
+                summary_f.write(f"\nFile: {filename}\n")
+                summary_f.write(f"Removed entries: {removed_count}\n")
+                summary_f.write("-"*40 + "\n")
+                
+                total_removed += removed_count
+                total_processed += 1
+        
+        summary_f.write(f"\nTotal files processed: {total_processed}\n")
+        summary_f.write(f"Total entries removed: {total_removed}\n")
+    
+    print(f"\n处理完成！所有文件的总日志已保存到: {summary_log}")
+
+if __name__ == "__main__":
+    # 使用示例 - 处理单个文件
+    # input_json = "silence.json"  # 替换为你的输入文件路径
+    # output_json = "silence_filtered_output.json"  # 输出文件路径
+    # filter_by_duration(input_json, output_json)
+    
+    # 使用示例 - 处理整个目录
+    input_directory = "./"  # 替换为你的输入目录
+    output_directory = "./filtered_output"  # 替换为你的输出目录
+    process_directory(input_directory, output_directory)

cotSFT_new/.ipynb_checkpoints/gemini2-Copy1.5_metainfo-checkpoint.py

@@ -0,0 +1,317 @@
+import os
+import json
+import re
+import requests
+from tqdm import tqdm
+from datetime import datetime
+import glob
+from requests.exceptions import Timeout
+import argparse
+
+prompt_template = (
+    "# Interactional Dialogue Evaluation\n\n"
+    "**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\n"  
+    "Evaluate the quality of the interaction in the given dialogue transcript, focusing on:\n"
+    "**Response Relevance:** \n"
+    "**logical consistency, topic coherence**\n"
+    "**Interactional Fluency:**\n"
+    "**Detect and evaluate extended overlaps in conversation.**\n"
+    "**Detect and evaluate long pauses between speaker turns.\n\n**"
+    "**Note**: Small pauses and brief overlaps in conversation are acceptable, while prolonged pauses and overlapping turns are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n"
+    "## Scoring Criteria\n"
+    "Assign a single holistic score based on the combined evaluation:\n"
+    "`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n"
+    "`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n"
+    "## Evaluation Output Format:\n"
+    "Strictly follow this template:\n"
+    "<response think>\n"
+    "[Analysing Response Relevance and giving reasons for scoring...]\n"
+    "</response think>\n"
+    "<fluency think>\n"
+    "[Analysing Interactional Fluency and giving reasons for scoring.]\n"
+    "</fluency think>\n"
+    "<overall score>X</overall score>\n"
+)
+
+# API configuration
+url = "https://api2.aigcbest.top/v1/chat/completions"
+headers = {
+    "Authorization": "Bearer sk-yAIqUaGzzVNSesHq4mRPaCbt53MMFRJIMB97cS4FkRy6idwN",
+    "Content-Type": "application/json",
+    "Accept": "application/json"
+}
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Process text evaluation with Gemini model')
+    parser.add_argument('--input_file', type=str, required=True,
+                      help='Input JSON file containing text data')
+    parser.add_argument('--output_file', type=str, default='texterror_gemini.json',
+                      help='Output JSON file for results')
+    parser.add_argument('--error_file', type=str, default='texterror_gemini_error.json',
+                      help='Output JSON file for errors')
+    parser.add_argument('--checkpoint_dir', type=str, default='checkpoints_test_text',
+                      help='Directory for storing checkpoints')
+    parser.add_argument('--max_retries', type=int, default=3,
+                      help='Maximum number of retries for failed predictions')
+    parser.add_argument('--checkpoint_interval', type=int, default=20,
+                      help='Number of items to process before saving checkpoint')
+    return parser.parse_args()
+
+def extract_overall_score(output_str):
+    """Extract <overall score>X</overall score> from model output."""
+    score_pattern = r"<overall score>(\d+)</overall score>"
+    match = re.search(score_pattern, output_str)
+    if match:
+        try:
+            return int(match.group(1))
+        except ValueError:
+            pass
+    return None
+
+def validate_model_output(output_str):
+    """Validate that the model output contains all required tags"""
+    required_tags = [
+        "<response think>",
+        "</response think>",
+        "<fluency think>",
+        "</fluency think>",
+        "<overall score>",
+        "</overall score>"
+    ]
+    
+    for tag in required_tags:
+        if tag not in output_str:
+            return False
+    return True
+
+def extract_tag_content(output_str, tag_name):
+    """Extract content between opening and closing tags"""
+    start_tag = f"<{tag_name}>"
+    end_tag = f"</{tag_name}>"
+    try:
+        start_idx = output_str.find(start_tag) + len(start_tag)
+        end_idx = output_str.find(end_tag)
+        if start_idx == -1 or end_idx == -1:
+            return None
+        return output_str[start_idx:end_idx].strip()
+    except:
+        return None
+
+def format_model_output(output_str):
+    """Extract and format content from all required tags"""
+    response_content = extract_tag_content(output_str, "response think")
+    fluency_content = extract_tag_content(output_str, "fluency think")
+    score_content = extract_tag_content(output_str, "overall score")
+    
+    if not all([response_content, fluency_content, score_content]):
+        return None
+        
+    formatted_output = (
+        f"<response think>\n{response_content}\n</response think>\n\n"
+        f"<fluency think>\n{fluency_content}\n</fluency think>\n\n"
+        f"<overall score>{score_content}</overall score>"
+    )
+    return formatted_output
+
+def make_api_call(text_input, retry_count=0, max_retries=5):
+    """Make API call with retry logic for API errors"""
+    try:
+        print(f"Attempting API call (attempt {retry_count + 1}/{max_retries + 1})")
+        data_req = {
+            "model": "gemini-2.5-flash-preview-05-20-thinking",
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": prompt_template
+                        },
+                        {
+                            "type": "text",
+                            "text": text_input
+                        },
+                    ]
+                }
+            ],
+            "temperature": 1,
+        }
+        
+        response = requests.post(url, headers=headers, json=data_req, timeout=(200, 200))
+        print(f"API response received with status code: {response.status_code}")
+        
+        if response.status_code == 200:
+            model_output = response.json()['choices'][0]['message']['content']
+            if not validate_model_output(model_output):
+                print("Model output missing required tags, retrying...")
+                return None, None
+                
+            formatted_output = format_model_output(model_output)
+            if formatted_output is None:
+                print("Failed to extract content from tags, retrying...")
+                return None, None
+                
+            pred_score = extract_overall_score(model_output)
+            return formatted_output, pred_score
+        else:
+            print(f"API returned error status {response.status_code}: {response.text}")
+            if retry_count >= max_retries:
+                raise Exception(f"POST error {response.status_code}: {response.text}")
+            return None, None
+    except requests.exceptions.ConnectTimeout:
+        print(f"Connection timeout (>10s)")
+        if retry_count >= max_retries:
+            raise Exception("Connection timeout")
+        return None, None
+    except requests.exceptions.ReadTimeout:
+        print(f"Read timeout (>30s)")
+        if retry_count >= max_retries:
+            raise Exception("Read timeout")
+        return None, None
+    except Exception as e:
+        print(f"Unexpected error during API call: {str(e)}")
+        if retry_count >= max_retries:
+            raise e
+        return None, None
+
+def get_latest_checkpoint(checkpoint_dir):
+    """Get the latest checkpoint file and its processed count"""
+    checkpoint_files = glob.glob(os.path.join(checkpoint_dir, "checkpoint_*.json"))
+    if not checkpoint_files:
+        return None, 0
+    
+    latest_checkpoint = None
+    max_count = 0
+    for checkpoint in checkpoint_files:
+        try:
+            count = int(os.path.basename(checkpoint).split('_')[1])
+            if count > max_count:
+                max_count = count
+                latest_checkpoint = checkpoint
+        except (ValueError, IndexError):
+            continue
+    
+    return latest_checkpoint, max_count
+
+def save_checkpoint(results, processed_count, checkpoint_dir):
+    """Save results to a checkpoint file"""
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    checkpoint_file = os.path.join(checkpoint_dir, f"checkpoint_{processed_count}_{timestamp}.json")
+    with open(checkpoint_file, "w", encoding="utf-8") as f:
+        json.dump(results, f, indent=2, ensure_ascii=False)
+    print(f"Checkpoint saved: {checkpoint_file}")
+
+def main():
+    args = parse_args()
+    
+    # Initialize results storage
+    results = []
+    save_file_name = args.output_file
+    error_file_name = args.error_file
+    
+    # Create checkpoints directory
+    checkpoint_dir = args.checkpoint_dir
+    if not os.path.exists(checkpoint_dir):
+        os.makedirs(checkpoint_dir)
+    
+    # Load test data
+    all_data_file = args.input_file
+    with open(all_data_file, 'r', encoding='utf-8') as f:
+        all_data = json.load(f)
+    
+    # Initialize error tracking
+    error_results = []
+    
+    # Load checkpoint if exists
+    latest_checkpoint, checkpoint_count = get_latest_checkpoint(checkpoint_dir)
+    if latest_checkpoint:
+        print(f"Found latest checkpoint with {checkpoint_count} processed items: {latest_checkpoint}")
+        try:
+            with open(latest_checkpoint, 'r', encoding='utf-8') as f:
+                results = json.load(f)
+                print(f"Resumed from checkpoint: processed {len(results)} items")
+        except Exception as e:
+            print(f"Warning: Failed to load checkpoint {latest_checkpoint}: {e}")
+            results = []
+    else:
+        print("No checkpoint found, starting from scratch")
+        results = []
+    
+    max_prediction_retries = args.max_retries
+    total_count = 0
+    
+    for item in tqdm(all_data, desc="Processing texts"):
+        key = item.get('key')
+        text_input = item.get('model_output')
+        
+        if not text_input:
+            print(f"No text input found for key {key}, skipping...")
+            continue
+        
+        print(f"Processing text for key={key}")
+        
+        prediction_retry_count = 0
+        success = False
+        
+        while prediction_retry_count < max_prediction_retries and not success:
+            try:
+                print(f"\nProcessing attempt {prediction_retry_count + 1}")
+                model_output, pred_score = make_api_call(text_input)
+                
+                if model_output is None or pred_score is None:
+                    print("API call failed, retrying...")
+                    prediction_retry_count += 1
+                    continue
+                    
+                print(f"Received prediction: {pred_score}")
+                
+                if pred_score == 2:
+                    success = True
+                    print("Prediction score is 2, accepting result")
+                else:
+                    prediction_retry_count += 1
+                    print(f"Prediction score is not 2 (attempt {prediction_retry_count}/{max_prediction_retries})")
+                    if prediction_retry_count >= max_prediction_retries:
+                        print("Max retries reached, accepting last prediction")
+                        success = True
+                    else:
+                        continue
+                
+                results.append({
+                    "key": key,
+                    "text_input": text_input,
+                    "model_output": model_output,
+                    "predicted_score": pred_score,
+                    "prediction_attempts": prediction_retry_count + 1
+                })
+                
+                with open(save_file_name, "w", encoding="utf-8") as f:
+                    json.dump(results, f, indent=2, ensure_ascii=False)
+                    
+                total_count += 1
+                
+                if total_count % args.checkpoint_interval == 0:
+                    save_checkpoint(results, total_count, checkpoint_dir)
+                
+            except Exception as e:
+                error_msg = str(e)
+                print(f"Failed to process text for key {key}: {error_msg}")
+                error_results.append({
+                    "key": key,
+                    "text_input": text_input,
+                    "error": f"Exception: {error_msg}"
+                })
+                break
+                
+        with open(error_file_name, "w", encoding="utf-8") as f:
+            json.dump(error_results, f, indent=2, ensure_ascii=False)
+    
+    # Save final results
+    with open(save_file_name, "w", encoding="utf-8") as f:
+        json.dump(results, f, indent=2, ensure_ascii=False)
+    
+    print(f"Results saved to {save_file_name}")
+    print(f"Total processed items: {total_count}")
+
+if __name__ == "__main__":
+    main()

cotSFT_new/all_audios_cotSFT.jsonl

@@ -0,0 +1,30 @@
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_manman_300/extracted_dialogues_manman/50/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HH_1/extracted_dialogues_girl_man/234/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_manman_300/extracted_dialogues_manman/234/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_3_interrupt/extracted_dialogues_HM_3/49/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_4_interrupt/extracted_dialogues_HM_4/51/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_1_interrupt/processed_extracted_dialogues_HM_1_interrupt/156/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HH_2/extracted_dialogues_man_girl/63/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_1_interrupt/processed_extracted_dialogues_HM_1_interrupt/52/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_girlgirl_300/extracted_dialogues_girlgirl/244/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_girlgirl_300/extracted_dialogues_girlgirl/184/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_manman_300/extracted_dialogues_manman/13/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_3_interrupt/extracted_dialogues_HM_3/121/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_manman_300/extracted_dialogues_manman/80/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_manman_300/extracted_dialogues_manman/222/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_2_interrupt/processed_extracted_dialogues_HM_2_interrupt/148/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_manman_300/extracted_dialogues_manman/42/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_3_interrupt/extracted_dialogues_HM_3/125/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HH_1/extracted_dialogues_girl_man/29/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_2_interrupt/processed_extracted_dialogues_HM_2_interrupt/39/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HH_1/extracted_dialogues_girl_man/137/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_girlgirl_300/extracted_dialogues_girlgirl/229/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_3_interrupt/extracted_dialogues_HM_3/33/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_2_interrupt/processed_extracted_dialogues_HM_2_interrupt/87/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_2_interrupt/processed_extracted_dialogues_HM_2_interrupt/10/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HH_2/extracted_dialogues_man_girl/136/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_4_interrupt/extracted_dialogues_HM_4/154/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_1_interrupt/processed_extracted_dialogues_HM_1_interrupt/110/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_process_HM_2_interrupt/processed_extracted_dialogues_HM_2_interrupt/92/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_girlgirl_300/extracted_dialogues_girlgirl/85/stereo_dialogue_with_laugh.wav"], "solution": [2]}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/ms-swift/newdataset_10k/output_manman_300/extracted_dialogues_manman/161/stereo_dialogue_with_laugh.wav"], "solution": [2]}

cotSFT_new/cotSFT_10data/dataset_real_mission4.jsonl

@@ -0,0 +1,44 @@
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/001.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/002.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/003.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/004.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/005.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/006.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/007.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/008.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/009.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/010.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/011.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/012.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/013.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/014.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/015.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/016.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/017.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/018.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/019.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/020.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/021.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/第16开始txt不规范/022.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/xiaoyuaudios/xiaoyu1.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/xiaoyuaudios/xiaoyu2.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/xiaoyuaudios/xiaoyu3.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/xiaoyuaudios/xiaoyu4.wav"], "solution": 1}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/xiaoyuaudios/xiaoyu5.wav"], "solution": 1}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/duihua/duihua/001.wav"], "solution": 1}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/duihua/duihua/002.wav"], "solution": 1}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/duihua/duihua/003.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/duihua/duihua/004.wav"], "solution": 1}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/duihua/duihua/005.wav"], "solution": 1}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/duihua/duihua/006.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/duihua/duihua/007.wav"], "solution": 1}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/duihua/duihua/008.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/duihua/duihua/009.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/duihua/duihua/010.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/duihua/duihua/011.wav"], "solution": 1}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/duihua/duihua/012.wav"], "solution": 2}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/duihua/duihua/013.wav"], "solution": 1}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/duihua/duihua/014.wav"], "solution": 1}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/duihua/duihua/015.wav"], "solution": 1}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/duihua/duihua/016.wav"], "solution": 1}
+{"messages": [{"role": "user", "content": "<audio># Interactional Dialogue Evaluation\n\n**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\nListen to a two-person interactional dialogue speech (Dual-channel audio, with each channel representing one speaker), labeled as speakers A and B. Evaluate the quality of the interaction, focusing on:\n**Response Relevance:** \n**logical consistency, topic coherence**\n**Interactional Fluency:**\n**Detect and evaluate extended vocal overlaps, e.g., cross-channel overlap.**\n**Detect and evaluate long pauses, e.g., pauses more than 3s between speaker turns.\n\n****Note**: Small pauses and brief overlaps in audio are acceptable, while prolonged pauses and overlapping audio are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n## Scoring Criteria\nAssign a single holistic score based on the combined evaluation:\n`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n## Evaluation Output Format:\nStrictly follow this template:\n<response think>\n[Analysing Response Relevance and giving reasons for scoring...]\n</response think>\n<fluency think>\n[Analysing Interactional Fluency and giving reasons for scoring.]\n</fluency think>\n<overall score>X</overall score>\n"}], "audios": ["/root/autodl-tmp/wavrewardDataset/conversations/data/testdata/predict_result_mission4/audios/duihua/duihua/017.wav"], "solution": 2}

cotSFT_new/filter.py

@@ -0,0 +1,132 @@
+import json
+import os
+from datetime import datetime
+
+def filter_by_duration(input_file, output_file, min_duration=10, max_duration=100):
+    """
+    过滤JSON文件，只保留total_duration在[min_duration, max_duration]范围内的条目
+    并记录被删除的文件信息到日志文件
+    
+    :param input_file: 输入JSON文件路径
+    :param output_file: 输出JSON文件路径
+    :param min_duration: 最小持续时间（秒）
+    :param max_duration: 最大持续时间（秒）
+    """
+    # 创建日志目录
+    log_dir = os.path.join(os.path.dirname(output_file), "filter_logs")
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+    
+    # 创建日志文件（以当前时间命名）
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    log_file = os.path.join(log_dir, f"removed_entries_{timestamp}.log")
+    
+    # 加载原始JSON文件
+    with open(input_file, 'r', encoding='utf-8') as f:
+        data = json.load(f)
+    
+    # 初始化过滤结果和删除列表
+    filtered_data = {}
+    removed_entries = []
+    
+    # 过滤数据并记录被删除的条目
+    for key, value in data.items():
+        if 'total_duration' in value and min_duration <= value['total_duration'] <= max_duration:
+            filtered_data[key] = value
+        else:
+            duration = value.get('total_duration', 'N/A')
+            removed_entries.append({
+                'key': key,
+                'duration': duration,
+                'original_dialog_id': value.get('original_dialog_id', 'N/A'),
+                'reason': 'too_short' if isinstance(duration, (int, float)) and duration < min_duration 
+                         else 'too_long' if isinstance(duration, (int, float)) and duration > max_duration 
+                         else 'missing_or_invalid'
+            })
+    
+    # 保存过滤后的结果
+    with open(output_file, 'w', encoding='utf-8') as f:
+        json.dump(filtered_data, f, indent=2, ensure_ascii=False)
+    
+    # 保存删除日志
+    with open(log_file, 'w', encoding='utf-8') as f:
+        f.write(f"Filtering log - {timestamp}\n")
+        f.write(f"Input file: {input_file}\n")
+        f.write(f"Output file: {output_file}\n")
+        f.write(f"Duration range: {min_duration}s to {max_duration}s\n\n")
+        f.write("Removed Entries:\n")
+        f.write("="*50 + "\n")
+        for entry in removed_entries:
+            f.write(f"Key: {entry['key']}\n")
+            f.write(f"Original Dialog ID: {entry['original_dialog_id']}\n")
+            f.write(f"Duration: {entry['duration']}s\n")
+            f.write(f"Reason: {entry['reason']}\n")
+            f.write("-"*50 + "\n")
+    
+    print(f"\n处理结果: {os.path.basename(input_file)}")
+    print(f"原始条目数: {len(data)}")
+    print(f"过滤后条目数: {len(filtered_data)}")
+    print(f"已删除 {len(removed_entries)} 个不符合时长要求的条目")
+    print(f"过滤后的数据已保存到: {output_file}")
+    print(f"删除条目日志已保存到: {log_file}")
+
+def process_directory(input_dir, output_dir, min_duration=10, max_duration=90):
+    """
+    处理目录中的所有JSON文件
+    """
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+    
+    # 创建总日志文件
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    summary_log = os.path.join(output_dir, f"summary_removed_entries_{timestamp}.log")
+    
+    total_removed = 0
+    total_processed = 0
+    
+    with open(summary_log, 'w', encoding='utf-8') as summary_f:
+        summary_f.write(f"Summary Filtering Log - {timestamp}\n")
+        summary_f.write(f"Input directory: {input_dir}\n")
+        summary_f.write(f"Output directory: {output_dir}\n")
+        summary_f.write(f"Duration range: {min_duration}s to {max_duration}s\n\n")
+        
+        for filename in os.listdir(input_dir):
+            if filename.endswith('.json'):
+                input_path = os.path.join(input_dir, filename)
+                output_path = os.path.join(output_dir, filename)
+                
+                print(f"\n处理文件: {filename}")
+                filter_by_duration(input_path, output_path, min_duration, max_duration)
+                
+                # 读取单个文件日志以获取统计信息
+                log_dir = os.path.join(output_dir, "filter_logs")
+                latest_log = max(
+                    [f for f in os.listdir(log_dir) if f.startswith('removed_entries')],
+                    key=lambda f: os.path.getmtime(os.path.join(log_dir, f)))
+                
+                with open(os.path.join(log_dir, latest_log), 'r', encoding='utf-8') as log_f:
+                    log_content = log_f.read()
+                    removed_count = log_content.count("Key: ")
+                
+                summary_f.write(f"\nFile: {filename}\n")
+                summary_f.write(f"Removed entries: {removed_count}\n")
+                summary_f.write("-"*40 + "\n")
+                
+                total_removed += removed_count
+                total_processed += 1
+        
+        summary_f.write(f"\nTotal files processed: {total_processed}\n")
+        summary_f.write(f"Total entries removed: {total_removed}\n")
+    
+    print(f"\n处理完成！所有文件的总日志已保存到: {summary_log}")
+
+if __name__ == "__main__":
+    # 使用示例 - 处理单个文件
+    # input_json = "silence.json"  # 替换为你的输入文件路径
+    # output_json = "silence_filtered_output.json"  # 输出文件路径
+    # filter_by_duration(input_json, output_json)
+    
+    # 使用示例 - 处理整个目录
+    input_directory = "./"  # 替换为你的输入目录
+    output_directory = "./filtered_output"  # 替换为你的输出目录
+    process_directory(input_directory, output_directory)

cotSFT_new/gemini2.5_metainfo.py

@@ -0,0 +1,317 @@
+import os
+import json
+import re
+import requests
+from tqdm import tqdm
+from datetime import datetime
+import glob
+from requests.exceptions import Timeout
+import argparse
+
+prompt_template = (
+    "# Interactional Dialogue Evaluation\n\n"
+    "**IMPORTANT**: Evaluation must include `<response think>` and `<fluency think>` analysis and `<overall score>` rating.\n"  
+    "Evaluate the quality of the interaction in the given dialogue transcript, focusing on:\n"
+    "**Response Relevance:** \n"
+    "**logical consistency, topic coherence**\n"
+    "**Interactional Fluency:**\n"
+    "**Detect and evaluate extended overlaps in conversation.**\n"
+    "**Detect and evaluate long pauses between speaker turns.\n\n**"
+    "**Note**: Small pauses and brief overlaps in conversation are acceptable, while prolonged pauses and overlapping turns are harmful. You should consider Response Relevance and Interactional Fluency separately, and provide the corresponding thinking process.\n\n"
+    "## Scoring Criteria\n"
+    "Assign a single holistic score based on the combined evaluation:\n"
+    "`1` (Poor): Significant issues in either  **Response Relevance ** or  **Interactional Fluency. **\n"
+    "`2` (Excellent): Both **Response Relevance ** and  **Interactional Fluency ** are consistently appropriate and natural.\n"
+    "## Evaluation Output Format:\n"
+    "Strictly follow this template:\n"
+    "<response think>\n"
+    "[Analysing Response Relevance and giving reasons for scoring...]\n"
+    "</response think>\n"
+    "<fluency think>\n"
+    "[Analysing Interactional Fluency and giving reasons for scoring.]\n"
+    "</fluency think>\n"
+    "<overall score>X</overall score>\n"
+)
+
+# API configuration
+url = "https://api2.aigcbest.top/v1/chat/completions"
+headers = {
+    "Authorization": "Bearer sk-yAIqUaGzzVNSesHq4mRPaCbt53MMFRJIMB97cS4FkRy6idwN",
+    "Content-Type": "application/json",
+    "Accept": "application/json"
+}
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Process text evaluation with Gemini model')
+    parser.add_argument('--input_file', type=str, required=True,
+                      help='Input JSON file containing text data')
+    parser.add_argument('--output_file', type=str, default='texterror_gemini.json',
+                      help='Output JSON file for results')
+    parser.add_argument('--error_file', type=str, default='texterror_gemini_error.json',
+                      help='Output JSON file for errors')
+    parser.add_argument('--checkpoint_dir', type=str, default='checkpoints_test_text',
+                      help='Directory for storing checkpoints')
+    parser.add_argument('--max_retries', type=int, default=3,
+                      help='Maximum number of retries for failed predictions')
+    parser.add_argument('--checkpoint_interval', type=int, default=20,
+                      help='Number of items to process before saving checkpoint')
+    return parser.parse_args()
+
+def extract_overall_score(output_str):
+    """Extract <overall score>X</overall score> from model output."""
+    score_pattern = r"<overall score>(\d+)</overall score>"
+    match = re.search(score_pattern, output_str)
+    if match:
+        try:
+            return int(match.group(1))
+        except ValueError:
+            pass
+    return None
+
+def validate_model_output(output_str):
+    """Validate that the model output contains all required tags"""
+    required_tags = [
+        "<response think>",
+        "</response think>",
+        "<fluency think>",
+        "</fluency think>",
+        "<overall score>",
+        "</overall score>"
+    ]
+    
+    for tag in required_tags:
+        if tag not in output_str:
+            return False
+    return True
+
+def extract_tag_content(output_str, tag_name):
+    """Extract content between opening and closing tags"""
+    start_tag = f"<{tag_name}>"
+    end_tag = f"</{tag_name}>"
+    try:
+        start_idx = output_str.find(start_tag) + len(start_tag)
+        end_idx = output_str.find(end_tag)
+        if start_idx == -1 or end_idx == -1:
+            return None
+        return output_str[start_idx:end_idx].strip()
+    except:
+        return None
+
+def format_model_output(output_str):
+    """Extract and format content from all required tags"""
+    response_content = extract_tag_content(output_str, "response think")
+    fluency_content = extract_tag_content(output_str, "fluency think")
+    score_content = extract_tag_content(output_str, "overall score")
+    
+    if not all([response_content, fluency_content, score_content]):
+        return None
+        
+    formatted_output = (
+        f"<response think>\n{response_content}\n</response think>\n\n"
+        f"<fluency think>\n{fluency_content}\n</fluency think>\n\n"
+        f"<overall score>{score_content}</overall score>"
+    )
+    return formatted_output
+
+def make_api_call(text_input, retry_count=0, max_retries=5):
+    """Make API call with retry logic for API errors"""
+    try:
+        print(f"Attempting API call (attempt {retry_count + 1}/{max_retries + 1})")
+        data_req = {
+            "model": "gemini-2.5-flash-preview-05-20-thinking",
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": prompt_template
+                        },
+                        {
+                            "type": "text",
+                            "text": text_input
+                        },
+                    ]
+                }
+            ],
+            "temperature": 1,
+        }
+        
+        response = requests.post(url, headers=headers, json=data_req, timeout=(200, 200))
+        print(f"API response received with status code: {response.status_code}")
+        
+        if response.status_code == 200:
+            model_output = response.json()['choices'][0]['message']['content']
+            if not validate_model_output(model_output):
+                print("Model output missing required tags, retrying...")
+                return None, None
+                
+            formatted_output = format_model_output(model_output)
+            if formatted_output is None:
+                print("Failed to extract content from tags, retrying...")
+                return None, None
+                
+            pred_score = extract_overall_score(model_output)
+            return formatted_output, pred_score
+        else:
+            print(f"API returned error status {response.status_code}: {response.text}")
+            if retry_count >= max_retries:
+                raise Exception(f"POST error {response.status_code}: {response.text}")
+            return None, None
+    except requests.exceptions.ConnectTimeout:
+        print(f"Connection timeout (>10s)")
+        if retry_count >= max_retries:
+            raise Exception("Connection timeout")
+        return None, None
+    except requests.exceptions.ReadTimeout:
+        print(f"Read timeout (>30s)")
+        if retry_count >= max_retries:
+            raise Exception("Read timeout")
+        return None, None
+    except Exception as e:
+        print(f"Unexpected error during API call: {str(e)}")
+        if retry_count >= max_retries:
+            raise e
+        return None, None
+
+def get_latest_checkpoint(checkpoint_dir):
+    """Get the latest checkpoint file and its processed count"""
+    checkpoint_files = glob.glob(os.path.join(checkpoint_dir, "checkpoint_*.json"))
+    if not checkpoint_files:
+        return None, 0
+    
+    latest_checkpoint = None
+    max_count = 0
+    for checkpoint in checkpoint_files:
+        try:
+            count = int(os.path.basename(checkpoint).split('_')[1])
+            if count > max_count:
+                max_count = count
+                latest_checkpoint = checkpoint
+        except (ValueError, IndexError):
+            continue
+    
+    return latest_checkpoint, max_count
+
+def save_checkpoint(results, processed_count, checkpoint_dir):
+    """Save results to a checkpoint file"""
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    checkpoint_file = os.path.join(checkpoint_dir, f"checkpoint_{processed_count}_{timestamp}.json")
+    with open(checkpoint_file, "w", encoding="utf-8") as f:
+        json.dump(results, f, indent=2, ensure_ascii=False)
+    print(f"Checkpoint saved: {checkpoint_file}")
+
+def main():
+    args = parse_args()
+    
+    # Initialize results storage
+    results = []
+    save_file_name = args.output_file
+    error_file_name = args.error_file
+    
+    # Create checkpoints directory
+    checkpoint_dir = args.checkpoint_dir
+    if not os.path.exists(checkpoint_dir):
+        os.makedirs(checkpoint_dir)
+    
+    # Load test data
+    all_data_file = args.input_file
+    with open(all_data_file, 'r', encoding='utf-8') as f:
+        all_data = json.load(f)
+    
+    # Initialize error tracking
+    error_results = []
+    
+    # Load checkpoint if exists
+    latest_checkpoint, checkpoint_count = get_latest_checkpoint(checkpoint_dir)
+    if latest_checkpoint:
+        print(f"Found latest checkpoint with {checkpoint_count} processed items: {latest_checkpoint}")
+        try:
+            with open(latest_checkpoint, 'r', encoding='utf-8') as f:
+                results = json.load(f)
+                print(f"Resumed from checkpoint: processed {len(results)} items")
+        except Exception as e:
+            print(f"Warning: Failed to load checkpoint {latest_checkpoint}: {e}")
+            results = []
+    else:
+        print("No checkpoint found, starting from scratch")
+        results = []
+    
+    max_prediction_retries = args.max_retries
+    total_count = 0
+    
+    for item in tqdm(all_data, desc="Processing texts"):
+        key = item.get('key')
+        text_input = item.get('model_output')
+        
+        if not text_input:
+            print(f"No text input found for key {key}, skipping...")
+            continue
+        
+        print(f"Processing text for key={key}")
+        
+        prediction_retry_count = 0
+        success = False
+        
+        while prediction_retry_count < max_prediction_retries and not success:
+            try:
+                print(f"\nProcessing attempt {prediction_retry_count + 1}")
+                model_output, pred_score = make_api_call(text_input)
+                
+                if model_output is None or pred_score is None:
+                    print("API call failed, retrying...")
+                    prediction_retry_count += 1
+                    continue
+                    
+                print(f"Received prediction: {pred_score}")
+                
+                if pred_score == 1:
+                    success = True
+                    print("Prediction score is 1, accepting result")
+                else:
+                    prediction_retry_count += 1
+                    print(f"Prediction score is not 1 (attempt {prediction_retry_count}/{max_prediction_retries})")
+                    if prediction_retry_count >= max_prediction_retries:
+                        print("Max retries reached, accepting last prediction")
+                        success = True
+                    else:
+                        continue
+                
+                results.append({
+                    "key": key,
+                    "text_input": text_input,
+                    "model_output": model_output,
+                    "predicted_score": pred_score,
+                    "prediction_attempts": prediction_retry_count + 1
+                })
+                
+                with open(save_file_name, "w", encoding="utf-8") as f:
+                    json.dump(results, f, indent=2, ensure_ascii=False)
+                    
+                total_count += 1
+                
+                if total_count % args.checkpoint_interval == 0:
+                    save_checkpoint(results, total_count, checkpoint_dir)
+                
+            except Exception as e:
+                error_msg = str(e)
+                print(f"Failed to process text for key {key}: {error_msg}")
+                error_results.append({
+                    "key": key,
+                    "text_input": text_input,
+                    "error": f"Exception: {error_msg}"
+                })
+                break
+                
+        with open(error_file_name, "w", encoding="utf-8") as f:
+            json.dump(error_results, f, indent=2, ensure_ascii=False)
+    
+    # Save final results
+    with open(save_file_name, "w", encoding="utf-8") as f:
+        json.dump(results, f, indent=2, ensure_ascii=False)
+    
+    print(f"Results saved to {save_file_name}")
+    print(f"Total processed items: {total_count}")
+
+if __name__ == "__main__":
+    main()