# vivos_asr_vi.py import datasets import os import logging # Định cấu hình logging cơ bản để xem thông tin từ thư viện datasets logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s") # --- METADATA --- _ORIGINAL_VIVOS_CITATION = """\ @inproceedings{vivos-dataset-2017, title = {VIVOS - A Vietnamese Voice Corpus for Speech Synthesis and Speech Recognition}, author = {Nguyen, Dat Quoc and Nguyen, Bach Xuan and Do, Luan Thanh and Nguyen, Chi Mai and Pham, Hung Duy and Nguyen, Tuan Anh}, booktitle = {Proceedings of the 8th International Conference on Language and Automata Theory and Applications (LATA 2017)}, year = {2017}, pages = {87--96}, publisher = {Springer International Publishing}, url = {https://link.springer.com/chapter/10.1007/978-3-319-53733-7_8} } """ _MCP_CLOUDWORDS_PROCESSED_CITATION = """\ @misc{mcp_cloudwords_vivos_processed_2025, author = {MCP Cloudwords}, title = {MCP Cloudwords Processed Version of the VIVOS ASR Dataset for Vietnamese}, year = {2025}, howpublished = {Dataset available on Hugging Face Hub at [TODO: YOUR_USERNAME/YOUR_DATASET_NAME_ON_HUB]} } """ _DESCRIPTION = """\ This dataset is a processed version of the VIVOS ASR Vietnamese speech corpus (original source: https://ailab.hcmus.edu.vn/vivos), prepared by MCP Cloudwords for use in Automatic Speech Recognition (ASR) tasks. The VIVOS corpus is a valuable public resource for Vietnamese speech processing. This MCP Cloudwords version aims to make the VIVOS data more readily usable by providing: - Original audio files in WAV format, organized into 'train' and 'test' splits. - Corresponding transcriptions. - Metadata files ('train_meta.txt', 'test_meta.txt') mapping audio files to transcriptions and durations. These files use relative paths to the audio files. Key processing steps by MCP Cloudwords: - Generation of 'train_meta.txt' and 'test_meta.txt' from original VIVOS 'prompts.txt'. - Calculation and inclusion of audio durations. - Conversion of transcriptions to uppercase. Users should cite the original VIVOS dataset and acknowledge MCP Cloudwords' processing. """ _HOMEPAGE = "Original VIVOS: https://ailab.hcmus.edu.vn/vivos\nProcessed by MCP Cloudwords: [TODO: YOUR_DATASET_URL_ON_HUB]" _LICENSE = "CC BY-NC-SA 4.0 (Please verify with original VIVOS license)" # --- DATASET SCRIPT --- class VivosASRVi(datasets.GeneratorBasedBuilder): # Đổi tên lớp lại thành VivosASRVi cho đơn giản """VIVOS Vietnamese ASR Dataset, processed by MCP Cloudwords.""" VERSION = datasets.Version("1.0.0") # Định nghĩa config MẶC ĐỊNH và DUY NHẤT cho dataset này # Tên của config này sẽ được sử dụng khi load_dataset mà không chỉ định 'name' # Hoặc khi bạn chỉ định name="default" (hoặc tên bạn đặt ở đây) BUILDER_CONFIGS = [ datasets.BuilderConfig( name="default", # Đặt tên config là "default" để đơn giản hóa version=VERSION, description="Processed version of VIVOS ASR dataset by MCP Cloudwords." ) ] # DEFAULT_CONFIG_NAME không cần thiết nếu bạn chỉ có một config và đặt tên nó là "default" # Tuy nhiên, để rõ ràng, bạn có thể đặt: DEFAULT_CONFIG_NAME = "default" def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "audio": datasets.Audio(sampling_rate=16000), "transcription": datasets.Value("string"), "duration": datasets.Value("float32"), "speaker_id": datasets.Value("string"), "file_id": datasets.Value("string"), } ), supervised_keys=("audio", "transcription"), homepage=_HOMEPAGE, license=_LICENSE, citation=f"{_ORIGINAL_VIVOS_CITATION}\n\n{_MCP_CLOUDWORDS_PROCESSED_CITATION}", ) def _split_generators(self, dl_manager): base_path = dl_manager.manual_dir or "." logging.info(f"Using base_path for splits: {os.path.abspath(base_path)}") train_meta = os.path.join(base_path, "train_meta.txt") test_meta = os.path.join(base_path, "test_meta.txt") if not os.path.exists(train_meta): raise FileNotFoundError( f"Required metadata file 'train_meta.txt' not found at {os.path.abspath(train_meta)}") if not os.path.exists(test_meta): raise FileNotFoundError(f"Required metadata file 'test_meta.txt' not found at {os.path.abspath(test_meta)}") return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"meta_filepath": train_meta, "base_data_path": base_path}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"meta_filepath": test_meta, "base_data_path": base_path}, ), ] def _generate_examples(self, meta_filepath, base_data_path): logging.info(f"Generating examples from: {meta_filepath}") processed_count = 0 error_count = 0 with open(meta_filepath, encoding="utf-8") as f: for uid, line in enumerate(f): try: rel_path, transcription, duration_str = line.strip().split("|", 2) duration = float(duration_str) abs_path = os.path.join(base_data_path, rel_path) if not os.path.exists(abs_path): logging.warning(f"Audio file not found: {abs_path}. Skipping line: {line.strip()}") error_count += 1 continue path_parts = rel_path.split('/') speaker_id = path_parts[-2] if len(path_parts) >= 2 else "unknown_speaker" file_id = os.path.splitext(path_parts[-1])[0] yield uid, { "audio": abs_path, "transcription": transcription, "duration": duration, "speaker_id": speaker_id, "file_id": file_id, } processed_count += 1 except ValueError as e: logging.error(f"Invalid format in line: {line.strip()}. Error: {e}. Skipping.") error_count += 1 except Exception as e: logging.error(f"Unexpected error processing line: {line.strip()}. Error: {e}. Skipping.") error_count += 1 logging.info( f"Finished generating examples from {meta_filepath}. Processed: {processed_count}, Errors/Skipped: {error_count}") # Đoạn kiểm tra cục bộ if __name__ == "__main__": current_dataset_dir = os.path.dirname(os.path.abspath(__file__)) print(f"Loading dataset from: {current_dataset_dir}") print("Ensure 'train_meta.txt' and 'test_meta.txt' exist in this directory, and audio files are correctly pathed.") # Khi chỉ có một BuilderConfig tên là "default", bạn không cần chỉ định `name` # hoặc có thể chỉ định name="default" config_to_load = "default" # HOẶC bạn có thể bỏ qua tham số name hoàn toàn trong load_dataset nếu DEFAULT_CONFIG_NAME là "default" # và chỉ có một config. # config_to_load = None # Thử bỏ trống để dùng default print(f"--- Attempting to load with config_name: '{config_to_load if config_to_load else 'implicit default'}' ---") for split in ["train", "test"]: try: print( f"\nAttempting to load '{split}' split with config '{config_to_load if config_to_load else 'implicit default'}'...") dataset_params = { "path": current_dataset_dir, "split": split, "trust_remote_code": True } if config_to_load: # Chỉ thêm 'name' nếu nó không phải None dataset_params["name"] = config_to_load dataset = datasets.load_dataset(**dataset_params) print(f"SUCCESS: Loaded '{split}' split successfully!") print(f"Number of samples in '{split}': {len(dataset)}") if len(dataset) > 0: print(f"First sample in '{split}':") print(dataset[0]) except FileNotFoundError as e: print(f"ERROR: FileNotFoundError while loading '{split}': {e}") except Exception as e: print( f"ERROR: An unexpected error occurred loading '{split}' with config '{config_to_load if config_to_load else 'implicit default'}': {e}") import traceback traceback.print_exc()