Datasets:
File size: 3,627 Bytes
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import os
import soundfile as sf
from tqdm import tqdm
def create_hf_metafile(dataset_type, base_data_path, output_meta_path):
subset_path = os.path.join(base_data_path, dataset_type) # Path to the train or test directory
prompts_file = os.path.join(subset_path, "prompts.txt")
waves_base_dir = os.path.join(subset_path, "waves")
if not os.path.exists(prompts_file):
print(f"Error: Cannot find file {prompts_file}")
return
if not os.path.exists(waves_base_dir):
print(f"Error: Cannot find directory {waves_base_dir}")
return
print(f"Processing {dataset_type}...")
# Read prompts.txt
prompt_data = {}
with open(prompts_file, "r", encoding="utf-8") as pf:
for line in pf:
try:
parts = line.strip().split(" ", 1)
if len(parts) == 2:
file_id, transcription = parts
prompt_data[file_id] = transcription.upper() # Convert to uppercase for consistency
except ValueError:
print(f"Ignoring line with incorrect format in {prompts_file}: {line.strip()}")
continue
with open(output_meta_path, "w", encoding="utf-8") as meta_f:
# Iterate through speaker directories in waves_base_dir
for speaker_dir in tqdm(os.listdir(waves_base_dir)):
speaker_path = os.path.join(waves_base_dir, speaker_dir)
if os.path.isdir(speaker_path):
for wav_filename in os.listdir(speaker_path):
if wav_filename.endswith(".wav"):
file_id_without_ext = os.path.splitext(wav_filename)[0]
if file_id_without_ext in prompt_data:
transcription = prompt_data[file_id_without_ext]
full_wav_path = os.path.join(speaker_path, wav_filename)
try:
frames = sf.SoundFile(full_wav_path).frames
samplerate = sf.SoundFile(full_wav_path).samplerate
duration = frames / samplerate
except Exception as e:
print(f"Error reading file {full_wav_path}: {e}. Skipping.")
continue
# Create relative path for Hugging Face Hub
# Example: train/waves/SPEAKER01/SPEAKER01_001.wav
relative_path = os.path.join(dataset_type, "waves", speaker_dir, wav_filename).replace(os.sep, '/')
meta_f.write(f"{relative_path}|{transcription}|{duration:.4f}\n")
# else:
# print(f"No transcription found for {file_id_without_ext} in {dataset_type}")
print(f"Meta file created: {output_meta_path}")
if __name__ == "__main__":
current_script_dir = os.path.dirname(os.path.abspath(__file__)) # Directory containing this script
# Generate meta file for training set
create_hf_metafile(
dataset_type="train",
base_data_path=current_script_dir, # Root directory of the dataset is the current directory
output_meta_path=os.path.join(current_script_dir, "train_meta.txt")
)
# Generate meta file for test set
create_hf_metafile(
dataset_type="test",
base_data_path=current_script_dir, # Root directory of the dataset is the current directory
output_meta_path=os.path.join(current_script_dir, "test_meta.txt")
)
print("Meta file creation completed.")
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