mirror of
https://github.com/Nighthawk42/soprano-factory.git
synced 2026-08-30 04:30:21 +00:00
91 lines
2.6 KiB
Python
91 lines
2.6 KiB
Python
"""
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Converts a dataset in LJSpeech format into audio tokens that can be used to train/fine-tune Soprano.
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This script creates two JSON files for train and test splits in the provided directory.
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Usage:
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python generate_dataset.py --input-dir path/to/files
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Args:
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--input-dir: Path to directory of LJSpeech-style dataset. If none is provided this defaults to the provided example dataset.
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"""
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import argparse
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import pathlib
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import random
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import json
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from scipy.io import wavfile
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import torchaudio
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import torch
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from tqdm import tqdm
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from huggingface_hub import hf_hub_download
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from encoder.codec import Encoder
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SAMPLE_RATE = 32000
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SEED = 42
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VAL_PROP = 0.1
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VAL_MAX = 512
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument("--input-dir",
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required=False,
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default="./example_dataset",
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type=pathlib.Path
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)
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return parser.parse_args()
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def main():
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args = get_args()
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input_dir = args.input_dir
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print("Loading model.")
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encoder = Encoder()
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encoder_path = hf_hub_download(repo_id='ekwek/Soprano-Encoder', filename='encoder.pth')
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encoder.load_state_dict(torch.load(encoder_path))
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print("Model loaded.")
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print("Reading metadata.")
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files = []
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with open(f'{input_dir}/metadata.txt', encoding='utf-8') as f:
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data = f.read().split('\n')
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for line in data:
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filename, transcript = line.split('|', maxsplit=1)
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files.append((filename, transcript))
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print(f'{len(files)} samples located in directory.')
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print("Encoding audio.")
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dataset = []
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for sample in tqdm(files):
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filename, transcript = sample
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sr, audio = wavfile.read(f'{input_dir}/wavs/{filename}.wav')
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audio = torch.from_numpy(audio)
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if sr != SAMPLE_RATE:
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audio = torchaudio.functional.resample(audio, sr, SAMPLE_RATE)
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audio = audio.unsqueeze(0)
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with torch.no_grad():
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audio_tokens = encoder(audio)
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dataset.append([transcript, audio_tokens.squeeze(0).tolist()])
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print("Generating train/test splits.")
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random.seed(SEED)
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random.shuffle(dataset)
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num_val = min(int(VAL_PROP * len(dataset)) + 1, VAL_MAX)
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train_dataset = dataset[num_val:]
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val_dataset = dataset[:num_val]
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print(f'# train samples: {len(train_dataset)}')
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print(f'# val samples: {len(val_dataset)}')
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print("Saving datasets.")
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with open(f'{input_dir}/train.json', 'w', encoding='utf-8') as f:
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json.dump(train_dataset, f, indent=2)
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with open(f'{input_dir}/val.json', 'w', encoding='utf-8') as f:
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json.dump(val_dataset, f, indent=2)
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print("Datasets saved.")
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if __name__ == '__main__':
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main()
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