56 lines
2.2 KiB
Python
Executable File
56 lines
2.2 KiB
Python
Executable File
import os
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import json
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import torch
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from tqdm.auto import tqdm
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from pathlib import Path
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from vall_e.emb.g2p import encode as valle_phonemize
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from vall_e.emb.qnt import encode_from_file as valle_quantize, _replace_file_extension
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input_dataset = "LibriTTS_R"
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output_dataset = "LibriTTS-Train"
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device = "cuda"
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txts = []
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wavs = []
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for dataset_name in os.listdir(f'./{input_dataset}/'):
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if not os.path.isdir(f'./{input_dataset}/{dataset_name}/'):
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continue
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for speaker_id in tqdm(os.listdir(f'./{input_dataset}/{dataset_name}/'), desc="Processing speaker"):
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if not os.path.isdir(f'./{input_dataset}/{dataset_name}/{speaker_id}'):
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continue
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os.makedirs(f'./{output_dataset}/{speaker_id}/', exist_ok=True)
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for book_id in os.listdir(f'./{input_dataset}/{dataset_name}/{speaker_id}'):
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if not os.path.isdir(f'./{input_dataset}/{dataset_name}/{speaker_id}/{book_id}'):
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continue
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for filename in os.listdir(f'./{input_dataset}/{dataset_name}/{speaker_id}/{book_id}'):
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# os.rename(f'./{input_dataset}/{dataset_name}/{speaker_id}/{book_id}/{filename}', f'./{output_dataset}/{speaker_id}/{filename}')
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inpath = Path(f'./{input_dataset}/{dataset_name}/{speaker_id}/{book_id}/{filename}')
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outpath = Path(f'./{output_dataset}/{speaker_id}/{filename}')
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if ".original.txt" in filename and not _replace_file_extension(outpath, ".json").exists():
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txts.append([inpath, outpath])
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if ".wav" in filename and not _replace_file_extension(outpath, ".dac").exists():
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wavs.append([inpath, outpath])
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for paths in tqdm(txts, desc="Phonemizing..."):
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text = open(paths[0], "r", encoding="utf-8").read()
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phones = valle_phonemize(text)
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data = {
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"text": text,
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"phonemes": phones,
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"language": "english",
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}
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open(_replace_file_extension(paths[1], ".json"), 'w', encoding='utf-8').write(json.dumps(data))
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#phones = valle_phonemize(open(paths[0], "r", encoding="utf-8").read())
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#open(_replace_file_extension(paths[1], ".phn.txt"), "w", encoding="utf-8").write(" ".join(phones))
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for paths in tqdm(wavs, desc="Quantizing..."):
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qnt = valle_quantize(paths[0], device=device)
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qnt.save(_replace_file_extension(paths[1], ".dac"))
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#torch.save(qnt.cpu(), _replace_file_extension(paths[1], ".qnt.pt"))
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