108 lines
3.7 KiB
Python
Executable File
108 lines
3.7 KiB
Python
Executable File
import os
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import json
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import torch
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import numpy as np
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from tqdm.auto import tqdm
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from pathlib import Path
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from vall_e.config import cfg
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# things that could be args
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cfg.sample_rate = 24_000
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cfg.inference.audio_backend = "encodec"
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"""
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cfg.inference.weight_dtype = "bfloat16"
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cfg.inference.dtype = torch.bfloat16
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cfg.inference.amp = True
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"""
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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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audio_extension = ".dac" if cfg.inference.audio_backend == "dac" else ".enc"
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input_dataset = "LibriTTS_R"
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output_dataset = f"LibriTTS-Train-{'2' if cfg.sample_rate == 24_000 else '4'}4KHz"
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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 ".wav" in filename: # and not _replace_file_extension(outpath, ".dac").exists():
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txts.append((
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inpath,
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outpath
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))
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for paths in tqdm(txts, desc="Processing..."):
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inpath, outpath = paths
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try:
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if _replace_file_extension(outpath, ".dac").exists() and _replace_file_extension(outpath, ".json").exists():
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data = json.loads(open(_replace_file_extension(outpath, ".json"), 'r', encoding='utf-8').read())
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qnt = np.load(_replace_file_extension(outpath, audio_extension), allow_pickle=True)
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if not isinstance(data["phonemes"], str):
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data["phonemes"] = "".join(data["phonemes"])
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for k, v in data.items():
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qnt[()]['metadata'][k] = v
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np.save(open(_replace_file_extension(outpath, audio_extension), "wb"), qnt)
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else:
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text = open(_replace_file_extension(inpath, ".original.txt"), "r", encoding="utf-8").read()
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phones = valle_phonemize(text)
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qnt = valle_quantize(_replace_file_extension(inpath, ".wav"), device=device)
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if cfg.inference.audio_backend == "dac":
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np.save(open(_replace_file_extension(outpath, audio_extension), "wb"), {
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"codes": qnt.codes.cpu().numpy().astype(np.uint16),
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"metadata": {
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"original_length": qnt.original_length,
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"sample_rate": qnt.sample_rate,
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"input_db": qnt.input_db.cpu().numpy().astype(np.float32),
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"chunk_length": qnt.chunk_length,
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"channels": qnt.channels,
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"padding": qnt.padding,
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"dac_version": "1.0.0",
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"text": text.strip(),
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"phonemes": "".join(phones),
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"language": "en",
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},
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})
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else:
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np.save(open(_replace_file_extension(outpath, audio_extension), "wb"), {
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"codes": qnt.cpu().numpy().astype(np.uint16),
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"metadata": {
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"original_length": qnt.shape[0] / 75.0,
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"sample_rate": cfg.sample_rate,
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"text": text.strip(),
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"phonemes": "".join(phones),
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"language": "en",
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},
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})
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except Exception as e:
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tqdm.write(f"Failed to process: {paths}: {e}")
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