76 lines
2.5 KiB
Python
76 lines
2.5 KiB
Python
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import glob
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import os
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from pathlib import Path
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import numpy as np
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from coqpit import Coqpit
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from tqdm import tqdm
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from TTS.utils.audio import AudioProcessor
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from TTS.utils.audio.numpy_transforms import mulaw_encode, quantize
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def preprocess_wav_files(out_path: str, config: Coqpit, ap: AudioProcessor):
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"""Process wav and compute mel and quantized wave signal.
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It is mainly used by WaveRNN dataloader.
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Args:
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out_path (str): Parent folder path to save the files.
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config (Coqpit): Model config.
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ap (AudioProcessor): Audio processor.
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"""
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os.makedirs(os.path.join(out_path, "quant"), exist_ok=True)
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os.makedirs(os.path.join(out_path, "mel"), exist_ok=True)
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wav_files = find_wav_files(config.data_path)
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for path in tqdm(wav_files):
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wav_name = Path(path).stem
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quant_path = os.path.join(out_path, "quant", wav_name + ".npy")
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mel_path = os.path.join(out_path, "mel", wav_name + ".npy")
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y = ap.load_wav(path)
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mel = ap.melspectrogram(y)
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np.save(mel_path, mel)
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if isinstance(config.mode, int):
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quant = (
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mulaw_encode(wav=y, mulaw_qc=config.mode)
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if config.model_args.mulaw
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else quantize(x=y, quantize_bits=config.mode)
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)
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np.save(quant_path, quant)
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def find_wav_files(data_path, file_ext="wav"):
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wav_paths = glob.glob(os.path.join(data_path, "**", f"*.{file_ext}"), recursive=True)
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return wav_paths
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def find_feat_files(data_path):
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feat_paths = glob.glob(os.path.join(data_path, "**", "*.npy"), recursive=True)
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return feat_paths
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def load_wav_data(data_path, eval_split_size, file_ext="wav"):
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wav_paths = find_wav_files(data_path, file_ext=file_ext)
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assert len(wav_paths) > 0, f" [!] {data_path} is empty."
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np.random.seed(0)
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np.random.shuffle(wav_paths)
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return wav_paths[:eval_split_size], wav_paths[eval_split_size:]
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def load_wav_feat_data(data_path, feat_path, eval_split_size):
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wav_paths = find_wav_files(data_path)
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feat_paths = find_feat_files(feat_path)
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wav_paths.sort(key=lambda x: Path(x).stem)
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feat_paths.sort(key=lambda x: Path(x).stem)
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assert len(wav_paths) == len(feat_paths), f" [!] {len(wav_paths)} vs {feat_paths}"
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for wav, feat in zip(wav_paths, feat_paths):
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wav_name = Path(wav).stem
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feat_name = Path(feat).stem
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assert wav_name == feat_name
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items = list(zip(wav_paths, feat_paths))
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np.random.seed(0)
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np.random.shuffle(items)
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return items[:eval_split_size], items[eval_split_size:]
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