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