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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
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 = ap.mulaw_encode(y, qc=config.mode) if config.model_args.mulaw else ap.quantize(y, 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:]
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