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import os
import glob
import tqdm
import torch
import argparse
import numpy as np
from utils.stft import TacotronSTFT
from utils.hparams import HParam
from utils.utils import read_wav_np
def main(hp, args):
stft = TacotronSTFT(filter_length=hp.audio.filter_length,
hop_length=hp.audio.hop_length,
win_length=hp.audio.win_length,
n_mel_channels=hp.audio.n_mel_channels,
sampling_rate=hp.audio.sampling_rate,
mel_fmin=hp.audio.mel_fmin,
mel_fmax=hp.audio.mel_fmax)
wav_files = glob.glob(os.path.join(args.data_path, '**', '*.wav'), recursive=True)
for wavpath in tqdm.tqdm(wav_files, desc='preprocess wav to mel'):
sr, wav = read_wav_np(wavpath)
assert sr == hp.audio.sampling_rate, \
"sample rate mismatch. expected %d, got %d at %s" % \
(hp.audio.sampling_rate, sr, wavpath)
if len(wav) < hp.audio.segment_length + hp.audio.pad_short:
wav = np.pad(wav, (0, hp.audio.segment_length + hp.audio.pad_short - len(wav)), \
mode='constant', constant_values=0.0)
wav = torch.from_numpy(wav).unsqueeze(0)
mel = stft.mel_spectrogram(wav)
melpath = wavpath.replace('.wav', '.mel')
torch.save(mel, melpath)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-c', '--config', type=str, required=True,
help="yaml file for config.")
parser.add_argument('-d', '--data_path', type=str, required=True,
help="root directory of wav files")
args = parser.parse_args()
hp = HParam(args.config)
main(hp, args)
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