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nvidia_preprocessing.py
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nvidia_preprocessing.py
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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.util import read_wav_np
from dataset.audio_processing import pitch
from utils.hparams import HParam
def main(args, hp):
stft = TacotronSTFT(
filter_length=hp.audio.n_fft,
hop_length=hp.audio.hop_length,
win_length=hp.audio.win_length,
n_mel_channels=hp.audio.n_mels,
sampling_rate=hp.audio.sample_rate,
mel_fmin=hp.audio.fmin,
mel_fmax=hp.audio.fmax,
)
wav_files = glob.glob(os.path.join(args.data_path, "**", "*.wav"), recursive=True)
mel_path = os.path.join(hp.data.data_dir, "mels")
energy_path = os.path.join(hp.data.data_dir, "energy")
pitch_path = os.path.join(hp.data.data_dir, "pitch")
os.makedirs(mel_path, exist_ok=True)
os.makedirs(energy_path, exist_ok=True)
os.makedirs(pitch_path, exist_ok=True)
print("Sample Rate : ", hp.audio.sample_rate)
for wavpath in tqdm.tqdm(wav_files, desc="preprocess wav to mel"):
sr, wav = read_wav_np(wavpath, hp.audio.sample_rate)
p = pitch(wav, hp) # [T, ] T = Number of frames
wav = torch.from_numpy(wav).unsqueeze(0)
mel, mag = stft.mel_spectrogram(wav) # mel [1, 80, T] mag [1, num_mag, T]
mel = mel.squeeze(0) # [num_mel, T]
mag = mag.squeeze(0) # [num_mag, T]
e = torch.norm(mag, dim=0) # [T, ]
p = p[: mel.shape[1]]
id = os.path.basename(wavpath).split(".")[0]
np.save("{}/{}.npy".format(mel_path, id), mel.numpy(), allow_pickle=False)
np.save("{}/{}.npy".format(energy_path, id), e.numpy(), allow_pickle=False)
np.save("{}/{}.npy".format(pitch_path, id), p, allow_pickle=False)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"-d", "--data_path", type=str, required=True, help="root directory of wav files"
)
parser.add_argument(
"-c", "--config", type=str, required=True, help="yaml file for configuration"
)
args = parser.parse_args()
hp = HParam(args.config)
main(args, hp)