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import numpy as np
import torch
import glob
import os
import tqdm
import librosa
import parselmouth
from utils.commons.pitch_utils import f0_to_coarse
from utils.commons.multiprocess_utils import multiprocess_run_tqdm
def librosa_pad_lr(x, fsize, fshift, pad_sides=1):
'''compute right padding (final frame) or both sides padding (first and final frames)
'''
assert pad_sides in (1, 2)
# return int(fsize // 2)
pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0]
if pad_sides == 1:
return 0, pad
else:
return pad // 2, pad // 2 + pad % 2
def extract_mel_from_fname(wav_path,
fft_size=512,
hop_size=320,
win_length=512,
window="hann",
num_mels=80,
fmin=80,
fmax=7600,
eps=1e-6,
sample_rate=16000,
min_level_db=-100):
if isinstance(wav_path, str):
wav, _ = librosa.core.load(wav_path, sr=sample_rate)
else:
wav = wav_path
# get amplitude spectrogram
x_stft = librosa.stft(wav, n_fft=fft_size, hop_length=hop_size,
win_length=win_length, window=window, center=False)
spc = np.abs(x_stft) # (n_bins, T)
# get mel basis
fmin = 0 if fmin == -1 else fmin
fmax = sample_rate / 2 if fmax == -1 else fmax
mel_basis = librosa.filters.mel(sr=sample_rate, n_fft=fft_size, n_mels=num_mels, fmin=fmin, fmax=fmax)
mel = mel_basis @ spc
mel = np.log10(np.maximum(eps, mel)) # (n_mel_bins, T)
mel = mel.T
l_pad, r_pad = librosa_pad_lr(wav, fft_size, hop_size, 1)
wav = np.pad(wav, (l_pad, r_pad), mode='constant', constant_values=0.0)
return wav.T, mel
def extract_f0_from_wav_and_mel(wav, mel,
hop_size=320,
audio_sample_rate=16000,
):
time_step = hop_size / audio_sample_rate * 1000
f0_min = 80
f0_max = 750
f0 = parselmouth.Sound(wav, audio_sample_rate).to_pitch_ac(
time_step=time_step / 1000, voicing_threshold=0.6,
pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
delta_l = len(mel) - len(f0)
assert np.abs(delta_l) <= 8
if delta_l > 0:
f0 = np.concatenate([f0, [f0[-1]] * delta_l], 0)
f0 = f0[:len(mel)]
pitch_coarse = f0_to_coarse(f0)
return f0, pitch_coarse
def extract_mel_f0_from_fname(fname, out_name=None):
assert fname.endswith(".wav")
if out_name is None:
out_name = fname[:-4] + '_audio.npy'
wav, mel = extract_mel_from_fname(fname)
f0, f0_coarse = extract_f0_from_wav_and_mel(wav, mel)
out_dict = {
"mel": mel, # [T, 80]
"f0": f0,
}
np.save(out_name, out_dict)
return True
if __name__ == '__main__':
import os, glob
lrs3_dir = "/home/yezhenhui/datasets/raw/lrs3_raw"
wav_name_pattern = os.path.join(lrs3_dir, "*/*.wav")
wav_names = glob.glob(wav_name_pattern)
wav_names = sorted(wav_names)
for _ in multiprocess_run_tqdm(extract_mel_f0_from_fname, args=wav_names, num_workers=32,desc='extracting Mel and f0'):
pass |