Spaces:
Configuration error
Configuration error
File size: 3,144 Bytes
aa5ee46 f0d8178 aa5ee46 f0d8178 aa5ee46 f0d8178 aa5ee46 f0d8178 aa5ee46 f0d8178 aa5ee46 f0d8178 ea0a289 f0d8178 aa5ee46 f0d8178 aa5ee46 f0d8178 aa5ee46 f0d8178 aa5ee46 f0d8178 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 |
import librosa
import torch
import numpy as np
from scipy.io import wavfile
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
warnings.filterwarnings("ignore", category=FutureWarning)
audio_opts = {
'sample_rate': 16000,
'n_fft': 512,
'win_length': 320,
'hop_length': 160,
'n_mel': 80,
}
def load_wav(path, fr=0, to=10000, sample_rate=16000):
"""Loads Audio wav from path at time indices given by fr, to (seconds)"""
_, wav = wavfile.read(path)
fr_aud = int(np.round(fr * sample_rate))
to_aud = int(np.round((to) * sample_rate))
wav = wav[fr_aud:to_aud]
return wav
def wav2filterbanks(wav, mel_basis=None):
"""
:param wav: Tensor b x T
"""
assert len(wav.shape) == 2, 'Need batch of wavs as input'
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device = 'cpu'
spect = torch.stft(wav,
return_complex=True,
n_fft=audio_opts['n_fft'],
hop_length=audio_opts['hop_length'],
win_length=audio_opts['win_length'],
window=torch.hann_window(audio_opts['win_length']).to(device),
center=True,
pad_mode='reflect',
normalized=False,
onesided=True) # b x F x T x 2
spect = torch.view_as_real(spect)
spect = spect[:, :, :-1, :]
# ----- Log filterbanks --------------
# mag spectrogram - # b x F x T
mag = power_spect = torch.norm(spect, dim=-1)
phase = torch.atan2(spect[..., 1], spect[..., 0])
if mel_basis is None:
# Build a Mel filter
mel_basis = torch.from_numpy(
librosa.filters.mel(audio_opts['sample_rate'],
audio_opts['n_fft'],
n_mels=audio_opts['n_mel'],
fmin=0,
fmax=int(audio_opts['sample_rate'] / 2)))
mel_basis = mel_basis.float().to(power_spect.device)
features = torch.log(torch.matmul(mel_basis, power_spect) +
1e-20) # b x F x T
features = features.permute([0, 2, 1]).contiguous() # b x T x F
# -------------------
# norm_axis = 1 # normalize every sample over time
# mean = features.mean(dim=norm_axis, keepdim=True) # b x 1 x F
# std_dev = features.std(dim=norm_axis, keepdim=True) # b x 1 x F
# features = (features - mean) / std_dev # b x T x F
return features, mag, phase, mel_basis
def torch_mag_phase_2_np_complex(mag_spect, phase):
complex_spect_2d = torch.stack(
[mag_spect * torch.cos(phase), mag_spect * torch.sin(phase)], -1)
complex_spect_np = complex_spect_2d.cpu().detach().numpy()
complex_spect_np = complex_spect_np[..., 0] + 1j * complex_spect_np[..., 1]
return complex_spect_np
def torch_mag_phase_2_complex_as_2d(mag_spect, phase):
complex_spect_2d = torch.stack(
[mag_spect * torch.cos(phase), mag_spect * torch.sin(phase)], -1)
return complex_spect_2d
def torch_phase_from_normalized_complex(spect):
phase = torch.atan2(spect[..., 1], spect[..., 0])
return phase
def reconstruct_wav_from_mag_phase(mag, phase):
spect = torch_mag_phase_2_np_complex(mag, phase)
wav = np.stack([
librosa.core.istft(spect[ii],
hop_length=audio_opts['hop_length'],
win_length=audio_opts['win_length'],
center=True) for ii in range(spect.shape[0])
])
return wav
|