forked from flywangsir/wav2lip_288x288_test
-
Notifications
You must be signed in to change notification settings - Fork 0
/
audio.py
138 lines (114 loc) · 4.54 KB
/
audio.py
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
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
import librosa
import librosa.filters
import numpy as np
# import tensorflow as tf
from scipy import signal
from scipy.io import wavfile
from hparams import hparams as hp
def load_wav(path, sr):
return librosa.core.load(path, sr=sr)[0]
def save_wav(wav, path, sr):
wav *= 32767 / max(0.01, np.max(np.abs(wav)))
#proposed by @dsmiller
wavfile.write(path, sr, wav.astype(np.int16))
def save_wavenet_wav(wav, path, sr):
librosa.output.write_wav(path, wav, sr=sr)
def preemphasis(wav, k, preemphasize=True):
if preemphasize:
return signal.lfilter([1, -k], [1], wav)
return wav
def inv_preemphasis(wav, k, inv_preemphasize=True):
if inv_preemphasize:
return signal.lfilter([1], [1, -k], wav)
return wav
def get_hop_size():
hop_size = hp.hop_size
if hop_size is None:
assert hp.frame_shift_ms is not None
hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate)
return hop_size
def linearspectrogram(wav):
D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
S = _amp_to_db(np.abs(D)) - hp.ref_level_db
if hp.signal_normalization:
return _normalize(S)
return S
def melspectrogram(wav):
D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize))
S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db
if hp.signal_normalization:
return _normalize(S)
return S
def _lws_processor():
import lws
return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech")
def _stft(y):
if hp.use_lws:
return _lws_processor(hp).stft(y).T
else:
return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size)
##########################################################
#Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!)
def num_frames(length, fsize, fshift):
"""Compute number of time frames of spectrogram
"""
pad = (fsize - fshift)
if length % fshift == 0:
M = (length + pad * 2 - fsize) // fshift + 1
else:
M = (length + pad * 2 - fsize) // fshift + 2
return M
def pad_lr(x, fsize, fshift):
"""Compute left and right padding
"""
M = num_frames(len(x), fsize, fshift)
pad = (fsize - fshift)
T = len(x) + 2 * pad
r = (M - 1) * fshift + fsize - T
return pad, pad + r
##########################################################
#Librosa correct padding
def librosa_pad_lr(x, fsize, fshift):
return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0]
# Conversions
_mel_basis = None
def _linear_to_mel(spectogram):
global _mel_basis
if _mel_basis is None:
_mel_basis = _build_mel_basis()
return np.dot(_mel_basis, spectogram)
def _build_mel_basis():
assert hp.fmax <= hp.sample_rate // 2
#return librosa.filters.mel(hp.sample_rate, hp.n_fft, n_mels=hp.num_mels,
# fmin=hp.fmin, fmax=hp.fmax)
return librosa.filters.mel(sr=hp.sample_rate, n_fft=hp.n_fft, n_mels=hp.num_mels,
fmin=hp.fmin, fmax=hp.fmax)
def _amp_to_db(x):
min_level = np.exp(hp.min_level_db / 20 * np.log(10))
return 20 * np.log10(np.maximum(min_level, x))
def _db_to_amp(x):
return np.power(10.0, (x) * 0.05)
def _normalize(S):
if hp.allow_clipping_in_normalization:
if hp.symmetric_mels:
return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value,
-hp.max_abs_value, hp.max_abs_value)
else:
return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value)
assert S.max() <= 0 and S.min() - hp.min_level_db >= 0
if hp.symmetric_mels:
return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value
else:
return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db))
def _denormalize(D):
if hp.allow_clipping_in_normalization:
if hp.symmetric_mels:
return (((np.clip(D, -hp.max_abs_value,
hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value))
+ hp.min_level_db)
else:
return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)
if hp.symmetric_mels:
return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db)
else:
return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db)