forked from isletennos/MMVC_Trainer
-
Notifications
You must be signed in to change notification settings - Fork 0
/
mel_processing.py
123 lines (93 loc) · 4.38 KB
/
mel_processing.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
import math
import os
import random
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.data
import numpy as np
import librosa
import librosa.util as librosa_util
from librosa.util import normalize, pad_center, tiny
from scipy.signal import get_window
from scipy.io.wavfile import read
from librosa.filters import mel as librosa_mel_fn
MAX_WAV_VALUE = 32768.0
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
"""
PARAMS
------
C: compression factor
"""
return torch.log(torch.clamp(x, min=clip_val) * C)
def dynamic_range_decompression_torch(x, C=1):
"""
PARAMS
------
C: compression factor used to compress
"""
return torch.exp(x) / C
def spectral_normalize_torch(magnitudes):
output = dynamic_range_compression_torch(magnitudes)
return output
def spectral_de_normalize_torch(magnitudes):
output = dynamic_range_decompression_torch(magnitudes)
return output
mel_basis = {}
hann_window = {}
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
if torch.min(y) < -1.:
print('min value is ', torch.min(y))
if torch.max(y) > 1.:
print('max value is ', torch.max(y))
global hann_window
dtype_device = str(y.dtype) + '_' + str(y.device)
wnsize_dtype_device = str(win_size) + '_' + dtype_device
if wnsize_dtype_device not in hann_window:
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
y = y.squeeze(1)
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)
spec = torch.view_as_real(spec)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
return spec
def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
global mel_basis
dtype_device = str(spec.dtype) + '_' + str(spec.device)
fmax_dtype_device = str(fmax) + '_' + dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
spec = spectral_normalize_torch(spec)
return spec
def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
if torch.min(y) < -1.:
print('min value is ', torch.min(y))
if torch.max(y) > 1.:
print('max value is ', torch.max(y))
global mel_basis, hann_window
dtype_device = str(y.dtype) + '_' + str(y.device)
fmax_dtype_device = str(fmax) + '_' + dtype_device
wnsize_dtype_device = str(win_size) + '_' + dtype_device
if fmax_dtype_device not in mel_basis:
mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
if wnsize_dtype_device not in hann_window:
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
y = y.squeeze(1)
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)
spec = torch.view_as_real(spec)
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
spec = spectral_normalize_torch(spec)
return spec
def spectrogram_torch_data(y, data):
return spectrogram_torch(y, data.filter_length, data.sampling_rate, data.hop_length, data.win_length, center=False)
def spec_to_mel_torch_data(spec, data):
return spec_to_mel_torch(spec, data.filter_length, data.n_mel_channels, data.sampling_rate, data.mel_fmin, data.mel_fmax)
def mel_spectrogram_torch_data(y, data):
return mel_spectrogram_torch(y, data.filter_length, data.n_mel_channels, data.sampling_rate, data.hop_length, data.win_length, data.mel_fmin, data.mel_fmax, center=False)