{{ message }}
Repository navigation
Expand file tree
/
Copy pathaudio.py
More file actions
213 lines (183 loc) · 9.09 KB
/
Copy pathaudio.py
File metadata and controls
213 lines (183 loc) · 9.09 KB
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
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
# Audio DSP adapted from TorchAudio (BSD-2-Clause).
# BSD 2-Clause License
#
# Copyright (c) 2017 Facebook Inc. (Soumith Chintala),
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import numpy as np
import scipy.signal
import torch
import torch.nn.functional as F
def resample(waveform, orig_freq, new_freq, lowpass_filter_width=6, rolloff=0.99, resampling_method="sinc_interp_hann", beta=None):
"""Bandlimited sinc resampling along the last dimension, on the input device."""
if orig_freq <= 0 or new_freq <= 0:
raise ValueError("Sample rates must be positive")
if orig_freq == new_freq:
return waveform
if int(orig_freq) != orig_freq or int(new_freq) != new_freq:
raise ValueError("Sample rates must be integers")
if not waveform.is_floating_point():
raise TypeError("Audio waveforms must be floating point")
if lowpass_filter_width <= 0:
raise ValueError("Lowpass filter width must be positive")
divisor = math.gcd(int(orig_freq), int(new_freq))
orig_freq = int(orig_freq) // divisor
new_freq = int(new_freq) // divisor
base_freq = min(orig_freq, new_freq) * rolloff
width = math.ceil(lowpass_filter_width * orig_freq / base_freq)
idx = torch.arange(-width, width + orig_freq, dtype=waveform.dtype, device=waveform.device)[None, None] / orig_freq
t = torch.arange(0, -new_freq, -1, dtype=waveform.dtype, device=waveform.device)[:, None, None] / new_freq + idx
t = (t * base_freq).clamp_(-lowpass_filter_width, lowpass_filter_width)
if resampling_method == "sinc_interp_hann":
window = torch.cos(t * math.pi / lowpass_filter_width / 2) ** 2
elif resampling_method == "sinc_interp_kaiser":
beta = torch.tensor(14.769656459379492 if beta is None else beta, device=waveform.device)
window = torch.i0(beta * torch.sqrt(1 - (t / lowpass_filter_width) ** 2)) / torch.i0(beta)
else:
raise ValueError(f"Unknown resampling method: {resampling_method}")
t *= math.pi
kernel = torch.where(t == 0, 1.0, t.sin() / t)
kernel *= window * (base_freq / orig_freq)
shape = waveform.shape
length = shape[-1]
waveform = waveform.reshape(-1, length)
waveform = F.pad(waveform, (width, width + orig_freq))
output = F.conv1d(waveform[:, None], kernel, stride=orig_freq)
output = output.transpose(1, 2).reshape(waveform.shape[0], -1)
# Match TorchAudio's float32 rounding to preserve existing output lengths.
target_length = math.ceil(np.float32(new_freq * length / orig_freq))
output = output[..., :target_length]
return output.reshape(*shape[:-1], output.shape[-1])
def _hz_to_mel(freq):
if freq >= 1000.0:
return 15.0 + math.log(freq / 1000.0) / (math.log(6.4) / 27.0)
return freq / (200.0 / 3)
class MelScale(torch.nn.Module):
"""Slaney mel filterbank with area normalization."""
def __init__(self, n_mels, sample_rate, f_min, f_max, n_stft):
super().__init__()
if f_max is None:
f_max = sample_rate // 2
all_freqs = torch.linspace(0, sample_rate // 2, n_stft)
mels = torch.linspace(_hz_to_mel(f_min), _hz_to_mel(f_max), n_mels + 2)
freqs = (200.0 / 3) * mels
log_region = mels >= 15.0
freqs[log_region] = 1000.0 * torch.exp((math.log(6.4) / 27.0) * (mels[log_region] - 15.0))
diff = freqs[1:] - freqs[:-1]
slopes = freqs.unsqueeze(0) - all_freqs.unsqueeze(1)
fb = torch.minimum(-slopes[:, :-2] / diff[:-1], slopes[:, 2:] / diff[1:]).clamp_min(0)
fb *= (2.0 / (freqs[2:] - freqs[:-2])).unsqueeze(0)
self.register_buffer("fb", fb)
def forward(self, spectrogram):
return (spectrogram.transpose(-1, -2) @ self.fb).transpose(-1, -2)
class _Spectrogram(torch.nn.Module):
def __init__(self, n_fft, win_length, hop_length, power):
super().__init__()
self.n_fft = n_fft
self.win_length = win_length
self.hop_length = hop_length
self.power = power
self.register_buffer("window", torch.hann_window(win_length))
def forward(self, waveform):
shape = waveform.shape
spec = torch.stft(waveform.reshape(-1, shape[-1]), self.n_fft, self.hop_length, self.win_length, self.window, center=True, pad_mode="reflect", normalized=False, onesided=True, return_complex=True)
spec = spec.reshape(*shape[:-1], *spec.shape[-2:]).abs()
return spec if self.power == 1.0 else spec.pow(self.power)
class MelSpectrogram(torch.nn.Module):
"""Hann-windowed magnitude/power spectrogram with Slaney mel normalization."""
def __init__(self, sample_rate, n_fft, hop_length, n_mels, f_min=0.0, f_max=None, win_length=None, power=2.0):
super().__init__()
self.spectrogram = _Spectrogram(n_fft, n_fft if win_length is None else win_length, hop_length, power)
self.mel_scale = MelScale(n_mels, sample_rate, f_min, f_max, n_fft // 2 + 1)
def forward(self, waveform):
return self.mel_scale(self.spectrogram(waveform))
class _LFilter(torch.autograd.Function):
@staticmethod
def forward(ctx, waveform, a, b):
ctx.a = a
ctx.b = b
# SciPy supplies the compiled IIR loop; audio nodes normally run on CPU.
output = scipy.signal.lfilter(b, a, waveform.detach().cpu().numpy())
return torch.from_numpy(output).to(waveform)
@staticmethod
def backward(ctx, grad):
return _LFilter.apply(grad.flip(-1), ctx.a, ctx.b).flip(-1), None, None
def _biquad(waveform, b0, b1, b2, a0, a1, a2):
a = torch.stack((a0, a1, a2)).detach().cpu().numpy()
b = torch.stack((b0, b1, b2)).detach().cpu().numpy()
return _LFilter.apply(waveform, a, b).clamp(-1, 1)
def bass_biquad(waveform, sample_rate, gain, central_freq=100, Q=0.707):
dtype = waveform.dtype
device = waveform.device
central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device)
Q = torch.as_tensor(Q, dtype=dtype, device=device)
gain = torch.as_tensor(gain, dtype=dtype, device=device)
w0 = 2 * math.pi * central_freq / sample_rate
alpha = torch.sin(w0) / 2 / Q
A = torch.exp(gain / 40 * math.log(10))
temp1 = 2 * torch.sqrt(A) * alpha
temp2 = (A - 1) * torch.cos(w0)
temp3 = (A + 1) * torch.cos(w0)
b0 = A * (A + 1 - temp2 + temp1)
b1 = 2 * A * (A - 1 - temp3)
b2 = A * (A + 1 - temp2 - temp1)
a0 = A + 1 + temp2 + temp1
a1 = -2 * (A - 1 + temp3)
a2 = A + 1 + temp2 - temp1
return _biquad(waveform, b0 / a0, b1 / a0, b2 / a0, a0 / a0, a1 / a0, a2 / a0)
def equalizer_biquad(waveform, sample_rate, center_freq, gain, Q=0.707):
dtype = waveform.dtype
device = waveform.device
center_freq = torch.as_tensor(center_freq, dtype=dtype, device=device)
Q = torch.as_tensor(Q, dtype=dtype, device=device)
gain = torch.as_tensor(gain, dtype=dtype, device=device)
w0 = 2 * math.pi * center_freq / sample_rate
A = torch.exp(gain / 40.0 * math.log(10))
alpha = torch.sin(w0) / 2 / Q
b0 = 1 + alpha * A
b1 = -2 * torch.cos(w0)
b2 = 1 - alpha * A
a0 = 1 + alpha / A
a1 = -2 * torch.cos(w0)
a2 = 1 - alpha / A
return _biquad(waveform, b0, b1, b2, a0, a1, a2)
def treble_biquad(waveform, sample_rate, gain, central_freq=3000, Q=0.707):
dtype = waveform.dtype
device = waveform.device
central_freq = torch.as_tensor(central_freq, dtype=dtype, device=device)
Q = torch.as_tensor(Q, dtype=dtype, device=device)
gain = torch.as_tensor(gain, dtype=dtype, device=device)
w0 = 2 * math.pi * central_freq / sample_rate
alpha = torch.sin(w0) / 2 / Q
A = torch.exp(gain / 40 * math.log(10))
temp1 = 2 * torch.sqrt(A) * alpha
temp2 = (A - 1) * torch.cos(w0)
temp3 = (A + 1) * torch.cos(w0)
b0 = A * (A + 1 + temp2 + temp1)
b1 = -2 * A * (A - 1 + temp3)
b2 = A * (A + 1 + temp2 - temp1)
a0 = A + 1 - temp2 + temp1
a1 = 2 * (A - 1 - temp3)
a2 = A + 1 - temp2 - temp1
return _biquad(waveform, b0, b1, b2, a0, a1, a2)
You can’t perform that action at this time.