-
Notifications
You must be signed in to change notification settings - Fork 14
/
rmvpe.py
432 lines (392 loc) · 14.2 KB
/
rmvpe.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
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
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
import sys, torch, numpy as np, traceback, pdb
import torch.nn as nn
from time import time as ttime
import torch.nn.functional as F
class BiGRU(nn.Module):
def __init__(self, input_features, hidden_features, num_layers):
super(BiGRU, self).__init__()
self.gru = nn.GRU(
input_features,
hidden_features,
num_layers=num_layers,
batch_first=True,
bidirectional=True,
)
def forward(self, x):
return self.gru(x)[0]
class ConvBlockRes(nn.Module):
def __init__(self, in_channels, out_channels, momentum=0.01):
super(ConvBlockRes, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias=False,
),
nn.BatchNorm2d(out_channels, momentum=momentum),
nn.ReLU(),
nn.Conv2d(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias=False,
),
nn.BatchNorm2d(out_channels, momentum=momentum),
nn.ReLU(),
)
if in_channels != out_channels:
self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
self.is_shortcut = True
else:
self.is_shortcut = False
def forward(self, x):
if self.is_shortcut:
return self.conv(x) + self.shortcut(x)
else:
return self.conv(x) + x
class Encoder(nn.Module):
def __init__(
self,
in_channels,
in_size,
n_encoders,
kernel_size,
n_blocks,
out_channels=16,
momentum=0.01,
):
super(Encoder, self).__init__()
self.n_encoders = n_encoders
self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
self.layers = nn.ModuleList()
self.latent_channels = []
for i in range(self.n_encoders):
self.layers.append(
ResEncoderBlock(
in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
)
)
self.latent_channels.append([out_channels, in_size])
in_channels = out_channels
out_channels *= 2
in_size //= 2
self.out_size = in_size
self.out_channel = out_channels
def forward(self, x):
concat_tensors = []
x = self.bn(x)
for i in range(self.n_encoders):
_, x = self.layers[i](x)
concat_tensors.append(_)
return x, concat_tensors
class ResEncoderBlock(nn.Module):
def __init__(
self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
):
super(ResEncoderBlock, self).__init__()
self.n_blocks = n_blocks
self.conv = nn.ModuleList()
self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
for i in range(n_blocks - 1):
self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
self.kernel_size = kernel_size
if self.kernel_size is not None:
self.pool = nn.AvgPool2d(kernel_size=kernel_size)
def forward(self, x):
for i in range(self.n_blocks):
x = self.conv[i](x)
if self.kernel_size is not None:
return x, self.pool(x)
else:
return x
class Intermediate(nn.Module): #
def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
super(Intermediate, self).__init__()
self.n_inters = n_inters
self.layers = nn.ModuleList()
self.layers.append(
ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
)
for i in range(self.n_inters - 1):
self.layers.append(
ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
)
def forward(self, x):
for i in range(self.n_inters):
x = self.layers[i](x)
return x
class ResDecoderBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
super(ResDecoderBlock, self).__init__()
out_padding = (0, 1) if stride == (1, 2) else (1, 1)
self.n_blocks = n_blocks
self.conv1 = nn.Sequential(
nn.ConvTranspose2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(3, 3),
stride=stride,
padding=(1, 1),
output_padding=out_padding,
bias=False,
),
nn.BatchNorm2d(out_channels, momentum=momentum),
nn.ReLU(),
)
self.conv2 = nn.ModuleList()
self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
for i in range(n_blocks - 1):
self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
def forward(self, x, concat_tensor):
x = self.conv1(x)
x = torch.cat((x, concat_tensor), dim=1)
for i in range(self.n_blocks):
x = self.conv2[i](x)
return x
class Decoder(nn.Module):
def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
super(Decoder, self).__init__()
self.layers = nn.ModuleList()
self.n_decoders = n_decoders
for i in range(self.n_decoders):
out_channels = in_channels // 2
self.layers.append(
ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
)
in_channels = out_channels
def forward(self, x, concat_tensors):
for i in range(self.n_decoders):
x = self.layers[i](x, concat_tensors[-1 - i])
return x
class DeepUnet(nn.Module):
def __init__(
self,
kernel_size,
n_blocks,
en_de_layers=5,
inter_layers=4,
in_channels=1,
en_out_channels=16,
):
super(DeepUnet, self).__init__()
self.encoder = Encoder(
in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
)
self.intermediate = Intermediate(
self.encoder.out_channel // 2,
self.encoder.out_channel,
inter_layers,
n_blocks,
)
self.decoder = Decoder(
self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
)
def forward(self, x):
x, concat_tensors = self.encoder(x)
x = self.intermediate(x)
x = self.decoder(x, concat_tensors)
return x
class E2E(nn.Module):
def __init__(
self,
n_blocks,
n_gru,
kernel_size,
en_de_layers=5,
inter_layers=4,
in_channels=1,
en_out_channels=16,
):
super(E2E, self).__init__()
self.unet = DeepUnet(
kernel_size,
n_blocks,
en_de_layers,
inter_layers,
in_channels,
en_out_channels,
)
self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
if n_gru:
self.fc = nn.Sequential(
BiGRU(3 * 128, 256, n_gru),
nn.Linear(512, 360),
nn.Dropout(0.25),
nn.Sigmoid(),
)
else:
self.fc = nn.Sequential(
nn.Linear(3 * N_MELS, N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
)
def forward(self, mel):
mel = mel.transpose(-1, -2).unsqueeze(1)
x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
x = self.fc(x)
return x
from librosa.filters import mel
class MelSpectrogram(torch.nn.Module):
def __init__(
self,
is_half,
n_mel_channels,
sampling_rate,
win_length,
hop_length,
n_fft=None,
mel_fmin=0,
mel_fmax=None,
clamp=1e-5,
):
super().__init__()
n_fft = win_length if n_fft is None else n_fft
self.hann_window = {}
mel_basis = mel(
sr=sampling_rate,
n_fft=n_fft,
n_mels=n_mel_channels,
fmin=mel_fmin,
fmax=mel_fmax,
htk=True,
)
mel_basis = torch.from_numpy(mel_basis).float()
self.register_buffer("mel_basis", mel_basis)
self.n_fft = win_length if n_fft is None else n_fft
self.hop_length = hop_length
self.win_length = win_length
self.sampling_rate = sampling_rate
self.n_mel_channels = n_mel_channels
self.clamp = clamp
self.is_half = is_half
def forward(self, audio, keyshift=0, speed=1, center=True):
factor = 2 ** (keyshift / 12)
n_fft_new = int(np.round(self.n_fft * factor))
win_length_new = int(np.round(self.win_length * factor))
hop_length_new = int(np.round(self.hop_length * speed))
keyshift_key = str(keyshift) + "_" + str(audio.device)
if keyshift_key not in self.hann_window:
self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(
audio.device
)
fft = torch.stft(
audio,
n_fft=n_fft_new,
hop_length=hop_length_new,
win_length=win_length_new,
window=self.hann_window[keyshift_key],
center=center,
return_complex=True,
)
magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
if keyshift != 0:
size = self.n_fft // 2 + 1
resize = magnitude.size(1)
if resize < size:
magnitude = F.pad(magnitude, (0, 0, 0, size - resize))
magnitude = magnitude[:, :size, :] * self.win_length / win_length_new
mel_output = torch.matmul(self.mel_basis, magnitude)
if self.is_half == True:
mel_output = mel_output.half()
log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
return log_mel_spec
class RMVPE:
def __init__(self, model_path, is_half, device=None):
self.resample_kernel = {}
model = E2E(4, 1, (2, 2))
ckpt = torch.load(model_path, map_location="cpu")
model.load_state_dict(ckpt)
model.eval()
if is_half == True:
model = model.half()
self.model = model
self.resample_kernel = {}
self.is_half = is_half
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.device = device
self.mel_extractor = MelSpectrogram(
is_half, 128, 16000, 1024, 160, None, 30, 8000
).to(device)
self.model = self.model.to(device)
cents_mapping = 20 * np.arange(360) + 1997.3794084376191
self.cents_mapping = np.pad(cents_mapping, (4, 4)) # 368
def mel2hidden(self, mel):
with torch.no_grad():
n_frames = mel.shape[-1]
mel = F.pad(
mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
)
hidden = self.model(mel)
return hidden[:, :n_frames]
def decode(self, hidden, thred=0.03):
cents_pred = self.to_local_average_cents(hidden, thred=thred)
f0 = 10 * (2 ** (cents_pred / 1200))
f0[f0 == 10] = 0
# f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
return f0
def infer_from_audio(self, audio, thred=0.03):
audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
# torch.cuda.synchronize()
# t0=ttime()
mel = self.mel_extractor(audio, center=True)
# torch.cuda.synchronize()
# t1=ttime()
hidden = self.mel2hidden(mel)
# torch.cuda.synchronize()
# t2=ttime()
hidden = hidden.squeeze(0).cpu().numpy()
if self.is_half == True:
hidden = hidden.astype("float32")
f0 = self.decode(hidden, thred=thred)
# torch.cuda.synchronize()
# t3=ttime()
# print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
return f0
def to_local_average_cents(self, salience, thred=0.05):
# t0 = ttime()
center = np.argmax(salience, axis=1) # 帧长#index
salience = np.pad(salience, ((0, 0), (4, 4))) # 帧长,368
# t1 = ttime()
center += 4
todo_salience = []
todo_cents_mapping = []
starts = center - 4
ends = center + 5
for idx in range(salience.shape[0]):
todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
# t2 = ttime()
todo_salience = np.array(todo_salience) # 帧长,9
todo_cents_mapping = np.array(todo_cents_mapping) # 帧长,9
product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
weight_sum = np.sum(todo_salience, 1) # 帧长
devided = product_sum / weight_sum # 帧长
# t3 = ttime()
maxx = np.max(salience, axis=1) # 帧长
devided[maxx <= thred] = 0
# t4 = ttime()
# print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
return devided
# if __name__ == '__main__':
# audio, sampling_rate = sf.read("卢本伟语录~1.wav")
# if len(audio.shape) > 1:
# audio = librosa.to_mono(audio.transpose(1, 0))
# audio_bak = audio.copy()
# if sampling_rate != 16000:
# audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
# model_path = "/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/test-RMVPE/weights/rmvpe_llc_half.pt"
# thred = 0.03 # 0.01
# device = 'cuda' if torch.cuda.is_available() else 'cpu'
# rmvpe = RMVPE(model_path,is_half=False, device=device)
# t0=ttime()
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
# t1=ttime()
# print(f0.shape,t1-t0)