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Browse files- rmvpe (1).pt +3 -0
- rmvpe (1).py +432 -0
rmvpe (1).pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:a5ed4719f59085d1affc5d81354c70828c740584f2d24e782523345a6a278962
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size 181189687
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rmvpe (1).py
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@@ -0,0 +1,432 @@
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1 |
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import sys, torch, numpy as np, traceback, pdb
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import torch.nn as nn
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3 |
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from time import time as ttime
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import torch.nn.functional as F
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6 |
+
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7 |
+
class BiGRU(nn.Module):
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def __init__(self, input_features, hidden_features, num_layers):
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super(BiGRU, self).__init__()
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self.gru = nn.GRU(
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input_features,
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+
hidden_features,
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num_layers=num_layers,
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batch_first=True,
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bidirectional=True,
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)
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+
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def forward(self, x):
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return self.gru(x)[0]
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+
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+
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22 |
+
class ConvBlockRes(nn.Module):
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+
def __init__(self, in_channels, out_channels, momentum=0.01):
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super(ConvBlockRes, self).__init__()
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self.conv = nn.Sequential(
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nn.Conv2d(
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in_channels=in_channels,
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out_channels=out_channels,
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+
kernel_size=(3, 3),
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+
stride=(1, 1),
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31 |
+
padding=(1, 1),
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32 |
+
bias=False,
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33 |
+
),
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+
nn.BatchNorm2d(out_channels, momentum=momentum),
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35 |
+
nn.ReLU(),
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36 |
+
nn.Conv2d(
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37 |
+
in_channels=out_channels,
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38 |
+
out_channels=out_channels,
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39 |
+
kernel_size=(3, 3),
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40 |
+
stride=(1, 1),
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41 |
+
padding=(1, 1),
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42 |
+
bias=False,
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43 |
+
),
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44 |
+
nn.BatchNorm2d(out_channels, momentum=momentum),
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45 |
+
nn.ReLU(),
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46 |
+
)
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47 |
+
if in_channels != out_channels:
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48 |
+
self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
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49 |
+
self.is_shortcut = True
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50 |
+
else:
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51 |
+
self.is_shortcut = False
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52 |
+
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53 |
+
def forward(self, x):
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54 |
+
if self.is_shortcut:
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55 |
+
return self.conv(x) + self.shortcut(x)
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56 |
+
else:
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57 |
+
return self.conv(x) + x
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58 |
+
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59 |
+
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60 |
+
class Encoder(nn.Module):
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61 |
+
def __init__(
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62 |
+
self,
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63 |
+
in_channels,
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64 |
+
in_size,
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65 |
+
n_encoders,
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66 |
+
kernel_size,
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67 |
+
n_blocks,
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68 |
+
out_channels=16,
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69 |
+
momentum=0.01,
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70 |
+
):
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71 |
+
super(Encoder, self).__init__()
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72 |
+
self.n_encoders = n_encoders
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73 |
+
self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
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74 |
+
self.layers = nn.ModuleList()
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75 |
+
self.latent_channels = []
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76 |
+
for i in range(self.n_encoders):
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77 |
+
self.layers.append(
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78 |
+
ResEncoderBlock(
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79 |
+
in_channels, out_channels, kernel_size, n_blocks, momentum=momentum
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80 |
+
)
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81 |
+
)
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82 |
+
self.latent_channels.append([out_channels, in_size])
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83 |
+
in_channels = out_channels
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84 |
+
out_channels *= 2
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85 |
+
in_size //= 2
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86 |
+
self.out_size = in_size
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87 |
+
self.out_channel = out_channels
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88 |
+
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89 |
+
def forward(self, x):
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90 |
+
concat_tensors = []
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91 |
+
x = self.bn(x)
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92 |
+
for i in range(self.n_encoders):
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93 |
+
_, x = self.layers[i](x)
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94 |
+
concat_tensors.append(_)
|
95 |
+
return x, concat_tensors
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96 |
+
|
97 |
+
|
98 |
+
class ResEncoderBlock(nn.Module):
|
99 |
+
def __init__(
|
100 |
+
self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
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101 |
+
):
|
102 |
+
super(ResEncoderBlock, self).__init__()
|
103 |
+
self.n_blocks = n_blocks
|
104 |
+
self.conv = nn.ModuleList()
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105 |
+
self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
|
106 |
+
for i in range(n_blocks - 1):
|
107 |
+
self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
|
108 |
+
self.kernel_size = kernel_size
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109 |
+
if self.kernel_size is not None:
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110 |
+
self.pool = nn.AvgPool2d(kernel_size=kernel_size)
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111 |
+
|
112 |
+
def forward(self, x):
|
113 |
+
for i in range(self.n_blocks):
|
114 |
+
x = self.conv[i](x)
|
115 |
+
if self.kernel_size is not None:
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116 |
+
return x, self.pool(x)
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117 |
+
else:
|
118 |
+
return x
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119 |
+
|
120 |
+
|
121 |
+
class Intermediate(nn.Module): #
|
122 |
+
def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
|
123 |
+
super(Intermediate, self).__init__()
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124 |
+
self.n_inters = n_inters
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125 |
+
self.layers = nn.ModuleList()
|
126 |
+
self.layers.append(
|
127 |
+
ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)
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128 |
+
)
|
129 |
+
for i in range(self.n_inters - 1):
|
130 |
+
self.layers.append(
|
131 |
+
ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)
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132 |
+
)
|
133 |
+
|
134 |
+
def forward(self, x):
|
135 |
+
for i in range(self.n_inters):
|
136 |
+
x = self.layers[i](x)
|
137 |
+
return x
|
138 |
+
|
139 |
+
|
140 |
+
class ResDecoderBlock(nn.Module):
|
141 |
+
def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
|
142 |
+
super(ResDecoderBlock, self).__init__()
|
143 |
+
out_padding = (0, 1) if stride == (1, 2) else (1, 1)
|
144 |
+
self.n_blocks = n_blocks
|
145 |
+
self.conv1 = nn.Sequential(
|
146 |
+
nn.ConvTranspose2d(
|
147 |
+
in_channels=in_channels,
|
148 |
+
out_channels=out_channels,
|
149 |
+
kernel_size=(3, 3),
|
150 |
+
stride=stride,
|
151 |
+
padding=(1, 1),
|
152 |
+
output_padding=out_padding,
|
153 |
+
bias=False,
|
154 |
+
),
|
155 |
+
nn.BatchNorm2d(out_channels, momentum=momentum),
|
156 |
+
nn.ReLU(),
|
157 |
+
)
|
158 |
+
self.conv2 = nn.ModuleList()
|
159 |
+
self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
|
160 |
+
for i in range(n_blocks - 1):
|
161 |
+
self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
|
162 |
+
|
163 |
+
def forward(self, x, concat_tensor):
|
164 |
+
x = self.conv1(x)
|
165 |
+
x = torch.cat((x, concat_tensor), dim=1)
|
166 |
+
for i in range(self.n_blocks):
|
167 |
+
x = self.conv2[i](x)
|
168 |
+
return x
|
169 |
+
|
170 |
+
|
171 |
+
class Decoder(nn.Module):
|
172 |
+
def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
|
173 |
+
super(Decoder, self).__init__()
|
174 |
+
self.layers = nn.ModuleList()
|
175 |
+
self.n_decoders = n_decoders
|
176 |
+
for i in range(self.n_decoders):
|
177 |
+
out_channels = in_channels // 2
|
178 |
+
self.layers.append(
|
179 |
+
ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)
|
180 |
+
)
|
181 |
+
in_channels = out_channels
|
182 |
+
|
183 |
+
def forward(self, x, concat_tensors):
|
184 |
+
for i in range(self.n_decoders):
|
185 |
+
x = self.layers[i](x, concat_tensors[-1 - i])
|
186 |
+
return x
|
187 |
+
|
188 |
+
|
189 |
+
class DeepUnet(nn.Module):
|
190 |
+
def __init__(
|
191 |
+
self,
|
192 |
+
kernel_size,
|
193 |
+
n_blocks,
|
194 |
+
en_de_layers=5,
|
195 |
+
inter_layers=4,
|
196 |
+
in_channels=1,
|
197 |
+
en_out_channels=16,
|
198 |
+
):
|
199 |
+
super(DeepUnet, self).__init__()
|
200 |
+
self.encoder = Encoder(
|
201 |
+
in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels
|
202 |
+
)
|
203 |
+
self.intermediate = Intermediate(
|
204 |
+
self.encoder.out_channel // 2,
|
205 |
+
self.encoder.out_channel,
|
206 |
+
inter_layers,
|
207 |
+
n_blocks,
|
208 |
+
)
|
209 |
+
self.decoder = Decoder(
|
210 |
+
self.encoder.out_channel, en_de_layers, kernel_size, n_blocks
|
211 |
+
)
|
212 |
+
|
213 |
+
def forward(self, x):
|
214 |
+
x, concat_tensors = self.encoder(x)
|
215 |
+
x = self.intermediate(x)
|
216 |
+
x = self.decoder(x, concat_tensors)
|
217 |
+
return x
|
218 |
+
|
219 |
+
|
220 |
+
class E2E(nn.Module):
|
221 |
+
def __init__(
|
222 |
+
self,
|
223 |
+
n_blocks,
|
224 |
+
n_gru,
|
225 |
+
kernel_size,
|
226 |
+
en_de_layers=5,
|
227 |
+
inter_layers=4,
|
228 |
+
in_channels=1,
|
229 |
+
en_out_channels=16,
|
230 |
+
):
|
231 |
+
super(E2E, self).__init__()
|
232 |
+
self.unet = DeepUnet(
|
233 |
+
kernel_size,
|
234 |
+
n_blocks,
|
235 |
+
en_de_layers,
|
236 |
+
inter_layers,
|
237 |
+
in_channels,
|
238 |
+
en_out_channels,
|
239 |
+
)
|
240 |
+
self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
|
241 |
+
if n_gru:
|
242 |
+
self.fc = nn.Sequential(
|
243 |
+
BiGRU(3 * 128, 256, n_gru),
|
244 |
+
nn.Linear(512, 360),
|
245 |
+
nn.Dropout(0.25),
|
246 |
+
nn.Sigmoid(),
|
247 |
+
)
|
248 |
+
else:
|
249 |
+
self.fc = nn.Sequential(
|
250 |
+
nn.Linear(3 * N_MELS, N_CLASS), nn.Dropout(0.25), nn.Sigmoid()
|
251 |
+
)
|
252 |
+
|
253 |
+
def forward(self, mel):
|
254 |
+
mel = mel.transpose(-1, -2).unsqueeze(1)
|
255 |
+
x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
|
256 |
+
x = self.fc(x)
|
257 |
+
return x
|
258 |
+
|
259 |
+
|
260 |
+
from librosa.filters import mel
|
261 |
+
|
262 |
+
|
263 |
+
class MelSpectrogram(torch.nn.Module):
|
264 |
+
def __init__(
|
265 |
+
self,
|
266 |
+
is_half,
|
267 |
+
n_mel_channels,
|
268 |
+
sampling_rate,
|
269 |
+
win_length,
|
270 |
+
hop_length,
|
271 |
+
n_fft=None,
|
272 |
+
mel_fmin=0,
|
273 |
+
mel_fmax=None,
|
274 |
+
clamp=1e-5,
|
275 |
+
):
|
276 |
+
super().__init__()
|
277 |
+
n_fft = win_length if n_fft is None else n_fft
|
278 |
+
self.hann_window = {}
|
279 |
+
mel_basis = mel(
|
280 |
+
sr=sampling_rate,
|
281 |
+
n_fft=n_fft,
|
282 |
+
n_mels=n_mel_channels,
|
283 |
+
fmin=mel_fmin,
|
284 |
+
fmax=mel_fmax,
|
285 |
+
htk=True,
|
286 |
+
)
|
287 |
+
mel_basis = torch.from_numpy(mel_basis).float()
|
288 |
+
self.register_buffer("mel_basis", mel_basis)
|
289 |
+
self.n_fft = win_length if n_fft is None else n_fft
|
290 |
+
self.hop_length = hop_length
|
291 |
+
self.win_length = win_length
|
292 |
+
self.sampling_rate = sampling_rate
|
293 |
+
self.n_mel_channels = n_mel_channels
|
294 |
+
self.clamp = clamp
|
295 |
+
self.is_half = is_half
|
296 |
+
|
297 |
+
def forward(self, audio, keyshift=0, speed=1, center=True):
|
298 |
+
factor = 2 ** (keyshift / 12)
|
299 |
+
n_fft_new = int(np.round(self.n_fft * factor))
|
300 |
+
win_length_new = int(np.round(self.win_length * factor))
|
301 |
+
hop_length_new = int(np.round(self.hop_length * speed))
|
302 |
+
keyshift_key = str(keyshift) + "_" + str(audio.device)
|
303 |
+
if keyshift_key not in self.hann_window:
|
304 |
+
self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(
|
305 |
+
audio.device
|
306 |
+
)
|
307 |
+
fft = torch.stft(
|
308 |
+
audio,
|
309 |
+
n_fft=n_fft_new,
|
310 |
+
hop_length=hop_length_new,
|
311 |
+
win_length=win_length_new,
|
312 |
+
window=self.hann_window[keyshift_key],
|
313 |
+
center=center,
|
314 |
+
return_complex=True,
|
315 |
+
)
|
316 |
+
magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))
|
317 |
+
if keyshift != 0:
|
318 |
+
size = self.n_fft // 2 + 1
|
319 |
+
resize = magnitude.size(1)
|
320 |
+
if resize < size:
|
321 |
+
magnitude = F.pad(magnitude, (0, 0, 0, size - resize))
|
322 |
+
magnitude = magnitude[:, :size, :] * self.win_length / win_length_new
|
323 |
+
mel_output = torch.matmul(self.mel_basis, magnitude)
|
324 |
+
if self.is_half == True:
|
325 |
+
mel_output = mel_output.half()
|
326 |
+
log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
|
327 |
+
return log_mel_spec
|
328 |
+
|
329 |
+
|
330 |
+
class RMVPE:
|
331 |
+
def __init__(self, model_path, is_half, device=None):
|
332 |
+
self.resample_kernel = {}
|
333 |
+
model = E2E(4, 1, (2, 2))
|
334 |
+
ckpt = torch.load(model_path, map_location="cpu")
|
335 |
+
model.load_state_dict(ckpt)
|
336 |
+
model.eval()
|
337 |
+
if is_half == True:
|
338 |
+
model = model.half()
|
339 |
+
self.model = model
|
340 |
+
self.resample_kernel = {}
|
341 |
+
self.is_half = is_half
|
342 |
+
if device is None:
|
343 |
+
device = "cuda" if torch.cuda.is_available() else "cpu"
|
344 |
+
self.device = device
|
345 |
+
self.mel_extractor = MelSpectrogram(
|
346 |
+
is_half, 128, 16000, 1024, 160, None, 30, 8000
|
347 |
+
).to(device)
|
348 |
+
self.model = self.model.to(device)
|
349 |
+
cents_mapping = 20 * np.arange(360) + 1997.3794084376191
|
350 |
+
self.cents_mapping = np.pad(cents_mapping, (4, 4)) # 368
|
351 |
+
|
352 |
+
def mel2hidden(self, mel):
|
353 |
+
with torch.no_grad():
|
354 |
+
n_frames = mel.shape[-1]
|
355 |
+
mel = F.pad(
|
356 |
+
mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
|
357 |
+
)
|
358 |
+
hidden = self.model(mel)
|
359 |
+
return hidden[:, :n_frames]
|
360 |
+
|
361 |
+
def decode(self, hidden, thred=0.03):
|
362 |
+
cents_pred = self.to_local_average_cents(hidden, thred=thred)
|
363 |
+
f0 = 10 * (2 ** (cents_pred / 1200))
|
364 |
+
f0[f0 == 10] = 0
|
365 |
+
# f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
|
366 |
+
return f0
|
367 |
+
|
368 |
+
def infer_from_audio(self, audio, thred=0.03):
|
369 |
+
audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)
|
370 |
+
# torch.cuda.synchronize()
|
371 |
+
# t0=ttime()
|
372 |
+
mel = self.mel_extractor(audio, center=True)
|
373 |
+
# torch.cuda.synchronize()
|
374 |
+
# t1=ttime()
|
375 |
+
hidden = self.mel2hidden(mel)
|
376 |
+
# torch.cuda.synchronize()
|
377 |
+
# t2=ttime()
|
378 |
+
hidden = hidden.squeeze(0).cpu().numpy()
|
379 |
+
if self.is_half == True:
|
380 |
+
hidden = hidden.astype("float32")
|
381 |
+
f0 = self.decode(hidden, thred=thred)
|
382 |
+
# torch.cuda.synchronize()
|
383 |
+
# t3=ttime()
|
384 |
+
# print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
|
385 |
+
return f0
|
386 |
+
|
387 |
+
def to_local_average_cents(self, salience, thred=0.05):
|
388 |
+
# t0 = ttime()
|
389 |
+
center = np.argmax(salience, axis=1) # 帧长#index
|
390 |
+
salience = np.pad(salience, ((0, 0), (4, 4))) # 帧长,368
|
391 |
+
# t1 = ttime()
|
392 |
+
center += 4
|
393 |
+
todo_salience = []
|
394 |
+
todo_cents_mapping = []
|
395 |
+
starts = center - 4
|
396 |
+
ends = center + 5
|
397 |
+
for idx in range(salience.shape[0]):
|
398 |
+
todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
|
399 |
+
todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
|
400 |
+
# t2 = ttime()
|
401 |
+
todo_salience = np.array(todo_salience) # 帧长,9
|
402 |
+
todo_cents_mapping = np.array(todo_cents_mapping) # 帧长,9
|
403 |
+
product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
|
404 |
+
weight_sum = np.sum(todo_salience, 1) # 帧长
|
405 |
+
devided = product_sum / weight_sum # 帧长
|
406 |
+
# t3 = ttime()
|
407 |
+
maxx = np.max(salience, axis=1) # 帧长
|
408 |
+
devided[maxx <= thred] = 0
|
409 |
+
# t4 = ttime()
|
410 |
+
# print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
|
411 |
+
return devided
|
412 |
+
|
413 |
+
|
414 |
+
# if __name__ == '__main__':
|
415 |
+
# audio, sampling_rate = sf.read("卢本伟语录~1.wav")
|
416 |
+
# if len(audio.shape) > 1:
|
417 |
+
# audio = librosa.to_mono(audio.transpose(1, 0))
|
418 |
+
# audio_bak = audio.copy()
|
419 |
+
# if sampling_rate != 16000:
|
420 |
+
# audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
|
421 |
+
# model_path = "/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/test-RMVPE/weights/rmvpe_llc_half.pt"
|
422 |
+
# thred = 0.03 # 0.01
|
423 |
+
# device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
424 |
+
# rmvpe = RMVPE(model_path,is_half=False, device=device)
|
425 |
+
# t0=ttime()
|
426 |
+
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
427 |
+
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
428 |
+
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
429 |
+
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
430 |
+
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
431 |
+
# t1=ttime()
|
432 |
+
# print(f0.shape,t1-t0)
|