surfmore/SimpleRVC
0
1import sys, torch, numpy as np, traceback, pdb2import torch.nn as nn3from time import time as ttime4import torch.nn.functional as F5 6 7class BiGRU(nn.Module):8 def __init__(self, input_features, hidden_features, num_layers):9 super(BiGRU, self).__init__()10 self.gru = nn.GRU(11 input_features,12 hidden_features,13 num_layers=num_layers,14 batch_first=True,15 bidirectional=True,16 )17 18 def forward(self, x):19 return self.gru(x)[0]20 21 22class ConvBlockRes(nn.Module):23 def __init__(self, in_channels, out_channels, momentum=0.01):24 super(ConvBlockRes, self).__init__()25 self.conv = nn.Sequential(26 nn.Conv2d(27 in_channels=in_channels,28 out_channels=out_channels,29 kernel_size=(3, 3),30 stride=(1, 1),31 padding=(1, 1),32 bias=False,33 ),34 nn.BatchNorm2d(out_channels, momentum=momentum),35 nn.ReLU(),36 nn.Conv2d(37 in_channels=out_channels,38 out_channels=out_channels,39 kernel_size=(3, 3),40 stride=(1, 1),41 padding=(1, 1),42 bias=False,43 ),44 nn.BatchNorm2d(out_channels, momentum=momentum),45 nn.ReLU(),46 )47 if in_channels != out_channels:48 self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))49 self.is_shortcut = True50 else:51 self.is_shortcut = False52 53 def forward(self, x):54 if self.is_shortcut:55 return self.conv(x) + self.shortcut(x)56 else:57 return self.conv(x) + x58 59 60class Encoder(nn.Module):61 def __init__(62 self,63 in_channels,64 in_size,65 n_encoders,66 kernel_size,67 n_blocks,68 out_channels=16,69 momentum=0.01,70 ):71 super(Encoder, self).__init__()72 self.n_encoders = n_encoders73 self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)74 self.layers = nn.ModuleList()75 self.latent_channels = []76 for i in range(self.n_encoders):77 self.layers.append(78 ResEncoderBlock(79 in_channels, out_channels, kernel_size, n_blocks, momentum=momentum80 )81 )82 self.latent_channels.append([out_channels, in_size])83 in_channels = out_channels84 out_channels *= 285 in_size //= 286 self.out_size = in_size87 self.out_channel = out_channels88 89 def forward(self, x):90 concat_tensors = []91 x = self.bn(x)92 for i in range(self.n_encoders):93 _, x = self.layers[i](x)94 concat_tensors.append(_)95 return x, concat_tensors96 97 98class ResEncoderBlock(nn.Module):99 def __init__(100 self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01101 ):102 super(ResEncoderBlock, self).__init__()103 self.n_blocks = n_blocks104 self.conv = nn.ModuleList()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_size109 if self.kernel_size is not None:110 self.pool = nn.AvgPool2d(kernel_size=kernel_size)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:116 return x, self.pool(x)117 else:118 return x119 120 121class Intermediate(nn.Module): #122 def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):123 super(Intermediate, self).__init__()124 self.n_inters = n_inters125 self.layers = nn.ModuleList()126 self.layers.append(127 ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)128 )129 for i in range(self.n_inters - 1):130 self.layers.append(131 ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)132 )133 134 def forward(self, x):135 for i in range(self.n_inters):136 x = self.layers[i](x)137 return x138 139 140class 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_blocks145 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 x169 170 171class 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_decoders176 for i in range(self.n_decoders):177 out_channels = in_channels // 2178 self.layers.append(179 ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)180 )181 in_channels = out_channels182 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 x187 188 189class 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_channels202 )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_blocks211 )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 x218 219 220class 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 x258 259 260from librosa.filters import mel261 262 263class 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_fft278 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_fft290 self.hop_length = hop_length291 self.win_length = win_length292 self.sampling_rate = sampling_rate293 self.n_mel_channels = n_mel_channels294 self.clamp = clamp295 self.is_half = is_half296 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.device306 )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 + 1319 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_new323 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_spec328 329 330class 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 = model340 self.resample_kernel = {}341 self.is_half = is_half342 if device is None:343 device = "cuda" if torch.cuda.is_available() else "cpu"344 self.device = device345 self.mel_extractor = MelSpectrogram(346 is_half, 128, 16000, 1024, 160, None, 30, 8000347 ).to(device)348 self.model = self.model.to(device)349 cents_mapping = 20 * np.arange(360) + 1997.3794084376191350 self.cents_mapping = np.pad(cents_mapping, (4, 4)) # 368351 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] = 0365 # f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])366 return f0367 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 f0386 387 def to_local_average_cents(self, salience, thred=0.05):388 # t0 = ttime()389 center = np.argmax(salience, axis=1) # 帧长#index390 salience = np.pad(salience, ((0, 0), (4, 4))) # 帧长,368391 # t1 = ttime()392 center += 4393 todo_salience = []394 todo_cents_mapping = []395 starts = center - 4396 ends = center + 5397 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，9402 todo_cents_mapping = np.array(todo_cents_mapping) # 帧长，9403 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] = 0409 # t4 = ttime()410 # print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))411 return devided412 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 # 0.01423# 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)433