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| 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) | |