Codecooker/rvcapi

1import numpy as np2import torch3import torch.nn as nn4import torch.nn.functional as F5from librosa.filters import mel6 7 8class BiGRU(nn.Module):9    def __init__(self, input_features, hidden_features, num_layers):10        super(BiGRU, self).__init__()11        self.gru = nn.GRU(12            input_features,13            hidden_features,14            num_layers=num_layers,15            batch_first=True,16            bidirectional=True,17        )18 19    def forward(self, x):20        return self.gru(x)[0]21 22 23class ConvBlockRes(nn.Module):24    def __init__(self, in_channels, out_channels, momentum=0.01):25        super(ConvBlockRes, self).__init__()26        self.conv = nn.Sequential(27            nn.Conv2d(28                in_channels=in_channels,29                out_channels=out_channels,30                kernel_size=(3, 3),31                stride=(1, 1),32                padding=(1, 1),33                bias=False,34            ),35            nn.BatchNorm2d(out_channels, momentum=momentum),36            nn.ReLU(),37            nn.Conv2d(38                in_channels=out_channels,39                out_channels=out_channels,40                kernel_size=(3, 3),41                stride=(1, 1),42                padding=(1, 1),43                bias=False,44            ),45            nn.BatchNorm2d(out_channels, momentum=momentum),46            nn.ReLU(),47        )48        if in_channels != out_channels:49            self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))50            self.is_shortcut = True51        else:52            self.is_shortcut = False53 54    def forward(self, x):55        if self.is_shortcut:56            return self.conv(x) + self.shortcut(x)57        else:58            return self.conv(x) + x59 60 61class Encoder(nn.Module):62    def __init__(63        self,64        in_channels,65        in_size,66        n_encoders,67        kernel_size,68        n_blocks,69        out_channels=16,70        momentum=0.01,71    ):72        super(Encoder, self).__init__()73        self.n_encoders = n_encoders74        self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)75        self.layers = nn.ModuleList()76        self.latent_channels = []77        for i in range(self.n_encoders):78            self.layers.append(79                ResEncoderBlock(80                    in_channels, out_channels, kernel_size, n_blocks, momentum=momentum81                )82            )83            self.latent_channels.append([out_channels, in_size])84            in_channels = out_channels85            out_channels *= 286            in_size //= 287        self.out_size = in_size88        self.out_channel = out_channels89 90    def forward(self, x):91        concat_tensors = []92        x = self.bn(x)93        for i in range(self.n_encoders):94            _, x = self.layers[i](x)95            concat_tensors.append(_)96        return x, concat_tensors97 98 99class ResEncoderBlock(nn.Module):100    def __init__(101        self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01102    ):103        super(ResEncoderBlock, self).__init__()104        self.n_blocks = n_blocks105        self.conv = nn.ModuleList()106        self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))107        for i in range(n_blocks - 1):108            self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))109        self.kernel_size = kernel_size110        if self.kernel_size is not None:111            self.pool = nn.AvgPool2d(kernel_size=kernel_size)112 113    def forward(self, x):114        for i in range(self.n_blocks):115            x = self.conv[i](x)116        if self.kernel_size is not None:117            return x, self.pool(x)118        else:119            return x120 121 122class Intermediate(nn.Module):  #123    def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):124        super(Intermediate, self).__init__()125        self.n_inters = n_inters126        self.layers = nn.ModuleList()127        self.layers.append(128            ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum)129        )130        for i in range(self.n_inters - 1):131            self.layers.append(132                ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum)133            )134 135    def forward(self, x):136        for i in range(self.n_inters):137            x = self.layers[i](x)138        return x139 140 141class ResDecoderBlock(nn.Module):142    def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):143        super(ResDecoderBlock, self).__init__()144        out_padding = (0, 1) if stride == (1, 2) else (1, 1)145        self.n_blocks = n_blocks146        self.conv1 = nn.Sequential(147            nn.ConvTranspose2d(148                in_channels=in_channels,149                out_channels=out_channels,150                kernel_size=(3, 3),151                stride=stride,152                padding=(1, 1),153                output_padding=out_padding,154                bias=False,155            ),156            nn.BatchNorm2d(out_channels, momentum=momentum),157            nn.ReLU(),158        )159        self.conv2 = nn.ModuleList()160        self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))161        for i in range(n_blocks - 1):162            self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))163 164    def forward(self, x, concat_tensor):165        x = self.conv1(x)166        x = torch.cat((x, concat_tensor), dim=1)167        for i in range(self.n_blocks):168            x = self.conv2[i](x)169        return x170 171 172class Decoder(nn.Module):173    def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):174        super(Decoder, self).__init__()175        self.layers = nn.ModuleList()176        self.n_decoders = n_decoders177        for i in range(self.n_decoders):178            out_channels = in_channels // 2179            self.layers.append(180                ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum)181            )182            in_channels = out_channels183 184    def forward(self, x, concat_tensors):185        for i in range(self.n_decoders):186            x = self.layers[i](x, concat_tensors[-1 - i])187        return x188 189 190class DeepUnet(nn.Module):191    def __init__(192        self,193        kernel_size,194        n_blocks,195        en_de_layers=5,196        inter_layers=4,197        in_channels=1,198        en_out_channels=16,199    ):200        super(DeepUnet, self).__init__()201        self.encoder = Encoder(202            in_channels, 128, en_de_layers, kernel_size, n_blocks, en_out_channels203        )204        self.intermediate = Intermediate(205            self.encoder.out_channel // 2,206            self.encoder.out_channel,207            inter_layers,208            n_blocks,209        )210        self.decoder = Decoder(211            self.encoder.out_channel, en_de_layers, kernel_size, n_blocks212        )213 214    def forward(self, x):215        x, concat_tensors = self.encoder(x)216        x = self.intermediate(x)217        x = self.decoder(x, concat_tensors)218        return x219 220 221class E2E(nn.Module):222    def __init__(223        self,224        n_blocks,225        n_gru,226        kernel_size,227        en_de_layers=5,228        inter_layers=4,229        in_channels=1,230        en_out_channels=16,231    ):232        super(E2E, self).__init__()233        self.unet = DeepUnet(234            kernel_size,235            n_blocks,236            en_de_layers,237            inter_layers,238            in_channels,239            en_out_channels,240        )241        self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))242        if n_gru:243            self.fc = nn.Sequential(244                BiGRU(3 * 128, 256, n_gru),245                nn.Linear(512, 360),246                nn.Dropout(0.25),247                nn.Sigmoid(),248            )249        else:250            self.fc = nn.Sequential(251                nn.Linear(3 * N_MELS, N_CLASS), nn.Dropout(0.25), nn.Sigmoid()252            )253 254    def forward(self, mel):255        mel = mel.transpose(-1, -2).unsqueeze(1)256        x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)257        x = self.fc(x)258        return x259 260 261class MelSpectrogram(torch.nn.Module):262    def __init__(263        self,264        is_half,265        n_mel_channels,266        sampling_rate,267        win_length,268        hop_length,269        n_fft=None,270        mel_fmin=0,271        mel_fmax=None,272        clamp=1e-5,273    ):274        super().__init__()275        n_fft = win_length if n_fft is None else n_fft276        self.hann_window = {}277        mel_basis = mel(278            sr=sampling_rate,279            n_fft=n_fft,280            n_mels=n_mel_channels,281            fmin=mel_fmin,282            fmax=mel_fmax,283            htk=True,284        )285        mel_basis = torch.from_numpy(mel_basis).float()286        self.register_buffer("mel_basis", mel_basis)287        self.n_fft = win_length if n_fft is None else n_fft288        self.hop_length = hop_length289        self.win_length = win_length290        self.sampling_rate = sampling_rate291        self.n_mel_channels = n_mel_channels292        self.clamp = clamp293        self.is_half = is_half294 295    def forward(self, audio, keyshift=0, speed=1, center=True):296        factor = 2 ** (keyshift / 12)297        n_fft_new = int(np.round(self.n_fft * factor))298        win_length_new = int(np.round(self.win_length * factor))299        hop_length_new = int(np.round(self.hop_length * speed))300        keyshift_key = str(keyshift) + "_" + str(audio.device)301        if keyshift_key not in self.hann_window:302            self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(303                audio.device304            )305        fft = torch.stft(306            audio,307            n_fft=n_fft_new,308            hop_length=hop_length_new,309            win_length=win_length_new,310            window=self.hann_window[keyshift_key],311            center=center,312            return_complex=True,313        )314        magnitude = torch.sqrt(fft.real.pow(2) + fft.imag.pow(2))315        if keyshift != 0:316            size = self.n_fft // 2 + 1317            resize = magnitude.size(1)318            if resize < size:319                magnitude = F.pad(magnitude, (0, 0, 0, size - resize))320            magnitude = magnitude[:, :size, :] * self.win_length / win_length_new321        mel_output = torch.matmul(self.mel_basis, magnitude)322        if self.is_half == True:323            mel_output = mel_output.half()324        log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))325        return log_mel_spec326 327 328class RMVPE:329    def __init__(self, model_path, is_half, device=None):330        self.resample_kernel = {}331        model = E2E(4, 1, (2, 2))332        ckpt = torch.load(model_path, map_location="cpu")333        model.load_state_dict(ckpt)334        model.eval()335        if is_half == True:336            model = model.half()337        self.model = model338        self.resample_kernel = {}339        self.is_half = is_half340        if device is None:341            device = "cuda" if torch.cuda.is_available() else "cpu"342        self.device = device343        self.mel_extractor = MelSpectrogram(344            is_half, 128, 16000, 1024, 160, None, 30, 8000345        ).to(device)346        self.model = self.model.to(device)347        cents_mapping = 20 * np.arange(360) + 1997.3794084376191348        self.cents_mapping = np.pad(cents_mapping, (4, 4))  # 368349 350    def mel2hidden(self, mel):351        with torch.no_grad():352            n_frames = mel.shape[-1]353            mel = F.pad(354                mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"355            )356            hidden = self.model(mel)357            return hidden[:, :n_frames]358 359    def decode(self, hidden, thred=0.03):360        cents_pred = self.to_local_average_cents(hidden, thred=thred)361        f0 = 10 * (2 ** (cents_pred / 1200))362        f0[f0 == 10] = 0363        # f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])364        return f0365 366    def infer_from_audio(self, audio, thred=0.03):367        audio = torch.from_numpy(audio).float().to(self.device).unsqueeze(0)368        # torch.cuda.synchronize()369        # t0=ttime()370        mel = self.mel_extractor(audio, center=True)371        # torch.cuda.synchronize()372        # t1=ttime()373        hidden = self.mel2hidden(mel)374        # torch.cuda.synchronize()375        # t2=ttime()376        hidden = hidden.squeeze(0).cpu().numpy()377        if self.is_half == True:378            hidden = hidden.astype("float32")379        f0 = self.decode(hidden, thred=thred)380        # torch.cuda.synchronize()381        # t3=ttime()382        # print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))383        return f0384 385    def to_local_average_cents(self, salience, thred=0.05):386        # t0 = ttime()387        center = np.argmax(salience, axis=1)  # 帧长#index388        salience = np.pad(salience, ((0, 0), (4, 4)))  # 帧长,368389        # t1 = ttime()390        center += 4391        todo_salience = []392        todo_cents_mapping = []393        starts = center - 4394        ends = center + 5395        for idx in range(salience.shape[0]):396            todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])397            todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])398        # t2 = ttime()399        todo_salience = np.array(todo_salience)  # 帧长，9400        todo_cents_mapping = np.array(todo_cents_mapping)  # 帧长，9401        product_sum = np.sum(todo_salience * todo_cents_mapping, 1)402        weight_sum = np.sum(todo_salience, 1)  # 帧长403        devided = product_sum / weight_sum  # 帧长404        # t3 = ttime()405        maxx = np.max(salience, axis=1)  # 帧长406        devided[maxx <= thred] = 0407        # t4 = ttime()408        # print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))409        return devided410