repo_description: A WebUI to create song covers with any RVC v2 trained AI voice from YouTube videos or audio files.
repo_topics: ai rvc song-covers uvr webui
code: import numpy as np
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from librosa.filters import mel
  
  
  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
  
  
  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
  