| import torch |
| from typing import Optional |
|
|
| from programs.applio_code.rvc.lib.algorithm.nsf import GeneratorNSF |
| from programs.applio_code.rvc.lib.algorithm.generators import Generator |
| from programs.applio_code.rvc.lib.algorithm.commons import ( |
| slice_segments, |
| rand_slice_segments, |
| ) |
| from programs.applio_code.rvc.lib.algorithm.residuals import ResidualCouplingBlock |
| from programs.applio_code.rvc.lib.algorithm.encoders import ( |
| TextEncoder, |
| PosteriorEncoder, |
| ) |
|
|
|
|
| class Synthesizer(torch.nn.Module): |
| """ |
| Base Synthesizer model. |
| |
| Args: |
| spec_channels (int): Number of channels in the spectrogram. |
| segment_size (int): Size of the audio segment. |
| inter_channels (int): Number of channels in the intermediate layers. |
| hidden_channels (int): Number of channels in the hidden layers. |
| filter_channels (int): Number of channels in the filter layers. |
| n_heads (int): Number of attention heads. |
| n_layers (int): Number of layers in the encoder. |
| kernel_size (int): Size of the convolution kernel. |
| p_dropout (float): Dropout probability. |
| resblock (str): Type of residual block. |
| resblock_kernel_sizes (list): Kernel sizes for the residual blocks. |
| resblock_dilation_sizes (list): Dilation sizes for the residual blocks. |
| upsample_rates (list): Upsampling rates for the decoder. |
| upsample_initial_channel (int): Number of channels in the initial upsampling layer. |
| upsample_kernel_sizes (list): Kernel sizes for the upsampling layers. |
| spk_embed_dim (int): Dimension of the speaker embedding. |
| gin_channels (int): Number of channels in the global conditioning vector. |
| sr (int): Sampling rate of the audio. |
| use_f0 (bool): Whether to use F0 information. |
| text_enc_hidden_dim (int): Hidden dimension for the text encoder. |
| kwargs: Additional keyword arguments. |
| """ |
|
|
| def __init__( |
| self, |
| spec_channels, |
| segment_size, |
| inter_channels, |
| hidden_channels, |
| filter_channels, |
| n_heads, |
| n_layers, |
| kernel_size, |
| p_dropout, |
| resblock, |
| resblock_kernel_sizes, |
| resblock_dilation_sizes, |
| upsample_rates, |
| upsample_initial_channel, |
| upsample_kernel_sizes, |
| spk_embed_dim, |
| gin_channels, |
| sr, |
| use_f0, |
| text_enc_hidden_dim=768, |
| **kwargs |
| ): |
| super(Synthesizer, self).__init__() |
| self.spec_channels = spec_channels |
| self.inter_channels = inter_channels |
| self.hidden_channels = hidden_channels |
| self.filter_channels = filter_channels |
| self.n_heads = n_heads |
| self.n_layers = n_layers |
| self.kernel_size = kernel_size |
| self.p_dropout = float(p_dropout) |
| self.resblock = resblock |
| self.resblock_kernel_sizes = resblock_kernel_sizes |
| self.resblock_dilation_sizes = resblock_dilation_sizes |
| self.upsample_rates = upsample_rates |
| self.upsample_initial_channel = upsample_initial_channel |
| self.upsample_kernel_sizes = upsample_kernel_sizes |
| self.segment_size = segment_size |
| self.gin_channels = gin_channels |
| self.spk_embed_dim = spk_embed_dim |
| self.use_f0 = use_f0 |
|
|
| self.enc_p = TextEncoder( |
| inter_channels, |
| hidden_channels, |
| filter_channels, |
| n_heads, |
| n_layers, |
| kernel_size, |
| float(p_dropout), |
| text_enc_hidden_dim, |
| f0=use_f0, |
| ) |
|
|
| if use_f0: |
| self.dec = GeneratorNSF( |
| inter_channels, |
| resblock, |
| resblock_kernel_sizes, |
| resblock_dilation_sizes, |
| upsample_rates, |
| upsample_initial_channel, |
| upsample_kernel_sizes, |
| gin_channels=gin_channels, |
| sr=sr, |
| is_half=kwargs["is_half"], |
| ) |
| else: |
| self.dec = Generator( |
| inter_channels, |
| resblock, |
| resblock_kernel_sizes, |
| resblock_dilation_sizes, |
| upsample_rates, |
| upsample_initial_channel, |
| upsample_kernel_sizes, |
| gin_channels=gin_channels, |
| ) |
|
|
| self.enc_q = PosteriorEncoder( |
| spec_channels, |
| inter_channels, |
| hidden_channels, |
| 5, |
| 1, |
| 16, |
| gin_channels=gin_channels, |
| ) |
| self.flow = ResidualCouplingBlock( |
| inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels |
| ) |
| self.emb_g = torch.nn.Embedding(self.spk_embed_dim, gin_channels) |
|
|
| def remove_weight_norm(self): |
| """Removes weight normalization from the model.""" |
| self.dec.remove_weight_norm() |
| self.flow.remove_weight_norm() |
| self.enc_q.remove_weight_norm() |
|
|
| def __prepare_scriptable__(self): |
| for hook in self.dec._forward_pre_hooks.values(): |
| if ( |
| hook.__module__ == "torch.nn.utils.parametrizations.weight_norm" |
| and hook.__class__.__name__ == "WeightNorm" |
| ): |
| torch.nn.utils.remove_weight_norm(self.dec) |
| for hook in self.flow._forward_pre_hooks.values(): |
| if ( |
| hook.__module__ == "torch.nn.utils.parametrizations.weight_norm" |
| and hook.__class__.__name__ == "WeightNorm" |
| ): |
| torch.nn.utils.remove_weight_norm(self.flow) |
| if hasattr(self, "enc_q"): |
| for hook in self.enc_q._forward_pre_hooks.values(): |
| if ( |
| hook.__module__ == "torch.nn.utils.parametrizations.weight_norm" |
| and hook.__class__.__name__ == "WeightNorm" |
| ): |
| torch.nn.utils.remove_weight_norm(self.enc_q) |
| return self |
|
|
| @torch.jit.ignore |
| def forward( |
| self, |
| phone: torch.Tensor, |
| phone_lengths: torch.Tensor, |
| pitch: Optional[torch.Tensor] = None, |
| pitchf: Optional[torch.Tensor] = None, |
| y: torch.Tensor = None, |
| y_lengths: torch.Tensor = None, |
| ds: Optional[torch.Tensor] = None, |
| ): |
| """ |
| Forward pass of the model. |
| |
| Args: |
| phone (torch.Tensor): Phoneme sequence. |
| phone_lengths (torch.Tensor): Lengths of the phoneme sequences. |
| pitch (torch.Tensor, optional): Pitch sequence. |
| pitchf (torch.Tensor, optional): Fine-grained pitch sequence. |
| y (torch.Tensor, optional): Target spectrogram. |
| y_lengths (torch.Tensor, optional): Lengths of the target spectrograms. |
| ds (torch.Tensor, optional): Speaker embedding. Defaults to None. |
| """ |
| g = self.emb_g(ds).unsqueeze(-1) |
| m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) |
| if y is not None: |
| z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) |
| z_p = self.flow(z, y_mask, g=g) |
| z_slice, ids_slice = rand_slice_segments(z, y_lengths, self.segment_size) |
| if self.use_f0: |
| pitchf = slice_segments(pitchf, ids_slice, self.segment_size, 2) |
| o = self.dec(z_slice, pitchf, g=g) |
| else: |
| o = self.dec(z_slice, g=g) |
| return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) |
| else: |
| return None, None, x_mask, None, (None, None, m_p, logs_p, None, None) |
|
|
| @torch.jit.export |
| def infer( |
| self, |
| phone: torch.Tensor, |
| phone_lengths: torch.Tensor, |
| pitch: Optional[torch.Tensor] = None, |
| nsff0: Optional[torch.Tensor] = None, |
| sid: torch.Tensor = None, |
| rate: Optional[torch.Tensor] = None, |
| ): |
| """ |
| Inference of the model. |
| |
| Args: |
| phone (torch.Tensor): Phoneme sequence. |
| phone_lengths (torch.Tensor): Lengths of the phoneme sequences. |
| pitch (torch.Tensor, optional): Pitch sequence. |
| nsff0 (torch.Tensor, optional): Fine-grained pitch sequence. |
| sid (torch.Tensor): Speaker embedding. |
| rate (torch.Tensor, optional): Rate for time-stretching. Defaults to None. |
| """ |
| g = self.emb_g(sid).unsqueeze(-1) |
| m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) |
| z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask |
| if rate is not None: |
| assert isinstance(rate, torch.Tensor) |
| head = int(z_p.shape[2] * (1.0 - rate.item())) |
| z_p = z_p[:, :, head:] |
| x_mask = x_mask[:, :, head:] |
| if self.use_f0: |
| nsff0 = nsff0[:, head:] |
| if self.use_f0: |
| z = self.flow(z_p, x_mask, g=g, reverse=True) |
| o = self.dec(z * x_mask, nsff0, g=g) |
| else: |
| z = self.flow(z_p, x_mask, g=g, reverse=True) |
| o = self.dec(z * x_mask, g=g) |
| return o, x_mask, (z, z_p, m_p, logs_p) |
|
|
