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import torch |
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from typing import Optional |
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from rvc.lib.algorithm.generators.hifigan_mrf import HiFiGANMRFGenerator |
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from rvc.lib.algorithm.generators.hifigan_nsf import HiFiGANNSFGenerator |
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from rvc.lib.algorithm.generators.hifigan import HiFiGANGenerator |
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from rvc.lib.algorithm.generators.refinegan import RefineGANGenerator |
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from rvc.lib.algorithm.commons import slice_segments, rand_slice_segments |
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from rvc.lib.algorithm.residuals import ResidualCouplingBlock |
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from rvc.lib.algorithm.encoders import TextEncoder, PosteriorEncoder |
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class Synthesizer(torch.nn.Module): |
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""" |
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Base Synthesizer model. |
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Args: |
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spec_channels (int): Number of channels in the spectrogram. |
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segment_size (int): Size of the audio segment. |
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inter_channels (int): Number of channels in the intermediate layers. |
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hidden_channels (int): Number of channels in the hidden layers. |
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filter_channels (int): Number of channels in the filter layers. |
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n_heads (int): Number of attention heads. |
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n_layers (int): Number of layers in the encoder. |
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kernel_size (int): Size of the convolution kernel. |
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p_dropout (float): Dropout probability. |
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resblock (str): Type of residual block. |
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resblock_kernel_sizes (list): Kernel sizes for the residual blocks. |
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resblock_dilation_sizes (list): Dilation sizes for the residual blocks. |
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upsample_rates (list): Upsampling rates for the decoder. |
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upsample_initial_channel (int): Number of channels in the initial upsampling layer. |
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upsample_kernel_sizes (list): Kernel sizes for the upsampling layers. |
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spk_embed_dim (int): Dimension of the speaker embedding. |
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gin_channels (int): Number of channels in the global conditioning vector. |
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sr (int): Sampling rate of the audio. |
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use_f0 (bool): Whether to use F0 information. |
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text_enc_hidden_dim (int): Hidden dimension for the text encoder. |
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kwargs: Additional keyword arguments. |
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""" |
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def __init__( |
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self, |
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spec_channels: int, |
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segment_size: int, |
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inter_channels: int, |
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hidden_channels: int, |
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filter_channels: int, |
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n_heads: int, |
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n_layers: int, |
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kernel_size: int, |
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p_dropout: float, |
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resblock: str, |
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resblock_kernel_sizes: list, |
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resblock_dilation_sizes: list, |
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upsample_rates: list, |
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upsample_initial_channel: int, |
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upsample_kernel_sizes: list, |
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spk_embed_dim: int, |
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gin_channels: int, |
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sr: int, |
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use_f0: bool, |
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text_enc_hidden_dim: int = 768, |
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vocoder: str = "HiFi-GAN", |
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randomized: bool = True, |
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checkpointing: bool = False, |
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**kwargs, |
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): |
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super().__init__() |
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self.segment_size = segment_size |
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self.use_f0 = use_f0 |
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self.randomized = randomized |
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self.enc_p = TextEncoder( |
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inter_channels, |
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hidden_channels, |
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filter_channels, |
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n_heads, |
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n_layers, |
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kernel_size, |
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p_dropout, |
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text_enc_hidden_dim, |
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f0=use_f0, |
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) |
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print(f"Using {vocoder} vocoder") |
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if use_f0: |
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if vocoder == "MRF HiFi-GAN": |
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self.dec = HiFiGANMRFGenerator( |
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in_channel=inter_channels, |
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upsample_initial_channel=upsample_initial_channel, |
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upsample_rates=upsample_rates, |
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upsample_kernel_sizes=upsample_kernel_sizes, |
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resblock_kernel_sizes=resblock_kernel_sizes, |
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resblock_dilations=resblock_dilation_sizes, |
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gin_channels=gin_channels, |
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sample_rate=sr, |
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harmonic_num=8, |
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checkpointing=checkpointing, |
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) |
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elif vocoder == "RefineGAN": |
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self.dec = RefineGANGenerator( |
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sample_rate=sr, |
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downsample_rates=upsample_rates[::-1], |
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upsample_rates=upsample_rates, |
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start_channels=16, |
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num_mels=inter_channels, |
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checkpointing=checkpointing, |
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) |
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else: |
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self.dec = HiFiGANNSFGenerator( |
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inter_channels, |
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resblock_kernel_sizes, |
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resblock_dilation_sizes, |
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upsample_rates, |
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upsample_initial_channel, |
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upsample_kernel_sizes, |
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gin_channels=gin_channels, |
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sr=sr, |
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checkpointing=checkpointing, |
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) |
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else: |
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if vocoder == "MRF HiFi-GAN": |
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print("MRF HiFi-GAN does not support training without pitch guidance.") |
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self.dec = None |
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elif vocoder == "RefineGAN": |
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print("RefineGAN does not support training without pitch guidance.") |
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self.dec = None |
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else: |
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self.dec = HiFiGANGenerator( |
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inter_channels, |
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resblock_kernel_sizes, |
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resblock_dilation_sizes, |
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upsample_rates, |
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upsample_initial_channel, |
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upsample_kernel_sizes, |
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gin_channels=gin_channels, |
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checkpointing=checkpointing, |
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) |
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self.enc_q = PosteriorEncoder( |
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spec_channels, |
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inter_channels, |
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hidden_channels, |
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5, |
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1, |
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16, |
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gin_channels=gin_channels, |
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) |
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self.flow = ResidualCouplingBlock( |
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inter_channels, |
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hidden_channels, |
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5, |
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1, |
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3, |
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gin_channels=gin_channels, |
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) |
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self.emb_g = torch.nn.Embedding(spk_embed_dim, gin_channels) |
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def _remove_weight_norm_from(self, module): |
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for hook in module._forward_pre_hooks.values(): |
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if getattr(hook, "__class__", None).__name__ == "WeightNorm": |
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torch.nn.utils.remove_weight_norm(module) |
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def remove_weight_norm(self): |
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for module in [self.dec, self.flow, self.enc_q]: |
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self._remove_weight_norm_from(module) |
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def __prepare_scriptable__(self): |
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self.remove_weight_norm() |
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return self |
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def forward( |
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self, |
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phone: torch.Tensor, |
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phone_lengths: torch.Tensor, |
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pitch: Optional[torch.Tensor] = None, |
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pitchf: Optional[torch.Tensor] = None, |
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y: Optional[torch.Tensor] = None, |
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y_lengths: Optional[torch.Tensor] = None, |
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ds: Optional[torch.Tensor] = None, |
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): |
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g = self.emb_g(ds).unsqueeze(-1) |
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m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) |
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if y is not None: |
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z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) |
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z_p = self.flow(z, y_mask, g=g) |
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if self.randomized: |
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z_slice, ids_slice = rand_slice_segments( |
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z, y_lengths, self.segment_size |
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) |
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if self.use_f0: |
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pitchf = slice_segments(pitchf, ids_slice, self.segment_size, 2) |
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o = self.dec(z_slice, pitchf, g=g) |
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else: |
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o = self.dec(z_slice, g=g) |
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return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) |
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else: |
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if self.use_f0: |
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o = self.dec(z, pitchf, g=g) |
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else: |
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o = self.dec(z, g=g) |
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return o, None, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) |
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else: |
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return None, None, x_mask, None, (None, None, m_p, logs_p, None, None) |
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@torch.jit.export |
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def infer( |
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self, |
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phone: torch.Tensor, |
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phone_lengths: torch.Tensor, |
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pitch: Optional[torch.Tensor] = None, |
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nsff0: Optional[torch.Tensor] = None, |
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sid: torch.Tensor = None, |
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rate: Optional[torch.Tensor] = None, |
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): |
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""" |
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Inference of the model. |
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Args: |
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phone (torch.Tensor): Phoneme sequence. |
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phone_lengths (torch.Tensor): Lengths of the phoneme sequences. |
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pitch (torch.Tensor, optional): Pitch sequence. |
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nsff0 (torch.Tensor, optional): Fine-grained pitch sequence. |
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sid (torch.Tensor): Speaker embedding. |
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rate (torch.Tensor, optional): Rate for time-stretching. |
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""" |
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g = self.emb_g(sid).unsqueeze(-1) |
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m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) |
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z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask |
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if rate is not None: |
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head = int(z_p.shape[2] * (1.0 - rate.item())) |
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z_p, x_mask = z_p[:, :, head:], x_mask[:, :, head:] |
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if self.use_f0 and nsff0 is not None: |
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nsff0 = nsff0[:, head:] |
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z = self.flow(z_p, x_mask, g=g, reverse=True) |
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o = ( |
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self.dec(z * x_mask, nsff0, g=g) |
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if self.use_f0 |
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else self.dec(z * x_mask, g=g) |
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) |
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return o, x_mask, (z, z_p, m_p, logs_p) |
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