espnet2.gan_svs.vits.prior_decoder.PriorDecoder

source

class espnet2.gan_svs.vits.prior_decoder.PriorDecoder(out_channels: int = 384, attention_dim: int = 192, attention_heads: int = 2, linear_units: int = 768, blocks: int = 6, positionwise_layer_type: str = 'conv1d', positionwise_conv_kernel_size: int = 3, positional_encoding_layer_type: str = 'rel_pos', self_attention_layer_type: str = 'rel_selfattn', activation_type: str = 'swish', normalize_before: bool = True, use_macaron_style: bool = False, use_conformer_conv: bool = False, conformer_kernel_size: int = 7, dropout_rate: float = 0.1, positional_dropout_rate: float = 0.0, attention_dropout_rate: float = 0.0, global_channels: int = 0)

Bases: Module

Initialize prior decoder module.

• Parameters:
  • out_channels (int) – Output channels of the prior decoder. Defaults to 384.
  • attention_dim (int) – Dimension of the attention mechanism. Defaults to 192.
  • attention_heads (int) – Number of attention heads. Defaults to 2.
  • linear_units (int) – Number of units in the linear layer. Defaults to 768.
  • blocks (int) – Number of blocks in the encoder. Defaults to 6.
  • positionwise_layer_type (str) – Type of the positionwise layer. Defaults to “conv1d”.
  • positionwise_conv_kernel_size (int) – Kernel size of the positionwise convolutional layer. Defaults to 3.
  • positional_encoding_layer_type (str) – Type of positional encoding layer. Defaults to “rel_pos”.
  • self_attention_layer_type (str) – Type of self-attention layer. Defaults to “rel_selfattn”.
  • activation_type (str) – Type of activation. Defaults to “swish”.
  • normalize_before (bool) – Flag for normalization. Defaults to True.
  • use_macaron_style (bool) – Flag for macaron style. Defaults to False.
  • use_conformer_conv (bool) – Flag for using conformer convolution. Defaults to False.
  • conformer_kernel_size (int) – Kernel size for conformer convolution. Defaults to 7.
  • dropout_rate (float) – Dropout rate. Defaults to 0.1.
  • positional_dropout_rate (float) – Dropout rate for positional encoding. Defaults to 0.0.
  • attention_dropout_rate (float) – Dropout rate for attention. Defaults to 0.0.
  • global_channels (int) – Number of global channels. Defaults to 0.

forward(x, x_lengths, g=None)

Forward pass of the PriorDecoder module.

• Parameters:
  • x (Tensor) – Input tensor (B, attention_dim + 2, T).
  • x_lengths (Tensor) – Length tensor (B,).
  • g (Tensor) – Tensor for multi-singer. (B, global_channels, 1)
• Returns: Output tensor (B, out_channels, T). Tensor: Output mask tensor (B, 1, T).
• Return type: Tensor