rvc/lib/algorithm/encoders.py

import math
import torch
from typing import Optional

from rvc.lib.algorithm.commons import sequence_mask
from rvc.lib.algorithm.modules import WaveNet
from rvc.lib.algorithm.normalization import LayerNorm
from rvc.lib.algorithm.attentions import FFN, MultiHeadAttention


class Encoder(torch.nn.Module):
    """
    Encoder module for the Transformer model.

    Args:
        hidden_channels (int): Number of hidden channels in the encoder.
        filter_channels (int): Number of filter channels in the feed-forward network.
        n_heads (int): Number of attention heads.
        n_layers (int): Number of encoder layers.
        kernel_size (int, optional): Kernel size of the convolution layers in the feed-forward network. Defaults to 1.
        p_dropout (float, optional): Dropout probability. Defaults to 0.0.
        window_size (int, optional): Window size for relative positional encoding. Defaults to 10.
    """

    def __init__(
        self,
        hidden_channels: int,
        filter_channels: int,
        n_heads: int,
        n_layers: int,
        kernel_size: int = 1,
        p_dropout: float = 0.0,
        window_size: int = 10,
    ):
        super().__init__()

        self.hidden_channels = hidden_channels
        self.n_layers = n_layers
        self.drop = torch.nn.Dropout(p_dropout)

        self.attn_layers = torch.nn.ModuleList(
            [
                MultiHeadAttention(
                    hidden_channels,
                    hidden_channels,
                    n_heads,
                    p_dropout=p_dropout,
                    window_size=window_size,
                )
                for _ in range(n_layers)
            ]
        )
        self.norm_layers_1 = torch.nn.ModuleList(
            [LayerNorm(hidden_channels) for _ in range(n_layers)]
        )
        self.ffn_layers = torch.nn.ModuleList(
            [
                FFN(
                    hidden_channels,
                    hidden_channels,
                    filter_channels,
                    kernel_size,
                    p_dropout=p_dropout,
                )
                for _ in range(n_layers)
            ]
        )
        self.norm_layers_2 = torch.nn.ModuleList(
            [LayerNorm(hidden_channels) for _ in range(n_layers)]
        )

    def forward(self, x, x_mask):
        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
        x = x * x_mask

        for i in range(self.n_layers):
            y = self.attn_layers[i](x, x, attn_mask)
            y = self.drop(y)
            x = self.norm_layers_1[i](x + y)

            y = self.ffn_layers[i](x, x_mask)
            y = self.drop(y)
            x = self.norm_layers_2[i](x + y)

        return x * x_mask


class TextEncoder(torch.nn.Module):
    """
    Text Encoder with configurable embedding dimension.

    Args:
        out_channels (int): Output channels of the encoder.
        hidden_channels (int): Hidden channels of the encoder.
        filter_channels (int): Filter channels of the encoder.
        n_heads (int): Number of attention heads.
        n_layers (int): Number of encoder layers.
        kernel_size (int): Kernel size of the convolutional layers.
        p_dropout (float): Dropout probability.
        embedding_dim (int): Embedding dimension for phone embeddings (v1 = 256, v2 = 768).
        f0 (bool, optional): Whether to use F0 embedding. Defaults to True.
    """

    def __init__(
        self,
        out_channels: int,
        hidden_channels: int,
        filter_channels: int,
        n_heads: int,
        n_layers: int,
        kernel_size: int,
        p_dropout: float,
        embedding_dim: int,
        f0: bool = True,
    ):
        super().__init__()
        self.hidden_channels = hidden_channels
        self.out_channels = out_channels
        self.emb_phone = torch.nn.Linear(embedding_dim, hidden_channels)
        self.lrelu = torch.nn.LeakyReLU(0.1, inplace=True)
        self.emb_pitch = torch.nn.Embedding(256, hidden_channels) if f0 else None

        self.encoder = Encoder(
            hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout
        )
        self.proj = torch.nn.Conv1d(hidden_channels, out_channels * 2, 1)

    def forward(
        self, phone: torch.Tensor, pitch: Optional[torch.Tensor], lengths: torch.Tensor
    ):
        x = self.emb_phone(phone)
        if pitch is not None and self.emb_pitch:
            x += self.emb_pitch(pitch)

        x *= math.sqrt(self.hidden_channels)
        x = self.lrelu(x)
        x = x.transpose(1, -1)  # [B, H, T]

        x_mask = sequence_mask(lengths, x.size(2)).unsqueeze(1).to(x.dtype)
        x = self.encoder(x, x_mask)
        stats = self.proj(x) * x_mask

        m, logs = torch.split(stats, self.out_channels, dim=1)
        return m, logs, x_mask


class PosteriorEncoder(torch.nn.Module):
    """
    Posterior Encoder for inferring latent representation.

    Args:
        in_channels (int): Number of channels in the input.
        out_channels (int): Number of channels in the output.
        hidden_channels (int): Number of hidden channels in the encoder.
        kernel_size (int): Kernel size of the convolutional layers.
        dilation_rate (int): Dilation rate of the convolutional layers.
        n_layers (int): Number of layers in the encoder.
        gin_channels (int, optional): Number of channels for the global conditioning input. Defaults to 0.
    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        hidden_channels: int,
        kernel_size: int,
        dilation_rate: int,
        n_layers: int,
        gin_channels: int = 0,
    ):
        super().__init__()
        self.out_channels = out_channels
        self.pre = torch.nn.Conv1d(in_channels, hidden_channels, 1)
        self.enc = WaveNet(
            hidden_channels,
            kernel_size,
            dilation_rate,
            n_layers,
            gin_channels=gin_channels,
        )
        self.proj = torch.nn.Conv1d(hidden_channels, out_channels * 2, 1)

    def forward(
        self, x: torch.Tensor, x_lengths: torch.Tensor, g: Optional[torch.Tensor] = None
    ):
        x_mask = sequence_mask(x_lengths, x.size(2)).unsqueeze(1).to(x.dtype)

        x = self.pre(x) * x_mask
        x = self.enc(x, x_mask, g=g)

        stats = self.proj(x) * x_mask
        m, logs = torch.split(stats, self.out_channels, dim=1)

        z = m + torch.randn_like(m) * torch.exp(logs)
        z *= x_mask

        return z, m, logs, x_mask

    def remove_weight_norm(self):
        self.enc.remove_weight_norm()

    def __prepare_scriptable__(self):
        for hook in self.enc._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)
        return self