# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. """Encodec SEANet-based encoder and decoder implementation.""" import typing as tp import numpy as np import torch.nn as nn from . import ( SConv1d, SConvTranspose1d, SLSTM ) class SEANetResnetBlock(nn.Module): """Residual block from SEANet model. Args: dim (int): Dimension of the input/output kernel_sizes (list): List of kernel sizes for the convolutions. dilations (list): List of dilations for the convolutions. activation (str): Activation function. activation_params (dict): Parameters to provide to the activation function norm (str): Normalization method. norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution. causal (bool): Whether to use fully causal convolution. pad_mode (str): Padding mode for the convolutions. compress (int): Reduced dimensionality in residual branches (from Demucs v3) true_skip (bool): Whether to use true skip connection or a simple convolution as the skip connection. """ def __init__(self, dim: int, kernel_sizes: tp.List[int] = [3, 1], dilations: tp.List[int] = [1, 1], activation: str = 'ELU', activation_params: dict = {'alpha': 1.0}, norm: str = 'weight_norm', norm_params: tp.Dict[str, tp.Any] = {}, causal: bool = False, pad_mode: str = 'reflect', compress: int = 2, true_skip: bool = True): super().__init__() assert len(kernel_sizes) == len(dilations), 'Number of kernel sizes should match number of dilations' act = getattr(nn, activation) hidden = dim // compress block = [] for i, (kernel_size, dilation) in enumerate(zip(kernel_sizes, dilations)): in_chs = dim if i == 0 else hidden out_chs = dim if i == len(kernel_sizes) - 1 else hidden block += [ act(**activation_params), SConv1d(in_chs, out_chs, kernel_size=kernel_size, dilation=dilation, norm=norm, norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode), ] self.block = nn.Sequential(*block) self.shortcut: nn.Module if true_skip: self.shortcut = nn.Identity() else: self.shortcut = SConv1d(dim, dim, kernel_size=1, norm=norm, norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode) def forward(self, x): return self.shortcut(x) + self.block(x) class SEANetEncoder(nn.Module): """SEANet encoder. Args: channels (int): Audio channels. dimension (int): Intermediate representation dimension. n_filters (int): Base width for the model. n_residual_layers (int): nb of residual layers. ratios (Sequence[int]): kernel size and stride ratios. The encoder uses downsampling ratios instead of upsampling ratios, hence it will use the ratios in the reverse order to the ones specified here that must match the decoder order activation (str): Activation function. activation_params (dict): Parameters to provide to the activation function norm (str): Normalization method. norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution. kernel_size (int): Kernel size for the initial convolution. last_kernel_size (int): Kernel size for the initial convolution. residual_kernel_size (int): Kernel size for the residual layers. dilation_base (int): How much to increase the dilation with each layer. causal (bool): Whether to use fully causal convolution. pad_mode (str): Padding mode for the convolutions. true_skip (bool): Whether to use true skip connection or a simple (streamable) convolution as the skip connection in the residual network blocks. compress (int): Reduced dimensionality in residual branches (from Demucs v3). lstm (int): Number of LSTM layers at the end of the encoder. """ def __init__(self, channels: int = 1, dimension: int = 128, n_filters: int = 32, n_residual_layers: int = 1, ratios: tp.List[int] = [8, 5, 4, 2], activation: str = 'ELU', activation_params: dict = {'alpha': 1.0}, norm: str = 'weight_norm', norm_params: tp.Dict[str, tp.Any] = {}, kernel_size: int = 7, last_kernel_size: int = 7, residual_kernel_size: int = 3, dilation_base: int = 2, causal: bool = False, pad_mode: str = 'reflect', true_skip: bool = False, compress: int = 2, lstm: int = 2): super().__init__() self.channels = channels self.dimension = dimension self.n_filters = n_filters self.ratios = list(reversed(ratios)) del ratios self.n_residual_layers = n_residual_layers self.hop_length = np.prod(self.ratios) act = getattr(nn, activation) mult = 1 model: tp.List[nn.Module] = [ SConv1d(channels, mult * n_filters, kernel_size, norm=norm, norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode) ] # Downsample to raw audio scale for i, ratio in enumerate(self.ratios): # Add residual layers for j in range(n_residual_layers): model += [ SEANetResnetBlock(mult * n_filters, kernel_sizes=[residual_kernel_size, 1], dilations=[dilation_base ** j, 1], norm=norm, norm_params=norm_params, activation=activation, activation_params=activation_params, causal=causal, pad_mode=pad_mode, compress=compress, true_skip=true_skip)] # Add downsampling layers model += [ act(**activation_params), SConv1d(mult * n_filters, mult * n_filters * 2, kernel_size=ratio * 2, stride=ratio, norm=norm, norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode), ] mult *= 2 if lstm: model += [SLSTM(mult * n_filters, num_layers=lstm)] model += [ act(**activation_params), SConv1d(mult * n_filters, dimension, last_kernel_size, norm=norm, norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode) ] self.model = nn.Sequential(*model) def forward(self, x): return self.model(x) class SEANetDecoder(nn.Module): """SEANet decoder. Args: channels (int): Audio channels. dimension (int): Intermediate representation dimension. n_filters (int): Base width for the model. n_residual_layers (int): nb of residual layers. ratios (Sequence[int]): kernel size and stride ratios activation (str): Activation function. activation_params (dict): Parameters to provide to the activation function final_activation (str): Final activation function after all convolutions. final_activation_params (dict): Parameters to provide to the activation function norm (str): Normalization method. norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution. kernel_size (int): Kernel size for the initial convolution. last_kernel_size (int): Kernel size for the initial convolution. residual_kernel_size (int): Kernel size for the residual layers. dilation_base (int): How much to increase the dilation with each layer. causal (bool): Whether to use fully causal convolution. pad_mode (str): Padding mode for the convolutions. true_skip (bool): Whether to use true skip connection or a simple (streamable) convolution as the skip connection in the residual network blocks. compress (int): Reduced dimensionality in residual branches (from Demucs v3). lstm (int): Number of LSTM layers at the end of the encoder. trim_right_ratio (float): Ratio for trimming at the right of the transposed convolution under the causal setup. If equal to 1.0, it means that all the trimming is done at the right. """ def __init__(self, channels: int = 1, dimension: int = 128, n_filters: int = 32, n_residual_layers: int = 1, ratios: tp.List[int] = [8, 5, 4, 2], activation: str = 'ELU', activation_params: dict = {'alpha': 1.0}, final_activation: tp.Optional[str] = None, final_activation_params: tp.Optional[dict] = None, norm: str = 'weight_norm', norm_params: tp.Dict[str, tp.Any] = {}, kernel_size: int = 7, last_kernel_size: int = 7, residual_kernel_size: int = 3, dilation_base: int = 2, causal: bool = False, pad_mode: str = 'reflect', true_skip: bool = False, compress: int = 2, lstm: int = 2, trim_right_ratio: float = 1.0): super().__init__() self.dimension = dimension self.channels = channels self.n_filters = n_filters self.ratios = ratios del ratios self.n_residual_layers = n_residual_layers self.hop_length = np.prod(self.ratios) act = getattr(nn, activation) mult = int(2 ** len(self.ratios)) model: tp.List[nn.Module] = [ SConv1d(dimension, mult * n_filters, kernel_size, norm=norm, norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode) ] if lstm: model += [SLSTM(mult * n_filters, num_layers=lstm)] # Upsample to raw audio scale for i, ratio in enumerate(self.ratios): # Add upsampling layers model += [ act(**activation_params), SConvTranspose1d(mult * n_filters, mult * n_filters // 2, kernel_size=ratio * 2, stride=ratio, norm=norm, norm_kwargs=norm_params, causal=causal, trim_right_ratio=trim_right_ratio), ] # Add residual layers for j in range(n_residual_layers): model += [ SEANetResnetBlock(mult * n_filters // 2, kernel_sizes=[residual_kernel_size, 1], dilations=[dilation_base ** j, 1], activation=activation, activation_params=activation_params, norm=norm, norm_params=norm_params, causal=causal, pad_mode=pad_mode, compress=compress, true_skip=true_skip)] mult //= 2 # Add final layers model += [ act(**activation_params), SConv1d(n_filters, channels, last_kernel_size, norm=norm, norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode) ] # Add optional final activation to decoder (eg. tanh) if final_activation is not None: final_act = getattr(nn, final_activation) final_activation_params = final_activation_params or {} model += [ final_act(**final_activation_params) ] self.model = nn.Sequential(*model) def forward(self, z): y = self.model(z) return y def test(): import torch encoder = SEANetEncoder() decoder = SEANetDecoder() x = torch.randn(1, 1, 24000) z = encoder(x) assert list(z.shape) == [1, 128, 75], z.shape y = decoder(z) assert y.shape == x.shape, (x.shape, y.shape) if __name__ == '__main__': test()