Spaces:
Running
Running
| # 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() | |