Spaces:
Runtime error
Runtime error
| # MIT License | |
| # Copyright (c) Meta Platforms, Inc. and affiliates. | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # The above copyright notice and this permission notice shall be included in all | |
| # copies or substantial portions of the Software. | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| # SOFTWARE. | |
| # Copyright (c) [2023] [Meta Platforms, Inc. and affiliates.] | |
| # Copyright (c) [2025] [Ziyue Jiang] | |
| # SPDX-License-Identifier: MIT | |
| # This file has been modified by Ziyue Jiang on 2025/03/19 | |
| # Original file was released under MIT, with the full license text # available at https://github.com/facebookresearch/encodec/blob/gh-pages/LICENSE. | |
| # This modified file is released under the same license. | |
| """Encodec SEANet-based encoder and decoder implementation.""" | |
| import typing as tp | |
| import numpy as np | |
| import torch.nn as nn | |
| from .conv import SConv1d | |
| from .lstm import SLSTM | |
| class SEANetResnetBlock(nn.Module): | |
| 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): | |
| 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) |