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| # 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. | |
| """MS-STFT discriminator, provided here for reference.""" | |
| import typing as tp | |
| import torch | |
| import torchaudio | |
| from einops import rearrange | |
| from torch import nn | |
| from academicodec.modules import NormConv2d | |
| FeatureMapType = tp.List[torch.Tensor] | |
| LogitsType = torch.Tensor | |
| DiscriminatorOutput = tp.Tuple[tp.List[LogitsType], tp.List[FeatureMapType]] | |
| def get_2d_padding(kernel_size: tp.Tuple[int, int], | |
| dilation: tp.Tuple[int, int]=(1, 1)): | |
| return (((kernel_size[0] - 1) * dilation[0]) // 2, ( | |
| (kernel_size[1] - 1) * dilation[1]) // 2) | |
| class DiscriminatorSTFT(nn.Module): | |
| """STFT sub-discriminator. | |
| Args: | |
| filters (int): Number of filters in convolutions | |
| in_channels (int): Number of input channels. Default: 1 | |
| out_channels (int): Number of output channels. Default: 1 | |
| n_fft (int): Size of FFT for each scale. Default: 1024 | |
| hop_length (int): Length of hop between STFT windows for each scale. Default: 256 | |
| kernel_size (tuple of int): Inner Conv2d kernel sizes. Default: ``(3, 9)`` | |
| stride (tuple of int): Inner Conv2d strides. Default: ``(1, 2)`` | |
| dilations (list of int): Inner Conv2d dilation on the time dimension. Default: ``[1, 2, 4]`` | |
| win_length (int): Window size for each scale. Default: 1024 | |
| normalized (bool): Whether to normalize by magnitude after stft. Default: True | |
| norm (str): Normalization method. Default: `'weight_norm'` | |
| activation (str): Activation function. Default: `'LeakyReLU'` | |
| activation_params (dict): Parameters to provide to the activation function. | |
| growth (int): Growth factor for the filters. Default: 1 | |
| """ | |
| def __init__(self, | |
| filters: int, | |
| in_channels: int=1, | |
| out_channels: int=1, | |
| n_fft: int=1024, | |
| hop_length: int=256, | |
| win_length: int=1024, | |
| max_filters: int=1024, | |
| filters_scale: int=1, | |
| kernel_size: tp.Tuple[int, int]=(3, 9), | |
| dilations: tp.List=[1, 2, 4], | |
| stride: tp.Tuple[int, int]=(1, 2), | |
| normalized: bool=True, | |
| norm: str='weight_norm', | |
| activation: str='LeakyReLU', | |
| activation_params: dict={'negative_slope': 0.2}): | |
| super().__init__() | |
| assert len(kernel_size) == 2 | |
| assert len(stride) == 2 | |
| self.filters = filters | |
| self.in_channels = in_channels | |
| self.out_channels = out_channels | |
| self.n_fft = n_fft | |
| self.hop_length = hop_length | |
| self.win_length = win_length | |
| self.normalized = normalized | |
| self.activation = getattr(torch.nn, activation)(**activation_params) | |
| self.spec_transform = torchaudio.transforms.Spectrogram( | |
| n_fft=self.n_fft, | |
| hop_length=self.hop_length, | |
| win_length=self.win_length, | |
| window_fn=torch.hann_window, | |
| normalized=self.normalized, | |
| center=False, | |
| pad_mode=None, | |
| power=None) | |
| spec_channels = 2 * self.in_channels | |
| self.convs = nn.ModuleList() | |
| self.convs.append( | |
| NormConv2d( | |
| spec_channels, | |
| self.filters, | |
| kernel_size=kernel_size, | |
| padding=get_2d_padding(kernel_size))) | |
| in_chs = min(filters_scale * self.filters, max_filters) | |
| for i, dilation in enumerate(dilations): | |
| out_chs = min((filters_scale**(i + 1)) * self.filters, max_filters) | |
| self.convs.append( | |
| NormConv2d( | |
| in_chs, | |
| out_chs, | |
| kernel_size=kernel_size, | |
| stride=stride, | |
| dilation=(dilation, 1), | |
| padding=get_2d_padding(kernel_size, (dilation, 1)), | |
| norm=norm)) | |
| in_chs = out_chs | |
| out_chs = min((filters_scale**(len(dilations) + 1)) * self.filters, | |
| max_filters) | |
| self.convs.append( | |
| NormConv2d( | |
| in_chs, | |
| out_chs, | |
| kernel_size=(kernel_size[0], kernel_size[0]), | |
| padding=get_2d_padding((kernel_size[0], kernel_size[0])), | |
| norm=norm)) | |
| self.conv_post = NormConv2d( | |
| out_chs, | |
| self.out_channels, | |
| kernel_size=(kernel_size[0], kernel_size[0]), | |
| padding=get_2d_padding((kernel_size[0], kernel_size[0])), | |
| norm=norm) | |
| def forward(self, x: torch.Tensor): | |
| fmap = [] | |
| # print('x ', x.shape) | |
| z = self.spec_transform(x) # [B, 2, Freq, Frames, 2] | |
| # print('z ', z.shape) | |
| z = torch.cat([z.real, z.imag], dim=1) | |
| # print('cat_z ', z.shape) | |
| z = rearrange(z, 'b c w t -> b c t w') | |
| for i, layer in enumerate(self.convs): | |
| z = layer(z) | |
| z = self.activation(z) | |
| # print('z i', i, z.shape) | |
| fmap.append(z) | |
| z = self.conv_post(z) | |
| # print('logit ', z.shape) | |
| return z, fmap | |
| class MultiScaleSTFTDiscriminator(nn.Module): | |
| """Multi-Scale STFT (MS-STFT) discriminator. | |
| Args: | |
| filters (int): Number of filters in convolutions | |
| in_channels (int): Number of input channels. Default: 1 | |
| out_channels (int): Number of output channels. Default: 1 | |
| n_ffts (Sequence[int]): Size of FFT for each scale | |
| hop_lengths (Sequence[int]): Length of hop between STFT windows for each scale | |
| win_lengths (Sequence[int]): Window size for each scale | |
| **kwargs: additional args for STFTDiscriminator | |
| """ | |
| def __init__(self, | |
| filters: int, | |
| in_channels: int=1, | |
| out_channels: int=1, | |
| n_ffts: tp.List[int]=[1024, 2048, 512, 256, 128], | |
| hop_lengths: tp.List[int]=[256, 512, 128, 64, 32], | |
| win_lengths: tp.List[int]=[1024, 2048, 512, 256, 128], | |
| **kwargs): | |
| super().__init__() | |
| assert len(n_ffts) == len(hop_lengths) == len(win_lengths) | |
| self.discriminators = nn.ModuleList([ | |
| DiscriminatorSTFT( | |
| filters, | |
| in_channels=in_channels, | |
| out_channels=out_channels, | |
| n_fft=n_ffts[i], | |
| win_length=win_lengths[i], | |
| hop_length=hop_lengths[i], | |
| **kwargs) for i in range(len(n_ffts)) | |
| ]) | |
| self.num_discriminators = len(self.discriminators) | |
| def forward(self, x: torch.Tensor) -> DiscriminatorOutput: | |
| logits = [] | |
| fmaps = [] | |
| for disc in self.discriminators: | |
| logit, fmap = disc(x) | |
| logits.append(logit) | |
| fmaps.append(fmap) | |
| return logits, fmaps | |
| def test(): | |
| disc = MultiScaleSTFTDiscriminator(filters=32) | |
| y = torch.randn(1, 1, 24000) | |
| y_hat = torch.randn(1, 1, 24000) | |
| y_disc_r, fmap_r = disc(y) | |
| y_disc_gen, fmap_gen = disc(y_hat) | |
| assert len(y_disc_r) == len(y_disc_gen) == len(fmap_r) == len( | |
| fmap_gen) == disc.num_discriminators | |
| assert all([len(fm) == 5 for fm in fmap_r + fmap_gen]) | |
| assert all( | |
| [list(f.shape)[:2] == [1, 32] for fm in fmap_r + fmap_gen for f in fm]) | |
| assert all([len(logits.shape) == 4 for logits in y_disc_r + y_disc_gen]) | |
| if __name__ == '__main__': | |
| test() | |