Spaces:
Running
on
A10G
Running
on
A10G
| # 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. | |
| import typing as tp | |
| import torchaudio | |
| import torch | |
| from torch import nn | |
| from einops import rearrange | |
| from ...modules import NormConv2d | |
| from .base import MultiDiscriminator, MultiDiscriminatorOutputType | |
| 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. | |
| out_channels (int): Number of output channels. | |
| n_fft (int): Size of FFT for each scale. | |
| hop_length (int): Length of hop between STFT windows for each scale. | |
| kernel_size (tuple of int): Inner Conv2d kernel sizes. | |
| stride (tuple of int): Inner Conv2d strides. | |
| dilations (list of int): Inner Conv2d dilation on the time dimension. | |
| win_length (int): Window size for each scale. | |
| normalized (bool): Whether to normalize by magnitude after stft. | |
| norm (str): Normalization method. | |
| activation (str): Activation function. | |
| activation_params (dict): Parameters to provide to the activation function. | |
| growth (int): Growth factor for the filters. | |
| """ | |
| 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 = [] | |
| z = self.spec_transform(x) # [B, 2, Freq, Frames, 2] | |
| z = torch.cat([z.real, z.imag], dim=1) | |
| 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) | |
| fmap.append(z) | |
| z = self.conv_post(z) | |
| return z, fmap | |
| class MultiScaleSTFTDiscriminator(MultiDiscriminator): | |
| """Multi-Scale STFT (MS-STFT) discriminator. | |
| Args: | |
| filters (int): Number of filters in convolutions. | |
| in_channels (int): Number of input channels. | |
| out_channels (int): Number of output channels. | |
| sep_channels (bool): Separate channels to distinct samples for stereo support. | |
| 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, sep_channels: bool = False, | |
| n_ffts: tp.List[int] = [1024, 2048, 512], hop_lengths: tp.List[int] = [256, 512, 128], | |
| win_lengths: tp.List[int] = [1024, 2048, 512], **kwargs): | |
| super().__init__() | |
| assert len(n_ffts) == len(hop_lengths) == len(win_lengths) | |
| self.sep_channels = sep_channels | |
| 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)) | |
| ]) | |
| def num_discriminators(self): | |
| return len(self.discriminators) | |
| def _separate_channels(self, x: torch.Tensor) -> torch.Tensor: | |
| B, C, T = x.shape | |
| return x.view(-1, 1, T) | |
| def forward(self, x: torch.Tensor) -> MultiDiscriminatorOutputType: | |
| logits = [] | |
| fmaps = [] | |
| for disc in self.discriminators: | |
| logit, fmap = disc(x) | |
| logits.append(logit) | |
| fmaps.append(fmap) | |
| return logits, fmaps | |