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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# This code is adopted from META's Encodec under MIT License
# https://github.com/facebookresearch/encodec
"""MS-STFT discriminator, provided here for reference."""
import typing as tp
import torchaudio
import torch
from torch import nn
from einops import rearrange
from modules.vocoder_blocks import *
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):
"""Discriminator STFT Module is the sub module of MultiScaleSTFTDiscriminator.
Args:
x (torch.Tensor): input tensor of shape [B, 1, Time]
Returns:
z: z is the output of the last convolutional layer of shape
fmap: fmap is the list of feature maps of every convolutional layer of shape
"""
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(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,
cfg,
in_channels: int = 1,
out_channels: int = 1,
n_ffts: tp.List[int] = [1024, 2048, 512],
hop_lengths: tp.List[int] = [256, 512, 256],
win_lengths: tp.List[int] = [1024, 2048, 512],
**kwargs,
):
self.cfg = cfg
super().__init__()
assert len(n_ffts) == len(hop_lengths) == len(win_lengths)
self.discriminators = nn.ModuleList(
[
DiscriminatorSTFT(
filters=self.cfg.model.msstftd.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, y, y_hat) -> DiscriminatorOutput:
"""Multi-Scale STFT (MS-STFT) discriminator.
Args:
x (torch.Tensor): input waveform
Returns:
logits: list of every discriminator's output
fmaps: list of every discriminator's feature maps,
each feature maps is a list of Discriminator STFT's every layer
"""
y_d_rs = []
y_d_gs = []
fmap_rs = []
fmap_gs = []
for disc in self.discriminators:
y_d_r, fmap_r = disc(y)
y_d_g, fmap_g = disc(y_hat)
y_d_rs.append(y_d_r)
fmap_rs.append(fmap_r)
y_d_gs.append(y_d_g)
fmap_gs.append(fmap_g)
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
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