mirror of
https://github.com/k2-fsa/icefall.git
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297 lines
9.4 KiB
Python
297 lines
9.4 KiB
Python
from typing import List, Optional, Tuple
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import torch
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from torch import nn
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from torch.nn import Conv2d
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from torch.nn.utils.parametrizations import weight_norm
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from torchaudio.transforms import Spectrogram
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class MultiPeriodDiscriminator(nn.Module):
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"""
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Multi-Period Discriminator module adapted from https://github.com/jik876/hifi-gan.
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Additionally, it allows incorporating conditional information with a learned embeddings table.
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Args:
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periods (tuple[int]): Tuple of periods for each discriminator.
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num_embeddings (int, optional): Number of embeddings. None means non-conditional discriminator.
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Defaults to None.
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"""
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def __init__(
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self,
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periods: Tuple[int, ...] = (2, 3, 5, 7, 11),
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num_embeddings: Optional[int] = None,
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):
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super().__init__()
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self.discriminators = nn.ModuleList(
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[DiscriminatorP(period=p, num_embeddings=num_embeddings) for p in periods]
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)
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def forward(
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self,
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y: torch.Tensor,
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y_hat: torch.Tensor,
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bandwidth_id: Optional[torch.Tensor] = None,
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) -> Tuple[
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List[torch.Tensor],
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List[torch.Tensor],
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List[List[torch.Tensor]],
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List[List[torch.Tensor]],
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]:
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y_d_rs = []
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y_d_gs = []
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fmap_rs = []
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fmap_gs = []
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for d in self.discriminators:
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y_d_r, fmap_r = d(x=y, cond_embedding_id=bandwidth_id)
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y_d_g, fmap_g = d(x=y_hat, cond_embedding_id=bandwidth_id)
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y_d_rs.append(y_d_r)
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fmap_rs.append(fmap_r)
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y_d_gs.append(y_d_g)
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fmap_gs.append(fmap_g)
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return y_d_rs, y_d_gs, fmap_rs, fmap_gs
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class DiscriminatorP(nn.Module):
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def __init__(
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self,
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period: int,
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in_channels: int = 1,
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kernel_size: int = 5,
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stride: int = 3,
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lrelu_slope: float = 0.1,
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num_embeddings: Optional[int] = None,
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):
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super().__init__()
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self.period = period
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self.convs = nn.ModuleList(
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[
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weight_norm(
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Conv2d(
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in_channels,
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32,
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(kernel_size, 1),
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(stride, 1),
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padding=(kernel_size // 2, 0),
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)
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),
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weight_norm(
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Conv2d(
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32,
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128,
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(kernel_size, 1),
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(stride, 1),
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padding=(kernel_size // 2, 0),
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)
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),
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weight_norm(
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Conv2d(
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128,
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512,
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(kernel_size, 1),
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(stride, 1),
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padding=(kernel_size // 2, 0),
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)
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),
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weight_norm(
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Conv2d(
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512,
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1024,
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(kernel_size, 1),
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(stride, 1),
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padding=(kernel_size // 2, 0),
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)
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),
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weight_norm(
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Conv2d(
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1024,
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1024,
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(kernel_size, 1),
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(1, 1),
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padding=(kernel_size // 2, 0),
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)
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),
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]
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)
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if num_embeddings is not None:
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self.emb = torch.nn.Embedding(
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num_embeddings=num_embeddings, embedding_dim=1024
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)
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torch.nn.init.zeros_(self.emb.weight)
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self.conv_post = weight_norm(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
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self.lrelu_slope = lrelu_slope
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def forward(
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self, x: torch.Tensor, cond_embedding_id: Optional[torch.Tensor] = None
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) -> Tuple[torch.Tensor, List[torch.Tensor]]:
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x = x.unsqueeze(1)
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fmap = []
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# 1d to 2d
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b, c, t = x.shape
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if t % self.period != 0: # pad first
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n_pad = self.period - (t % self.period)
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x = torch.nn.functional.pad(x, (0, n_pad), "reflect")
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t = t + n_pad
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x = x.view(b, c, t // self.period, self.period)
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for i, l in enumerate(self.convs):
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x = l(x)
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x = torch.nn.functional.leaky_relu(x, self.lrelu_slope)
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if i > 0:
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fmap.append(x)
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if cond_embedding_id is not None:
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emb = self.emb(cond_embedding_id)
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h = (emb.view(1, -1, 1, 1) * x).sum(dim=1, keepdims=True)
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else:
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h = 0
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x = self.conv_post(x)
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fmap.append(x)
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x += h
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x = torch.flatten(x, 1, -1)
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return x, fmap
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class MultiResolutionDiscriminator(nn.Module):
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def __init__(
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self,
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fft_sizes: Tuple[int, ...] = (2048, 1024, 512),
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num_embeddings: Optional[int] = None,
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):
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"""
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Multi-Resolution Discriminator module adapted from https://github.com/descriptinc/descript-audio-codec.
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Additionally, it allows incorporating conditional information with a learned embeddings table.
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Args:
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fft_sizes (tuple[int]): Tuple of window lengths for FFT. Defaults to (2048, 1024, 512).
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num_embeddings (int, optional): Number of embeddings. None means non-conditional discriminator.
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Defaults to None.
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"""
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super().__init__()
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self.discriminators = nn.ModuleList(
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[
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DiscriminatorR(window_length=w, num_embeddings=num_embeddings)
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for w in fft_sizes
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]
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)
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def forward(
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self, y: torch.Tensor, y_hat: torch.Tensor, bandwidth_id: torch.Tensor = None
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) -> Tuple[
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List[torch.Tensor],
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List[torch.Tensor],
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List[List[torch.Tensor]],
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List[List[torch.Tensor]],
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]:
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y_d_rs = []
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y_d_gs = []
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fmap_rs = []
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fmap_gs = []
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for d in self.discriminators:
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y_d_r, fmap_r = d(x=y, cond_embedding_id=bandwidth_id)
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y_d_g, fmap_g = d(x=y_hat, cond_embedding_id=bandwidth_id)
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y_d_rs.append(y_d_r)
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fmap_rs.append(fmap_r)
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y_d_gs.append(y_d_g)
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fmap_gs.append(fmap_g)
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return y_d_rs, y_d_gs, fmap_rs, fmap_gs
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class DiscriminatorR(nn.Module):
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def __init__(
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self,
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window_length: int,
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num_embeddings: Optional[int] = None,
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channels: int = 32,
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hop_factor: float = 0.25,
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bands: Tuple[Tuple[float, float], ...] = (
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(0.0, 0.1),
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(0.1, 0.25),
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(0.25, 0.5),
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(0.5, 0.75),
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(0.75, 1.0),
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),
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):
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super().__init__()
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self.window_length = window_length
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self.hop_factor = hop_factor
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self.spec_fn = Spectrogram(
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n_fft=window_length,
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hop_length=int(window_length * hop_factor),
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win_length=window_length,
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power=None,
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)
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n_fft = window_length // 2 + 1
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bands = [(int(b[0] * n_fft), int(b[1] * n_fft)) for b in bands]
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self.bands = bands
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convs = lambda: nn.ModuleList(
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[
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weight_norm(nn.Conv2d(2, channels, (3, 9), (1, 1), padding=(1, 4))),
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weight_norm(
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nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))
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),
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weight_norm(
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nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))
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),
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weight_norm(
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nn.Conv2d(channels, channels, (3, 9), (1, 2), padding=(1, 4))
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),
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weight_norm(
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nn.Conv2d(channels, channels, (3, 3), (1, 1), padding=(1, 1))
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),
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]
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)
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self.band_convs = nn.ModuleList([convs() for _ in range(len(self.bands))])
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if num_embeddings is not None:
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self.emb = torch.nn.Embedding(
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num_embeddings=num_embeddings, embedding_dim=channels
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)
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torch.nn.init.zeros_(self.emb.weight)
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self.conv_post = weight_norm(
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nn.Conv2d(channels, 1, (3, 3), (1, 1), padding=(1, 1))
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)
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def spectrogram(self, x):
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# Remove DC offset
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x = x - x.mean(dim=-1, keepdims=True)
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# Peak normalize the volume of input audio
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x = 0.8 * x / (x.abs().max(dim=-1, keepdim=True)[0] + 1e-9)
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x = self.spec_fn(x)
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x = torch.view_as_real(x)
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# x = rearrange(x, "b f t c -> b c t f")
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x = x.permute(0, 3, 2, 1)
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# Split into bands
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x_bands = [x[..., b[0] : b[1]] for b in self.bands]
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return x_bands
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def forward(self, x: torch.Tensor, cond_embedding_id: torch.Tensor = None):
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x_bands = self.spectrogram(x)
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fmap = []
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x = []
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for band, stack in zip(x_bands, self.band_convs):
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for i, layer in enumerate(stack):
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band = layer(band)
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band = torch.nn.functional.leaky_relu(band, 0.1)
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if i > 0:
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fmap.append(band)
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x.append(band)
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x = torch.cat(x, dim=-1)
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if cond_embedding_id is not None:
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emb = self.emb(cond_embedding_id)
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h = (emb.view(1, -1, 1, 1) * x).sum(dim=1, keepdims=True)
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else:
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h = 0
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x = self.conv_post(x)
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fmap.append(x)
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x += h
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return x, fmap
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