177 lines
6.4 KiB
Python
177 lines
6.4 KiB
Python
# Copyright 2020 LMNT, Inc. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from math import sqrt
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from trainer.networks import register_model
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Linear = nn.Linear
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ConvTranspose2d = nn.ConvTranspose2d
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def Conv1d(*args, **kwargs):
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layer = nn.Conv1d(*args, **kwargs)
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nn.init.kaiming_normal_(layer.weight)
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return layer
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@torch.jit.script
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def silu(x):
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return x * torch.sigmoid(x)
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class DiffusionEmbedding(nn.Module):
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def __init__(self, max_steps):
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super().__init__()
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self.register_buffer('embedding', self._build_embedding(max_steps), persistent=False)
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self.projection1 = Linear(128, 512)
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self.projection2 = Linear(512, 512)
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def forward(self, diffusion_step):
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if diffusion_step.dtype in [torch.int32, torch.int64]:
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x = self.embedding[diffusion_step]
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else:
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x = self._lerp_embedding(diffusion_step)
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x = self.projection1(x)
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x = silu(x)
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x = self.projection2(x)
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x = silu(x)
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return x
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def _lerp_embedding(self, t):
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low_idx = torch.floor(t).long()
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high_idx = torch.ceil(t).long()
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low = self.embedding[low_idx]
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high = self.embedding[high_idx]
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return low + (high - low) * (t - low_idx)
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def _build_embedding(self, max_steps):
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steps = torch.arange(max_steps).unsqueeze(1) # [T,1]
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dims = torch.arange(64).unsqueeze(0) # [1,64]
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table = steps * 10.0 ** (dims * 4.0 / 63.0) # [T,64]
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table = torch.cat([torch.sin(table), torch.cos(table)], dim=1)
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return table
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class SpectrogramUpsampler(nn.Module):
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def __init__(self, n_mels):
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super().__init__()
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self.conv1 = ConvTranspose2d(1, 1, [3, 32], stride=[1, 16], padding=[1, 8])
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self.conv2 = ConvTranspose2d(1, 1, [3, 32], stride=[1, 16], padding=[1, 8])
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def forward(self, x):
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x = torch.unsqueeze(x, 1)
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x = self.conv1(x)
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x = F.leaky_relu(x, 0.4)
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x = self.conv2(x)
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x = F.leaky_relu(x, 0.4)
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x = torch.squeeze(x, 1)
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return x
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class ResidualBlock(nn.Module):
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def __init__(self, n_mels, residual_channels, dilation, uncond=False):
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'''
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:param n_mels: inplanes of conv1x1 for spectrogram conditional
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:param residual_channels: audio conv
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:param dilation: audio conv dilation
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:param uncond: disable spectrogram conditional
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'''
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super().__init__()
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self.dilated_conv = Conv1d(residual_channels, 2 * residual_channels, 3, padding=dilation, dilation=dilation)
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self.diffusion_projection = Linear(512, residual_channels)
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if not uncond: # conditional model
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self.conditioner_projection = Conv1d(n_mels, 2 * residual_channels, 1)
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else: # unconditional model
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self.conditioner_projection = None
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self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1)
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def forward(self, x, diffusion_step, conditioner=None):
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assert (conditioner is None and self.conditioner_projection is None) or \
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(conditioner is not None and self.conditioner_projection is not None)
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diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1)
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y = x + diffusion_step
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if self.conditioner_projection is None: # using a unconditional model
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y = self.dilated_conv(y)
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else:
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y = self.dilated_conv(y)
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conditioner = self.conditioner_projection(conditioner)
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conditioner = F.interpolate(conditioner, size=y.shape[-1], mode='nearest')
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y = y + conditioner
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gate, filter = torch.chunk(y, 2, dim=1)
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y = torch.sigmoid(gate) * torch.tanh(filter)
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y = self.output_projection(y)
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residual, skip = torch.chunk(y, 2, dim=1)
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return (x + residual) / sqrt(2.0), skip
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class DiffWave(nn.Module):
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def __init__(self, residual_layers=30, residual_channels=64, num_timesteps=4000, n_mels=128,
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dilation_cycle_length=10, unconditional=False):
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super().__init__()
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self.input_projection = Conv1d(1, residual_channels, 1)
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self.diffusion_embedding = DiffusionEmbedding(num_timesteps)
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if unconditional: # use unconditional model
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self.spectrogram_upsampler = None
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else:
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self.spectrogram_upsampler = SpectrogramUpsampler(n_mels)
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self.residual_layers = nn.ModuleList([
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ResidualBlock(n_mels, residual_channels, 2 ** (i % dilation_cycle_length), uncond=unconditional)
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for i in range(residual_layers)
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])
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self.skip_projection = Conv1d(residual_channels, residual_channels, 1)
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self.output_projection = Conv1d(residual_channels, 1, 1)
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nn.init.zeros_(self.output_projection.weight)
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def forward(self, x, timesteps, spectrogram=None):
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assert (spectrogram is None and self.spectrogram_upsampler is None) or \
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(spectrogram is not None and self.spectrogram_upsampler is not None)
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x = self.input_projection(x)
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x = F.relu(x)
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timesteps = self.diffusion_embedding(timesteps)
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if self.spectrogram_upsampler: # use conditional model
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spectrogram = self.spectrogram_upsampler(spectrogram)
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skip = None
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for layer in self.residual_layers:
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x, skip_connection = layer(x, timesteps, spectrogram)
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skip = skip_connection if skip is None else skip_connection + skip
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x = skip / sqrt(len(self.residual_layers))
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x = self.skip_projection(x)
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x = F.relu(x)
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x = self.output_projection(x)
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return x
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@register_model
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def register_diffwave(opt_net, opt):
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return DiffWave(**opt_net['kwargs'])
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if __name__ == '__main__':
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model = DiffWave()
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model(torch.randn(2,1,20000), torch.tensor([500,3999]), torch.randn(2,128,78)) |