# Copyright 2020 LMNT, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from math import sqrt
from torch.utils.checkpoint import checkpoint
from trainer.networks import register_model
Linear = nn.Linear
ConvTranspose2d = nn.ConvTranspose2d
def Conv1d(*args, **kwargs):
layer = nn.Conv1d(*args, **kwargs)
nn.init.kaiming_normal_(layer.weight)
return layer
@torch.jit.script
def silu(x):
return x * torch.sigmoid(x)
class DiffusionEmbedding(nn.Module):
def __init__(self, max_steps):
super().__init__()
self.register_buffer('embedding', self._build_embedding(max_steps), persistent=False)
self.projection1 = Linear(128, 512)
self.projection2 = Linear(512, 512)
def forward(self, diffusion_step):
if diffusion_step.dtype in [torch.int32, torch.int64]:
x = self.embedding[diffusion_step]
else:
x = self._lerp_embedding(diffusion_step)
x = self.projection1(x)
x = silu(x)
x = self.projection2(x)
x = silu(x)
return x
def _lerp_embedding(self, t):
low_idx = torch.floor(t).long()
high_idx = torch.ceil(t).long()
low = self.embedding[low_idx]
high = self.embedding[high_idx]
return low + (high - low) * (t - low_idx)
def _build_embedding(self, max_steps):
steps = torch.arange(max_steps).unsqueeze(1) # [T,1]
dims = torch.arange(64).unsqueeze(0) # [1,64]
table = steps * 10.0 ** (dims * 4.0 / 63.0) # [T,64]
table = torch.cat([torch.sin(table), torch.cos(table)], dim=1)
return table
class SpectrogramUpsampler(nn.Module):
def __init__(self, n_mels):
super().__init__()
self.conv1 = ConvTranspose2d(1, 1, [3, 32], stride=[1, 16], padding=[1, 8])
self.conv2 = ConvTranspose2d(1, 1, [3, 32], stride=[1, 16], padding=[1, 8])
def forward(self, x):
x = torch.unsqueeze(x, 1)
x = self.conv1(x)
x = F.leaky_relu(x, 0.4)
x = self.conv2(x)
x = F.leaky_relu(x, 0.4)
x = torch.squeeze(x, 1)
return x
class ResidualBlock(nn.Module):
def __init__(self, n_mels, residual_channels, dilation, uncond=False):
'''
:param n_mels: inplanes of conv1x1 for spectrogram conditional
:param residual_channels: audio conv
:param dilation: audio conv dilation
:param uncond: disable spectrogram conditional
'''
super().__init__()
self.dilated_conv = Conv1d(residual_channels, 2 * residual_channels, 3, padding=dilation, dilation=dilation)
self.diffusion_projection = Linear(512, residual_channels)
if not uncond: # conditional model
self.conditioner_projection = Conv1d(n_mels, 2 * residual_channels, 1)
else: # unconditional model
self.conditioner_projection = None
self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1)
def forward(self, x, diffusion_step, conditioner=None):
assert (conditioner is None and self.conditioner_projection is None) or \
(conditioner is not None and self.conditioner_projection is not None)
diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1)
y = x + diffusion_step
if self.conditioner_projection is None: # using a unconditional model
y = self.dilated_conv(y)
else:
y = self.dilated_conv(y)
conditioner = self.conditioner_projection(conditioner)
conditioner = F.interpolate(conditioner, size=y.shape[-1], mode='nearest')
y = y + conditioner
gate, filter = torch.chunk(y, 2, dim=1)
y = torch.sigmoid(gate) * torch.tanh(filter)
y = self.output_projection(y)
residual, skip = torch.chunk(y, 2, dim=1)
return (x + residual) / sqrt(2.0), skip
class DiffWave(nn.Module):
def __init__(self, residual_layers=30, residual_channels=64, num_timesteps=4000, n_mels=128,
dilation_cycle_length=10, unconditional=False):
super().__init__()
self.input_projection = Conv1d(1, residual_channels, 1)
self.diffusion_embedding = DiffusionEmbedding(num_timesteps)
if unconditional: # use unconditional model
self.spectrogram_upsampler = None
else:
self.spectrogram_upsampler = SpectrogramUpsampler(n_mels)
self.residual_layers = nn.ModuleList([
ResidualBlock(n_mels, residual_channels, 2 ** (i % dilation_cycle_length), uncond=unconditional)
for i in range(residual_layers)
])
self.skip_projection = Conv1d(residual_channels, residual_channels, 1)
self.output_projection = Conv1d(residual_channels, 2, 1)
nn.init.zeros_(self.output_projection.weight)
def forward(self, x, timesteps, spectrogram=None):
assert (spectrogram is None and self.spectrogram_upsampler is None) or \
(spectrogram is not None and self.spectrogram_upsampler is not None)
x = self.input_projection(x)
x = F.relu(x)
timesteps = checkpoint(self.diffusion_embedding, timesteps)
if self.spectrogram_upsampler: # use conditional model
spectrogram = checkpoint(self.spectrogram_upsampler, spectrogram)
skip = None
for layer in self.residual_layers:
x, skip_connection = checkpoint(layer, x, timesteps, spectrogram)
skip = skip_connection if skip is None else skip_connection + skip
x = skip / sqrt(len(self.residual_layers))
x = self.skip_projection(x)
x = F.relu(x)
x = self.output_projection(x)
return x
@register_model
def register_diffwave(opt_net, opt):
return DiffWave(**opt_net['kwargs'])
if __name__ == '__main__':
model = DiffWave()
model(torch.randn(2,1,65536), torch.tensor([500,3999]), torch.randn(2,128,256))