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model.py
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# 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
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, fix_in=False, split=False):
super().__init__()
self.dilated_conv = Conv1d(residual_channels, 2 * residual_channels, 3, padding=dilation, dilation=dilation)
self.diffusion_projection = Linear(512, residual_channels)
self.conditioner_projection = Conv1d(n_mels, 2 * residual_channels, 1)
self.split = split
self.fix_in = fix_in
if self.split:
# print("2 individual Conv1d")
self.output_projection = Conv1d(residual_channels, residual_channels, 1)
self.output_residual = Conv1d(residual_channels, residual_channels, 1)
else:
if self.fix_in:
print("1 big and 1 small Conv1d")
self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1)
self.output_residual = Conv1d(residual_channels, residual_channels, 1)
else:
print("1 big Conv1d")
self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1)
def forward(self, x, conditioner, diffusion_step):
diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1)
conditioner = self.conditioner_projection(conditioner)
y = x + diffusion_step
y = self.dilated_conv(y) + conditioner
gate, filter = torch.chunk(y, 2, dim=1)
y = torch.sigmoid(gate) * torch.tanh(filter)
if self.split:
residual = self.output_residual(y)
skip = self.output_projection(y)
elif self.fix_in:
#calculate residual from non-fixed parameter
residual = self.output_residual(y)
#calculate skip from fixed parameter
y = self.output_projection(y)
_ , skip = torch.chunk(y, 2, dim=1)
else:
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, args, params):
super().__init__()
self.params = params
self.input_projection = Conv1d(1, params.residual_channels, 1)
self.diffusion_embedding = DiffusionEmbedding(len(params.noise_schedule))
self.spectrogram_upsampler = SpectrogramUpsampler(params.n_mels)
self.residual_layers = nn.ModuleList([
ResidualBlock(params.n_mels, params.residual_channels, 2**(i % params.dilation_cycle_length), fix_in=args.fix_in, split=args.voicebank)
for i in range(params.residual_layers)
])
self.skip_projection = Conv1d(params.residual_channels, params.residual_channels, 1)
self.output_projection = Conv1d(params.residual_channels, 1, 1)
nn.init.zeros_(self.output_projection.weight)
def forward(self, audio, spectrogram, diffusion_step):
x = audio.unsqueeze(1)
x = self.input_projection(x)
x = F.relu(x)
diffusion_step = self.diffusion_embedding(diffusion_step)
spectrogram = self.spectrogram_upsampler(spectrogram)
skip = []
for layer in self.residual_layers:
x, skip_connection = layer(x, spectrogram, diffusion_step)
skip.append(skip_connection)
x = torch.sum(torch.stack(skip), dim=0) / sqrt(len(self.residual_layers))
x = self.skip_projection(x)
x = F.relu(x)
x = self.output_projection(x)
return x