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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 model code is adopted from DiffWave/model.py under the Apache License
# https://github.com/lmnt-com/diffwave
# Only the config-related varaible names are changed.
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, upsample_factors):
super().__init__()
self.conv1 = ConvTranspose2d(
1,
1,
[3, upsample_factors[0] * 2],
stride=[1, upsample_factors[0]],
padding=[1, upsample_factors[0] // 2],
)
self.conv2 = ConvTranspose2d(
1,
1,
[3, upsample_factors[1] * 2],
stride=[1, upsample_factors[1]],
padding=[1, upsample_factors[1] // 2],
)
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):
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.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1)
def forward(self, x, diffusion_step, conditioner):
diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1)
y = x + diffusion_step
conditioner = self.conditioner_projection(conditioner)
y = self.dilated_conv(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, cfg):
super().__init__()
self.cfg = cfg
self.cfg.model.diffwave.noise_schedule = np.linspace(
self.cfg.model.diffwave.noise_schedule_factors[0],
self.cfg.model.diffwave.noise_schedule_factors[1],
self.cfg.model.diffwave.noise_schedule_factors[2],
).tolist()
self.input_projection = Conv1d(1, self.cfg.model.diffwave.residual_channels, 1)
self.diffusion_embedding = DiffusionEmbedding(
len(self.cfg.model.diffwave.noise_schedule)
)
self.spectrogram_upsampler = SpectrogramUpsampler(
self.cfg.model.diffwave.upsample_factors
)
self.residual_layers = nn.ModuleList(
[
ResidualBlock(
self.cfg.preprocess.n_mel,
self.cfg.model.diffwave.residual_channels,
2 ** (i % self.cfg.model.diffwave.dilation_cycle_length),
)
for i in range(self.cfg.model.diffwave.residual_layers)
]
)
self.skip_projection = Conv1d(
self.cfg.model.diffwave.residual_channels,
self.cfg.model.diffwave.residual_channels,
1,
)
self.output_projection = Conv1d(self.cfg.model.diffwave.residual_channels, 1, 1)
nn.init.zeros_(self.output_projection.weight)
def forward(self, audio, diffusion_step, spectrogram):
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 = None
for layer in self.residual_layers:
x, skip_connection = layer(x, diffusion_step, 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
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