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# Copyright (c) 2025 SparkAudio
# 2025 Xinsheng Wang ([email protected])
#
# 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.
# Adapted from https://github.com/descriptinc/descript-audio-codec under the Apache License 2.0
import torch
import torch.nn as nn
from torch.nn.utils import weight_norm
def WNConv1d(*args, **kwargs):
return weight_norm(nn.Conv1d(*args, **kwargs))
def WNConvTranspose1d(*args, **kwargs):
return weight_norm(nn.ConvTranspose1d(*args, **kwargs))
# Scripting this brings model speed up 1.4x
@torch.jit.script
def snake(x, alpha):
shape = x.shape
x = x.reshape(shape[0], shape[1], -1)
x = x + (alpha + 1e-9).reciprocal() * torch.sin(alpha * x).pow(2)
x = x.reshape(shape)
return x
class Snake1d(nn.Module):
def __init__(self, channels):
super().__init__()
self.alpha = nn.Parameter(torch.ones(1, channels, 1))
def forward(self, x):
return snake(x, self.alpha)
class ResidualUnit(nn.Module):
def __init__(self, dim: int = 16, dilation: int = 1):
super().__init__()
pad = ((7 - 1) * dilation) // 2
self.block = nn.Sequential(
Snake1d(dim),
WNConv1d(dim, dim, kernel_size=7, dilation=dilation, padding=pad),
Snake1d(dim),
WNConv1d(dim, dim, kernel_size=1),
)
def forward(self, x):
y = self.block(x)
pad = (x.shape[-1] - y.shape[-1]) // 2
if pad > 0:
x = x[..., pad:-pad]
return x + y
def init_weights(m):
if isinstance(m, nn.Conv1d):
nn.init.trunc_normal_(m.weight, std=0.02)
nn.init.constant_(m.bias, 0)
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