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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.
import torch
import torch.nn as nn
from typing import List
from sparktts.modules.blocks.vocos import VocosBackbone
from sparktts.modules.blocks.samper import SamplingBlock
class Decoder(nn.Module):
"""Decoder module with convnext and upsampling blocks
Args:
sample_ratios (List[int]): sample ratios
example: [2, 2] means downsample by 2x and then upsample by 2x
"""
def __init__(
self,
input_channels: int,
vocos_dim: int,
vocos_intermediate_dim: int,
vocos_num_layers: int,
out_channels: int,
condition_dim: int = None,
sample_ratios: List[int] = [1, 1],
use_tanh_at_final: bool = False,
):
super().__init__()
self.linear_pre = nn.Linear(input_channels, vocos_dim)
modules = [
nn.Sequential(
SamplingBlock(
dim=vocos_dim,
groups=vocos_dim,
upsample_scale=ratio,
),
VocosBackbone(
input_channels=vocos_dim,
dim=vocos_dim,
intermediate_dim=vocos_intermediate_dim,
num_layers=2,
condition_dim=None,
),
)
for ratio in sample_ratios
]
self.downsample = nn.Sequential(*modules)
self.vocos_backbone = VocosBackbone(
input_channels=vocos_dim,
dim=vocos_dim,
intermediate_dim=vocos_intermediate_dim,
num_layers=vocos_num_layers,
condition_dim=condition_dim,
)
self.linear = nn.Linear(vocos_dim, out_channels)
self.use_tanh_at_final = use_tanh_at_final
def forward(self, x: torch.Tensor, c: torch.Tensor = None):
"""encoder forward.
Args:
x (torch.Tensor): (batch_size, input_channels, length)
Returns:
x (torch.Tensor): (batch_size, encode_channels, length)
"""
x = self.linear_pre(x.transpose(1, 2))
x = self.downsample(x).transpose(1, 2)
x = self.vocos_backbone(x, condition=c)
x = self.linear(x).transpose(1, 2)
if self.use_tanh_at_final:
x = torch.tanh(x)
return x
# test
if __name__ == "__main__":
test_input = torch.randn(8, 1024, 50) # Batch size = 8, 1024 channels, length = 50
condition = torch.randn(8, 256)
decoder = Decoder(
input_channels=1024,
vocos_dim=384,
vocos_intermediate_dim=2048,
vocos_num_layers=12,
out_channels=256,
condition_dim=256,
sample_ratios=[2, 2],
)
output = decoder(test_input, condition)
print(output.shape) # torch.Size([8, 256, 200])
if output.shape == torch.Size([8, 256, 200]):
print("Decoder test passed")
else:
print("Decoder test failed")
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