sparktts/modules/encoder_decoder/wave_generator.py

# Copyright (c) 2024 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.nn as nn

from sparktts.modules.blocks.layers import (
    Snake1d,
    WNConv1d,
    ResidualUnit,
    WNConvTranspose1d,
    init_weights,
)


class DecoderBlock(nn.Module):
    def __init__(
        self,
        input_dim: int = 16,
        output_dim: int = 8,
        kernel_size: int = 2,
        stride: int = 1,
    ):
        super().__init__()
        self.block = nn.Sequential(
            Snake1d(input_dim),
            WNConvTranspose1d(
                input_dim,
                output_dim,
                kernel_size=kernel_size,
                stride=stride,
                padding=(kernel_size - stride) // 2,
            ),
            ResidualUnit(output_dim, dilation=1),
            ResidualUnit(output_dim, dilation=3),
            ResidualUnit(output_dim, dilation=9),
        )

    def forward(self, x):
        return self.block(x)


class WaveGenerator(nn.Module):
    def __init__(
        self,
        input_channel,
        channels,
        rates,
        kernel_sizes,
        d_out: int = 1,
    ):
        super().__init__()

        # Add first conv layer
        layers = [WNConv1d(input_channel, channels, kernel_size=7, padding=3)]

        # Add upsampling + MRF blocks
        for i, (kernel_size, stride) in enumerate(zip(kernel_sizes, rates)):
            input_dim = channels // 2**i
            output_dim = channels // 2 ** (i + 1)
            layers += [DecoderBlock(input_dim, output_dim, kernel_size, stride)]

        # Add final conv layer
        layers += [
            Snake1d(output_dim),
            WNConv1d(output_dim, d_out, kernel_size=7, padding=3),
            nn.Tanh(),
        ]

        self.model = nn.Sequential(*layers)

        self.apply(init_weights)

    def forward(self, x):
        return self.model(x)