56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu)
|
|
#
|
|
# 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 torch.nn.utils import weight_norm
|
|
|
|
|
|
class ConvRNNF0Predictor(nn.Module):
|
|
def __init__(self,
|
|
num_class: int = 1,
|
|
in_channels: int = 80,
|
|
cond_channels: int = 512
|
|
):
|
|
super().__init__()
|
|
|
|
self.num_class = num_class
|
|
self.condnet = nn.Sequential(
|
|
weight_norm(
|
|
nn.Conv1d(in_channels, cond_channels, kernel_size=3, padding=1)
|
|
),
|
|
nn.ELU(),
|
|
weight_norm(
|
|
nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
|
|
),
|
|
nn.ELU(),
|
|
weight_norm(
|
|
nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
|
|
),
|
|
nn.ELU(),
|
|
weight_norm(
|
|
nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
|
|
),
|
|
nn.ELU(),
|
|
weight_norm(
|
|
nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
|
|
),
|
|
nn.ELU(),
|
|
)
|
|
self.classifier = nn.Linear(in_features=cond_channels, out_features=self.num_class)
|
|
|
|
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
|
x = self.condnet(x)
|
|
x = x.transpose(1, 2)
|
|
return torch.abs(self.classifier(x).squeeze(-1))
|