9c1009d16d
- vendor/CosyVoice: 패치된 CosyVoice 코드 + Matcha-TTS 벤더링 - cosyvoice_worker: 전용 venv에서 상주하는 합성 워커(FastAPI, 모델 1회 로드) - app/engines/cosyvoice_engine: HTTP 프록시 엔진(속도/피치) - Dockerfile: 전용 venv(torch cu128) + 모델 베이킹 + 빌드 워밍업 - entrypoint.sh: 워커(8001) + 메인앱(8788) 동시 기동
104 lines
3.7 KiB
Python
104 lines
3.7 KiB
Python
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu, Kai Hu)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import torch.nn as nn
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try:
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from torch.nn.utils.parametrizations import weight_norm
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except ImportError:
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from torch.nn.utils import weight_norm
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from cosyvoice.transformer.convolution import CausalConv1d
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class ConvRNNF0Predictor(nn.Module):
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def __init__(self,
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num_class: int = 1,
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in_channels: int = 80,
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cond_channels: int = 512
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):
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super().__init__()
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self.num_class = num_class
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self.condnet = nn.Sequential(
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weight_norm(
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nn.Conv1d(in_channels, cond_channels, kernel_size=3, padding=1)
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),
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nn.ELU(),
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weight_norm(
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nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
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),
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nn.ELU(),
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weight_norm(
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nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
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),
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nn.ELU(),
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weight_norm(
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nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
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),
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nn.ELU(),
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weight_norm(
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nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
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),
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nn.ELU(),
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)
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self.classifier = nn.Linear(in_features=cond_channels, out_features=self.num_class)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.condnet(x)
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x = x.transpose(1, 2)
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return torch.abs(self.classifier(x).squeeze(-1))
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class CausalConvRNNF0Predictor(nn.Module):
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def __init__(self,
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num_class: int = 1,
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in_channels: int = 80,
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cond_channels: int = 512
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):
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super().__init__()
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self.num_class = num_class
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self.condnet = nn.Sequential(
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weight_norm(
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CausalConv1d(in_channels, cond_channels, kernel_size=4, causal_type='right')
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),
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nn.ELU(),
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weight_norm(
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CausalConv1d(cond_channels, cond_channels, kernel_size=3, causal_type='left')
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),
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nn.ELU(),
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weight_norm(
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CausalConv1d(cond_channels, cond_channels, kernel_size=3, causal_type='left')
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),
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nn.ELU(),
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weight_norm(
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CausalConv1d(cond_channels, cond_channels, kernel_size=3, causal_type='left')
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),
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nn.ELU(),
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weight_norm(
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CausalConv1d(cond_channels, cond_channels, kernel_size=3, causal_type='left')
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),
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nn.ELU(),
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)
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self.classifier = nn.Linear(in_features=cond_channels, out_features=self.num_class)
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def forward(self, x: torch.Tensor, finalize: bool = True) -> torch.Tensor:
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if finalize is True:
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x = self.condnet[0](x)
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else:
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x = self.condnet[0](x[:, :, :-self.condnet[0].causal_padding], x[:, :, -self.condnet[0].causal_padding:])
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for i in range(1, len(self.condnet)):
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x = self.condnet[i](x)
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x = x.transpose(1, 2)
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return torch.abs(self.classifier(x).squeeze(-1))
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