56 lines
2.0 KiB
Python
56 lines
2.0 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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 paddle.fluid.dygraph as dg
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import paddle.fluid as fluid
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from parakeet.modules.customized import Conv1D
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from parakeet.models.transformer_tts.utils import *
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from parakeet.models.transformer_tts.cbhg import CBHG
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class Vocoder(dg.Layer):
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def __init__(self, config, batch_size):
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"""CBHG Network (mel -> linear)
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Args:
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config: the yaml configs used in Vocoder model.
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batch_size (int): the batch size of input.
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"""
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super(Vocoder, self).__init__()
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self.pre_proj = Conv1D(
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num_channels=config['audio']['num_mels'],
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num_filters=config['hidden_size'],
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filter_size=1)
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self.cbhg = CBHG(config['hidden_size'], batch_size)
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self.post_proj = Conv1D(
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num_channels=config['hidden_size'],
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num_filters=(config['audio']['n_fft'] // 2) + 1,
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filter_size=1)
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def forward(self, mel):
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"""
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Compute mel spectrum to linear spectrum.
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Args:
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mel (Variable): shape(B, C, T), dtype float32, the input mel spectrum.
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Returns:
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mag_pred (Variable): shape(B, T, C), the linear output.
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"""
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mel = layers.transpose(mel, [0, 2, 1])
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mel = self.pre_proj(mel)
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mel = self.cbhg(mel)
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mag_pred = self.post_proj(mel)
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mag_pred = layers.transpose(mag_pred, [0, 2, 1])
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return mag_pred
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