107 lines
5.4 KiB
Python
107 lines
5.4 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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from __future__ import division
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import numpy as np
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import paddle.fluid.layers as F
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import paddle.fluid.initializer as I
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import paddle.fluid.dygraph as dg
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class DeepVoice3(dg.Layer):
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def __init__(self, encoder, decoder, converter, speaker_embedding,
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use_decoder_states):
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"""Deep Voice 3 TTS model.
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Args:
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encoder (Layer): the encoder.
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decoder (Layer): the decoder.
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converter (Layer): the converter.
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speaker_embedding (Layer): the speaker embedding (for multispeaker cases).
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use_decoder_states (bool): use decoder states instead of predicted mel spectrogram as the input of the converter.
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"""
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super(DeepVoice3, self).__init__()
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if speaker_embedding is None:
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self.n_speakers = 1
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else:
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self.speaker_embedding = speaker_embedding
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self.encoder = encoder
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self.decoder = decoder
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self.converter = converter
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self.use_decoder_states = use_decoder_states
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def forward(self, text_sequences, text_positions, valid_lengths,
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speaker_indices, mel_inputs, frame_positions):
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"""Compute predicted value in a teacher forcing training manner.
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Args:
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text_sequences (Variable): shape(B, T_enc), dtype: int64, text indices.
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text_positions (Variable): shape(B, T_enc), dtype: int64, positions of text indices.
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valid_lengths (Variable): shape(B, ), dtype: int64, valid lengths of utterances.
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speaker_indices (Variable): shape(B, ), dtype: int64, speaker indices for utterances.
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mel_inputs (Variable): shape(B, T_mel, C_mel), dytpe: int64, ground truth mel spectrogram.
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frame_positions (Variable): shape(B, T_dec), dtype: int64, positions of decoder steps.
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Returns:
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(mel_outputs, linear_outputs, alignments, done)
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mel_outputs (Variable): shape(B, T_mel, C_mel), dtype float32, predicted mel spectrogram.
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mel_outputs (Variable): shape(B, T_mel, C_mel), dtype float32, predicted mel spectrogram.
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alignments (Variable): shape(N, B, T_dec, T_enc), dtype float32, predicted attention.
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done (Variable): shape(B, T_dec), dtype float32, predicted done probability.
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(T_mel: time steps of mel spectrogram, T_lin: time steps of linear spectrogra, T_dec, time steps of decoder, T_enc: time steps of encoder.)
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"""
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if hasattr(self, "speaker_embedding"):
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speaker_embed = self.speaker_embedding(speaker_indices)
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else:
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speaker_embed = None
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keys, values = self.encoder(text_sequences, speaker_embed)
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mel_outputs, alignments, done, decoder_states = self.decoder(
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(keys, values), valid_lengths, mel_inputs, text_positions,
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frame_positions, speaker_embed)
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linear_outputs = self.converter(decoder_states
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if self.use_decoder_states else
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mel_outputs, speaker_embed)
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return mel_outputs, linear_outputs, alignments, done
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def transduce(self, text_sequences, text_positions, speaker_indices=None):
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"""Generate output without teacher forcing. Only batch_size = 1 is supported.
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Args:
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text_sequences (Variable): shape(B, T_enc), dtype: int64, text indices.
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text_positions (Variable): shape(B, T_enc), dtype: int64, positions of text indices.
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speaker_indices (Variable): shape(B, ), dtype: int64, speaker indices for utterances.
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Returns:
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(mel_outputs, linear_outputs, alignments, done)
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mel_outputs (Variable): shape(B, T_mel, C_mel), dtype float32, predicted mel spectrogram.
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mel_outputs (Variable): shape(B, T_mel, C_mel), dtype float32, predicted mel spectrogram.
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alignments (Variable): shape(B, T_dec, T_enc), dtype float32, predicted average attention of all attention layers.
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done (Variable): shape(B, T_dec), dtype float32, predicted done probability.
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(T_mel: time steps of mel spectrogram, T_lin: time steps of linear spectrogra, T_dec, time steps of decoder, T_enc: time steps of encoder.)
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"""
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if hasattr(self, "speaker_embedding"):
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speaker_embed = self.speaker_embedding(speaker_indices)
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else:
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speaker_embed = None
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keys, values = self.encoder(text_sequences, speaker_embed)
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mel_outputs, alignments, done, decoder_states = self.decoder.decode(
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(keys, values), text_positions, speaker_embed)
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linear_outputs = self.converter(decoder_states
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if self.use_decoder_states else
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mel_outputs, speaker_embed)
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return mel_outputs, linear_outputs, alignments, done
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