111 lines
4.4 KiB
Python
111 lines
4.4 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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import paddle.fluid.dygraph as dg
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import paddle.fluid as fluid
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from parakeet.models.transformer_tts.utils import *
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from parakeet.models.fastspeech.fft_block import FFTBlock
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class Encoder(dg.Layer):
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def __init__(self,
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n_src_vocab,
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len_max_seq,
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n_layers,
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n_head,
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d_k,
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d_q,
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d_model,
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d_inner,
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fft_conv1d_kernel,
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fft_conv1d_padding,
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dropout=0.1):
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"""Encoder layer of FastSpeech.
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Args:
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n_src_vocab (int): the number of source vocabulary.
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len_max_seq (int): the max mel len of sequence.
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n_layers (int): the layers number of FFTBlock.
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n_head (int): the head number of multihead attention.
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d_k (int): the dim of key in multihead attention.
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d_q (int): the dim of query in multihead attention.
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d_model (int): the dim of hidden layer in multihead attention.
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d_inner (int): the dim of hidden layer in ffn.
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fft_conv1d_kernel (int): the conv kernel size in FFTBlock.
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fft_conv1d_padding (int): the conv padding size in FFTBlock.
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dropout (float, optional): dropout probability of FFTBlock. Defaults to 0.1.
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"""
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super(Encoder, self).__init__()
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n_position = len_max_seq + 1
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self.n_head = n_head
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self.src_word_emb = dg.Embedding(
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size=[n_src_vocab, d_model],
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padding_idx=0,
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param_attr=fluid.initializer.Normal(
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loc=0.0, scale=1.0))
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self.pos_inp = get_sinusoid_encoding_table(
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n_position, d_model, padding_idx=0)
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self.position_enc = dg.Embedding(
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size=[n_position, d_model],
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.NumpyArrayInitializer(
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self.pos_inp),
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trainable=False))
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self.layer_stack = [
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FFTBlock(
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d_model,
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d_inner,
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n_head,
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d_k,
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d_q,
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fft_conv1d_kernel,
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fft_conv1d_padding,
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dropout=dropout) for _ in range(n_layers)
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]
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for i, layer in enumerate(self.layer_stack):
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self.add_sublayer('fft_{}'.format(i), layer)
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def forward(self, character, text_pos, non_pad_mask, slf_attn_mask=None):
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"""
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Encode text sequence.
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Args:
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character (Variable): shape(B, T_text), dtype float32, the input text characters,
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where T_text means the timesteps of input characters,
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text_pos (Variable): shape(B, T_text), dtype int64, the input text position.
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non_pad_mask (Variable): shape(B, T_text, 1), dtype int64, the mask with non pad.
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slf_attn_mask (Variable, optional): shape(B, T_text, T_text), dtype int64,
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the mask of input characters. Defaults to None.
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Returns:
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enc_output (Variable): shape(B, T_text, C), the encoder output.
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non_pad_mask (Variable): shape(B, T_text, 1), the mask with non pad.
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enc_slf_attn_list (list[Variable]): len(n_layers), the encoder self attention list.
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"""
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enc_slf_attn_list = []
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slf_attn_mask = layers.expand(slf_attn_mask, [self.n_head, 1, 1])
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# -- Forward
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enc_output = self.src_word_emb(character) + self.position_enc(
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text_pos) #(N, T, C)
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for enc_layer in self.layer_stack:
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enc_output, enc_slf_attn = enc_layer(
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enc_output,
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non_pad_mask=non_pad_mask,
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slf_attn_mask=slf_attn_mask)
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enc_slf_attn_list += [enc_slf_attn]
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return enc_output, enc_slf_attn_list
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