107 lines
4.2 KiB
Python
107 lines
4.2 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.modules.multihead_attention import MultiheadAttention
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from parakeet.modules.ffn import PositionwiseFeedForward
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from parakeet.models.transformer_tts.encoderprenet import EncoderPrenet
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class Encoder(dg.Layer):
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def __init__(self, embedding_size, num_hidden, num_head=4, n_layers=3):
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"""Encoder layer of TransformerTTS.
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Args:
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embedding_size (int): the size of position embedding.
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num_hidden (int): the size of hidden layer in network.
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num_head (int, optional): the head number of multihead attention. Defaults to 4.
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n_layers (int, optional): the layers number of multihead attention. Defaults to 3.
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"""
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super(Encoder, self).__init__()
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self.num_hidden = num_hidden
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self.num_head = num_head
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param = fluid.ParamAttr(initializer=fluid.initializer.Constant(
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value=1.0))
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self.alpha = self.create_parameter(
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shape=(1, ), attr=param, dtype='float32')
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self.pos_inp = get_sinusoid_encoding_table(
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1024, self.num_hidden, padding_idx=0)
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self.pos_emb = dg.Embedding(
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size=[1024, num_hidden],
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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.encoder_prenet = EncoderPrenet(
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embedding_size=embedding_size,
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num_hidden=num_hidden,
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use_cudnn=True)
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self.layers = [
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MultiheadAttention(num_hidden, num_hidden // num_head,
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num_hidden // num_head) for _ in range(n_layers)
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]
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for i, layer in enumerate(self.layers):
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self.add_sublayer("self_attn_{}".format(i), layer)
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self.ffns = [
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PositionwiseFeedForward(
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num_hidden,
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num_hidden * num_head,
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filter_size=1,
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use_cudnn=True) for _ in range(n_layers)
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]
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for i, layer in enumerate(self.ffns):
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self.add_sublayer("ffns_{}".format(i), layer)
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def forward(self, x, positional):
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"""
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Encode text sequence.
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Args:
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x (Variable): shape(B, T_text), dtype float32, the input character,
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where T_text means the timesteps of input text,
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positional (Variable): shape(B, T_text), dtype int64, the characters position.
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Returns:
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x (Variable): shape(B, T_text, C), the encoder output.
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attentions (list[Variable]): len(n_layers), the encoder self attention list.
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"""
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# Encoder pre_network
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x = self.encoder_prenet(x)
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if fluid.framework._dygraph_tracer()._train_mode:
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mask = get_attn_key_pad_mask(positional, self.num_head, x.dtype)
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query_mask = get_non_pad_mask(positional, self.num_head, x.dtype)
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else:
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query_mask, mask = None, None
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# Get positional encoding
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positional = self.pos_emb(positional)
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x = positional * self.alpha + x
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# Positional dropout
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x = layers.dropout(x, 0.1, dropout_implementation='upscale_in_train')
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# Self attention encoder
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attentions = list()
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for layer, ffn in zip(self.layers, self.ffns):
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x, attention = layer(x, x, x, mask=mask, query_mask=query_mask)
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x = ffn(x)
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attentions.append(attention)
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return x, attentions, query_mask
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