194 lines
7.9 KiB
Python
194 lines
7.9 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 math
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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.prenet import PreNet
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from parakeet.models.transformer_tts.post_convnet import PostConvNet
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class Decoder(dg.Layer):
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def __init__(self,
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num_hidden,
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num_mels=80,
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outputs_per_step=1,
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num_head=4,
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n_layers=3):
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"""Decoder layer of TransformerTTS.
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Args:
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num_hidden (int): the number of source vocabulary.
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n_mels (int, optional): the number of mel bands when calculating mel spectrograms. Defaults to 80.
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outputs_per_step (int, optional): the num of output frames per step . Defaults to 1.
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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(Decoder, 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()
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self.alpha = self.create_parameter(
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shape=(1, ),
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attr=param,
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dtype='float32',
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default_initializer=fluid.initializer.ConstantInitializer(
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value=1.0))
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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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padding_idx=0,
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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.decoder_prenet = PreNet(
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input_size=num_mels,
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hidden_size=num_hidden * 2,
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output_size=num_hidden,
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dropout_rate=0.2)
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k = math.sqrt(1.0 / num_hidden)
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self.linear = dg.Linear(
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num_hidden,
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num_hidden,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.XavierInitializer()),
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bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
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low=-k, high=k)))
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self.selfattn_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.selfattn_layers):
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self.add_sublayer("self_attn_{}".format(i), layer)
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self.attn_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.attn_layers):
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self.add_sublayer("attn_{}".format(i), layer)
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self.ffns = [
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PositionwiseFeedForward(
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num_hidden, num_hidden * num_head, filter_size=1)
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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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self.mel_linear = dg.Linear(
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num_hidden,
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num_mels * outputs_per_step,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.XavierInitializer()),
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bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
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low=-k, high=k)))
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self.stop_linear = dg.Linear(
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num_hidden,
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1,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.XavierInitializer()),
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bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
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low=-k, high=k)))
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self.postconvnet = PostConvNet(
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num_mels,
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num_hidden,
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filter_size=5,
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padding=4,
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num_conv=5,
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outputs_per_step=outputs_per_step,
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use_cudnn=True)
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def forward(self, key, value, query, positional, c_mask):
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"""
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Compute decoder outputs.
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Args:
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key (Variable): shape(B, T_text, C), dtype float32, the input key of decoder,
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where T_text means the timesteps of input text,
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value (Variable): shape(B, T_text, C), dtype float32, the input value of decoder.
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query (Variable): shape(B, T_mel, C), dtype float32, the input query of decoder,
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where T_mel means the timesteps of input spectrum,
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positional (Variable): shape(B, T_mel), dtype int64, the spectrum position.
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c_mask (Variable): shape(B, T_text, 1), dtype float32, query mask returned from encoder.
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Returns:
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mel_out (Variable): shape(B, T_mel, C), the decoder output after mel linear projection.
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out (Variable): shape(B, T_mel, C), the decoder output after post mel network.
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stop_tokens (Variable): shape(B, T_mel, 1), the stop tokens of output.
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attn_list (list[Variable]): len(n_layers), the encoder-decoder attention list.
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selfattn_list (list[Variable]): len(n_layers), the decoder self attention list.
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"""
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# get decoder mask with triangular matrix
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if fluid.framework._dygraph_tracer()._train_mode:
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mask = get_dec_attn_key_pad_mask(positional, self.num_head,
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query.dtype)
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m_mask = get_non_pad_mask(positional, self.num_head, query.dtype)
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zero_mask = layers.cast(c_mask == 0, dtype=query.dtype) * -1e30
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zero_mask = layers.transpose(zero_mask, perm=[0, 2, 1])
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else:
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len_q = query.shape[1]
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mask = layers.triu(
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layers.ones(
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shape=[len_q, len_q], dtype=query.dtype),
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diagonal=1)
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mask = layers.cast(mask != 0, dtype=query.dtype) * -1e30
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m_mask, zero_mask = None, None
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# Decoder pre-network
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query = self.decoder_prenet(query)
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# Centered position
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query = self.linear(query)
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# Get position embedding
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positional = self.pos_emb(positional)
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query = positional * self.alpha + query
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#positional dropout
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query = fluid.layers.dropout(
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query, 0.1, dropout_implementation='upscale_in_train')
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# Attention decoder-decoder, encoder-decoder
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selfattn_list = list()
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attn_list = list()
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for selfattn, attn, ffn in zip(self.selfattn_layers, self.attn_layers,
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self.ffns):
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query, attn_dec = selfattn(
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query, query, query, mask=mask, query_mask=m_mask)
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query, attn_dot = attn(
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key, value, query, mask=zero_mask, query_mask=m_mask)
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query = ffn(query)
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selfattn_list.append(attn_dec)
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attn_list.append(attn_dot)
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# Mel linear projection
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mel_out = self.mel_linear(query)
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# Post Mel Network
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out = self.postconvnet(mel_out)
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out = mel_out + out
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# Stop tokens
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stop_tokens = self.stop_linear(query)
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stop_tokens = layers.squeeze(stop_tokens, [-1])
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stop_tokens = layers.sigmoid(stop_tokens)
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return mel_out, out, attn_list, stop_tokens, selfattn_list
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