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706 lines
29 KiB
706 lines
29 KiB
# Copyright (c) 2022 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 typing import Dict
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from typing import List
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from typing import Optional
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import paddle
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from paddle import nn
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from paddlespeech.t2s.modules.activation import get_activation
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from paddlespeech.t2s.modules.conformer.convolution import ConvolutionModule
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from paddlespeech.t2s.modules.conformer.encoder_layer import EncoderLayer
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from paddlespeech.t2s.modules.layer_norm import LayerNorm
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from paddlespeech.t2s.modules.masked_fill import masked_fill
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from paddlespeech.t2s.modules.nets_utils import initialize
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from paddlespeech.t2s.modules.tacotron2.decoder import Postnet
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from paddlespeech.t2s.modules.transformer.attention import LegacyRelPositionMultiHeadedAttention
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from paddlespeech.t2s.modules.transformer.attention import MultiHeadedAttention
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from paddlespeech.t2s.modules.transformer.attention import RelPositionMultiHeadedAttention
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from paddlespeech.t2s.modules.transformer.embedding import LegacyRelPositionalEncoding
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from paddlespeech.t2s.modules.transformer.embedding import PositionalEncoding
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from paddlespeech.t2s.modules.transformer.embedding import RelPositionalEncoding
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from paddlespeech.t2s.modules.transformer.embedding import ScaledPositionalEncoding
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from paddlespeech.t2s.modules.transformer.multi_layer_conv import Conv1dLinear
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from paddlespeech.t2s.modules.transformer.multi_layer_conv import MultiLayeredConv1d
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from paddlespeech.t2s.modules.transformer.positionwise_feed_forward import PositionwiseFeedForward
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from paddlespeech.t2s.modules.transformer.repeat import repeat
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from paddlespeech.t2s.modules.transformer.subsampling import Conv2dSubsampling
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# MLM -> Mask Language Model
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class mySequential(nn.Sequential):
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def forward(self, *inputs):
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for module in self._sub_layers.values():
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if type(inputs) == tuple:
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inputs = module(*inputs)
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else:
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inputs = module(inputs)
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return inputs
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class MaskInputLayer(nn.Layer):
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def __init__(self, out_features: int) -> None:
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super().__init__()
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self.mask_feature = paddle.create_parameter(
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shape=(1, 1, out_features),
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dtype=paddle.float32,
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default_initializer=paddle.nn.initializer.Assign(
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paddle.normal(shape=(1, 1, out_features))))
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def forward(self, input: paddle.Tensor,
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masked_pos: paddle.Tensor=None) -> paddle.Tensor:
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masked_pos = paddle.expand_as(paddle.unsqueeze(masked_pos, -1), input)
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masked_input = masked_fill(input, masked_pos, 0) + masked_fill(
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paddle.expand_as(self.mask_feature, input), ~masked_pos, 0)
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return masked_input
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class MLMEncoder(nn.Layer):
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"""Conformer encoder module.
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Args:
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idim (int): Input dimension.
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attention_dim (int): Dimension of attention.
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attention_heads (int): The number of heads of multi head attention.
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linear_units (int): The number of units of position-wise feed forward.
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num_blocks (int): The number of decoder blocks.
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dropout_rate (float): Dropout rate.
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positional_dropout_rate (float): Dropout rate after adding positional encoding.
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attention_dropout_rate (float): Dropout rate in attention.
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input_layer (Union[str, paddle.nn.Layer]): Input layer type.
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normalize_before (bool): Whether to use layer_norm before the first block.
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concat_after (bool): Whether to concat attention layer's input and output.
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if True, additional linear will be applied.
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i.e. x -> x + linear(concat(x, att(x)))
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if False, no additional linear will be applied. i.e. x -> x + att(x)
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positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
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positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
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macaron_style (bool): Whether to use macaron style for positionwise layer.
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pos_enc_layer_type (str): Encoder positional encoding layer type.
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selfattention_layer_type (str): Encoder attention layer type.
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activation_type (str): Encoder activation function type.
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use_cnn_module (bool): Whether to use convolution module.
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zero_triu (bool): Whether to zero the upper triangular part of attention matrix.
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cnn_module_kernel (int): Kernerl size of convolution module.
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padding_idx (int): Padding idx for input_layer=embed.
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stochastic_depth_rate (float): Maximum probability to skip the encoder layer.
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"""
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def __init__(self,
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idim: int,
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vocab_size: int=0,
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pre_speech_layer: int=0,
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attention_dim: int=256,
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attention_heads: int=4,
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linear_units: int=2048,
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num_blocks: int=6,
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dropout_rate: float=0.1,
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positional_dropout_rate: float=0.1,
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attention_dropout_rate: float=0.0,
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input_layer: str="conv2d",
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normalize_before: bool=True,
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concat_after: bool=False,
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positionwise_layer_type: str="linear",
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positionwise_conv_kernel_size: int=1,
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macaron_style: bool=False,
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pos_enc_layer_type: str="abs_pos",
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pos_enc_class=None,
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selfattention_layer_type: str="selfattn",
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activation_type: str="swish",
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use_cnn_module: bool=False,
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zero_triu: bool=False,
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cnn_module_kernel: int=31,
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padding_idx: int=-1,
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stochastic_depth_rate: float=0.0,
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text_masking: bool=False):
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"""Construct an Encoder object."""
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super().__init__()
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self._output_size = attention_dim
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self.text_masking = text_masking
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if self.text_masking:
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self.text_masking_layer = MaskInputLayer(attention_dim)
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activation = get_activation(activation_type)
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if pos_enc_layer_type == "abs_pos":
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pos_enc_class = PositionalEncoding
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elif pos_enc_layer_type == "scaled_abs_pos":
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pos_enc_class = ScaledPositionalEncoding
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elif pos_enc_layer_type == "rel_pos":
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assert selfattention_layer_type == "rel_selfattn"
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pos_enc_class = RelPositionalEncoding
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elif pos_enc_layer_type == "legacy_rel_pos":
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pos_enc_class = LegacyRelPositionalEncoding
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assert selfattention_layer_type == "legacy_rel_selfattn"
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else:
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raise ValueError("unknown pos_enc_layer: " + pos_enc_layer_type)
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self.conv_subsampling_factor = 1
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if input_layer == "linear":
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self.embed = nn.Sequential(
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nn.Linear(idim, attention_dim),
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nn.LayerNorm(attention_dim),
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nn.Dropout(dropout_rate),
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nn.ReLU(),
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pos_enc_class(attention_dim, positional_dropout_rate), )
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elif input_layer == "conv2d":
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self.embed = Conv2dSubsampling(
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idim,
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attention_dim,
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dropout_rate,
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pos_enc_class(attention_dim, positional_dropout_rate), )
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self.conv_subsampling_factor = 4
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elif input_layer == "embed":
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self.embed = nn.Sequential(
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nn.Embedding(idim, attention_dim, padding_idx=padding_idx),
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pos_enc_class(attention_dim, positional_dropout_rate), )
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elif input_layer == "mlm":
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self.segment_emb = None
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self.speech_embed = mySequential(
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MaskInputLayer(idim),
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nn.Linear(idim, attention_dim),
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nn.LayerNorm(attention_dim),
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nn.ReLU(),
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pos_enc_class(attention_dim, positional_dropout_rate))
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self.text_embed = nn.Sequential(
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nn.Embedding(
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vocab_size, attention_dim, padding_idx=padding_idx),
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pos_enc_class(attention_dim, positional_dropout_rate), )
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elif input_layer == "sega_mlm":
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self.segment_emb = nn.Embedding(
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500, attention_dim, padding_idx=padding_idx)
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self.speech_embed = mySequential(
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MaskInputLayer(idim),
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nn.Linear(idim, attention_dim),
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nn.LayerNorm(attention_dim),
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nn.ReLU(),
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pos_enc_class(attention_dim, positional_dropout_rate))
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self.text_embed = nn.Sequential(
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nn.Embedding(
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vocab_size, attention_dim, padding_idx=padding_idx),
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pos_enc_class(attention_dim, positional_dropout_rate), )
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elif isinstance(input_layer, nn.Layer):
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self.embed = nn.Sequential(
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input_layer,
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pos_enc_class(attention_dim, positional_dropout_rate), )
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elif input_layer is None:
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self.embed = nn.Sequential(
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pos_enc_class(attention_dim, positional_dropout_rate))
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else:
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raise ValueError("unknown input_layer: " + input_layer)
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self.normalize_before = normalize_before
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# self-attention module definition
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if selfattention_layer_type == "selfattn":
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encoder_selfattn_layer = MultiHeadedAttention
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encoder_selfattn_layer_args = (attention_heads, attention_dim,
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attention_dropout_rate, )
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elif selfattention_layer_type == "legacy_rel_selfattn":
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assert pos_enc_layer_type == "legacy_rel_pos"
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encoder_selfattn_layer = LegacyRelPositionMultiHeadedAttention
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encoder_selfattn_layer_args = (attention_heads, attention_dim,
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attention_dropout_rate, )
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elif selfattention_layer_type == "rel_selfattn":
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assert pos_enc_layer_type == "rel_pos"
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encoder_selfattn_layer = RelPositionMultiHeadedAttention
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encoder_selfattn_layer_args = (attention_heads, attention_dim,
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attention_dropout_rate, zero_triu, )
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else:
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raise ValueError("unknown encoder_attn_layer: " +
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selfattention_layer_type)
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# feed-forward module definition
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if positionwise_layer_type == "linear":
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positionwise_layer = PositionwiseFeedForward
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positionwise_layer_args = (attention_dim, linear_units,
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dropout_rate, activation, )
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elif positionwise_layer_type == "conv1d":
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positionwise_layer = MultiLayeredConv1d
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positionwise_layer_args = (attention_dim, linear_units,
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positionwise_conv_kernel_size,
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dropout_rate, )
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elif positionwise_layer_type == "conv1d-linear":
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positionwise_layer = Conv1dLinear
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positionwise_layer_args = (attention_dim, linear_units,
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positionwise_conv_kernel_size,
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dropout_rate, )
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else:
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raise NotImplementedError("Support only linear or conv1d.")
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# convolution module definition
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convolution_layer = ConvolutionModule
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convolution_layer_args = (attention_dim, cnn_module_kernel, activation)
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self.encoders = repeat(
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num_blocks,
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lambda lnum: EncoderLayer(
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attention_dim,
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encoder_selfattn_layer(*encoder_selfattn_layer_args),
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positionwise_layer(*positionwise_layer_args),
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positionwise_layer(*positionwise_layer_args) if macaron_style else None,
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convolution_layer(*convolution_layer_args) if use_cnn_module else None,
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dropout_rate,
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normalize_before,
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concat_after,
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stochastic_depth_rate * float(1 + lnum) / num_blocks, ), )
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self.pre_speech_layer = pre_speech_layer
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self.pre_speech_encoders = repeat(
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self.pre_speech_layer,
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lambda lnum: EncoderLayer(
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attention_dim,
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encoder_selfattn_layer(*encoder_selfattn_layer_args),
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positionwise_layer(*positionwise_layer_args),
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positionwise_layer(*positionwise_layer_args) if macaron_style else None,
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convolution_layer(*convolution_layer_args) if use_cnn_module else None,
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dropout_rate,
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normalize_before,
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concat_after,
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stochastic_depth_rate * float(1 + lnum) / self.pre_speech_layer, ),
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)
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if self.normalize_before:
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self.after_norm = LayerNorm(attention_dim)
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def forward(self,
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speech: paddle.Tensor,
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text: paddle.Tensor,
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masked_pos: paddle.Tensor,
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speech_mask: paddle.Tensor=None,
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text_mask: paddle.Tensor=None,
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speech_seg_pos: paddle.Tensor=None,
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text_seg_pos: paddle.Tensor=None):
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"""Encode input sequence.
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"""
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if masked_pos is not None:
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speech = self.speech_embed(speech, masked_pos)
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else:
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speech = self.speech_embed(speech)
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if text is not None:
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text = self.text_embed(text)
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if speech_seg_pos is not None and text_seg_pos is not None and self.segment_emb:
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speech_seg_emb = self.segment_emb(speech_seg_pos)
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text_seg_emb = self.segment_emb(text_seg_pos)
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text = (text[0] + text_seg_emb, text[1])
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speech = (speech[0] + speech_seg_emb, speech[1])
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if self.pre_speech_encoders:
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speech, _ = self.pre_speech_encoders(speech, speech_mask)
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if text is not None:
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xs = paddle.concat([speech[0], text[0]], axis=1)
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xs_pos_emb = paddle.concat([speech[1], text[1]], axis=1)
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masks = paddle.concat([speech_mask, text_mask], axis=-1)
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else:
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xs = speech[0]
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xs_pos_emb = speech[1]
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masks = speech_mask
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xs, masks = self.encoders((xs, xs_pos_emb), masks)
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if isinstance(xs, tuple):
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xs = xs[0]
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if self.normalize_before:
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xs = self.after_norm(xs)
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return xs, masks
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class MLMDecoder(MLMEncoder):
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def forward(self, xs: paddle.Tensor, masks: paddle.Tensor):
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"""Encode input sequence.
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Args:
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xs (paddle.Tensor): Input tensor (#batch, time, idim).
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masks (paddle.Tensor): Mask tensor (#batch, time).
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Returns:
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paddle.Tensor: Output tensor (#batch, time, attention_dim).
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paddle.Tensor: Mask tensor (#batch, time).
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"""
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xs = self.embed(xs)
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xs, masks = self.encoders(xs, masks)
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if isinstance(xs, tuple):
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xs = xs[0]
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if self.normalize_before:
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xs = self.after_norm(xs)
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return xs, masks
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# encoder and decoder is nn.Layer, not str
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class MLM(nn.Layer):
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def __init__(self,
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odim: int,
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encoder: nn.Layer,
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decoder: Optional[nn.Layer],
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postnet_layers: int=0,
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postnet_chans: int=0,
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postnet_filts: int=0,
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text_masking: bool=False):
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super().__init__()
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self.odim = odim
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self.encoder = encoder
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self.decoder = decoder
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self.vocab_size = encoder.text_embed[0]._num_embeddings
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if self.decoder is None or not (hasattr(self.decoder,
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'output_layer') and
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self.decoder.output_layer is not None):
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self.sfc = nn.Linear(self.encoder._output_size, odim)
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else:
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self.sfc = None
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if text_masking:
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self.text_sfc = nn.Linear(
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self.encoder.text_embed[0]._embedding_dim,
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self.vocab_size,
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weight_attr=self.encoder.text_embed[0]._weight_attr)
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else:
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self.text_sfc = None
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self.postnet = (None if postnet_layers == 0 else Postnet(
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idim=self.encoder._output_size,
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odim=odim,
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n_layers=postnet_layers,
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n_chans=postnet_chans,
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n_filts=postnet_filts,
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use_batch_norm=True,
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dropout_rate=0.5, ))
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def inference(
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self,
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speech: paddle.Tensor,
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text: paddle.Tensor,
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masked_pos: paddle.Tensor,
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speech_mask: paddle.Tensor,
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text_mask: paddle.Tensor,
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speech_seg_pos: paddle.Tensor,
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text_seg_pos: paddle.Tensor,
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span_bdy: List[int],
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use_teacher_forcing: bool=True, ) -> List[paddle.Tensor]:
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'''
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Args:
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speech (paddle.Tensor): input speech (1, Tmax, D).
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text (paddle.Tensor): input text (1, Tmax2).
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masked_pos (paddle.Tensor): masked position of input speech (1, Tmax)
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speech_mask (paddle.Tensor): mask of speech (1, 1, Tmax).
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text_mask (paddle.Tensor): mask of text (1, 1, Tmax2).
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speech_seg_pos (paddle.Tensor): n-th phone of each mel, 0<=n<=Tmax2 (1, Tmax).
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text_seg_pos (paddle.Tensor): n-th phone of each phone, 0<=n<=Tmax2 (1, Tmax2).
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span_bdy (List[int]): masked mel boundary of input speech (2,)
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use_teacher_forcing (bool): whether to use teacher forcing
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Returns:
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List[Tensor]:
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eg:
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[Tensor(shape=[1, 181, 80]), Tensor(shape=[80, 80]), Tensor(shape=[1, 67, 80])]
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'''
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z_cache = None
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if use_teacher_forcing:
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before_outs, zs, *_ = self.forward(
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speech=speech,
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text=text,
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masked_pos=masked_pos,
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speech_mask=speech_mask,
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text_mask=text_mask,
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speech_seg_pos=speech_seg_pos,
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text_seg_pos=text_seg_pos)
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if zs is None:
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zs = before_outs
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speech = speech.squeeze(0)
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outs = [speech[:span_bdy[0]]]
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outs += [zs[0][span_bdy[0]:span_bdy[1]]]
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outs += [speech[span_bdy[1]:]]
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return outs
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return None
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class MLMEncAsDecoder(MLM):
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def forward(self,
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speech: paddle.Tensor,
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text: paddle.Tensor,
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masked_pos: paddle.Tensor,
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speech_mask: paddle.Tensor,
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text_mask: paddle.Tensor,
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speech_seg_pos: paddle.Tensor,
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text_seg_pos: paddle.Tensor):
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# feats: (Batch, Length, Dim)
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# -> encoder_out: (Batch, Length2, Dim2)
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encoder_out, h_masks = self.encoder(
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speech=speech,
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text=text,
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masked_pos=masked_pos,
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speech_mask=speech_mask,
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text_mask=text_mask,
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speech_seg_pos=speech_seg_pos,
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text_seg_pos=text_seg_pos)
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if self.decoder is not None:
|
|
zs, _ = self.decoder(encoder_out, h_masks)
|
|
else:
|
|
zs = encoder_out
|
|
speech_hidden_states = zs[:, :paddle.shape(speech)[1], :]
|
|
if self.sfc is not None:
|
|
before_outs = paddle.reshape(
|
|
self.sfc(speech_hidden_states),
|
|
(paddle.shape(speech_hidden_states)[0], -1, self.odim))
|
|
else:
|
|
before_outs = speech_hidden_states
|
|
if self.postnet is not None:
|
|
after_outs = before_outs + paddle.transpose(
|
|
self.postnet(paddle.transpose(before_outs, [0, 2, 1])),
|
|
[0, 2, 1])
|
|
else:
|
|
after_outs = None
|
|
return before_outs, after_outs, None
|
|
|
|
|
|
class MLMDualMaksing(MLM):
|
|
def forward(self,
|
|
speech: paddle.Tensor,
|
|
text: paddle.Tensor,
|
|
masked_pos: paddle.Tensor,
|
|
speech_mask: paddle.Tensor,
|
|
text_mask: paddle.Tensor,
|
|
speech_seg_pos: paddle.Tensor,
|
|
text_seg_pos: paddle.Tensor):
|
|
# feats: (Batch, Length, Dim)
|
|
# -> encoder_out: (Batch, Length2, Dim2)
|
|
encoder_out, h_masks = self.encoder(
|
|
speech=speech,
|
|
text=text,
|
|
masked_pos=masked_pos,
|
|
speech_mask=speech_mask,
|
|
text_mask=text_mask,
|
|
speech_seg_pos=speech_seg_pos,
|
|
text_seg_pos=text_seg_pos)
|
|
if self.decoder is not None:
|
|
zs, _ = self.decoder(encoder_out, h_masks)
|
|
else:
|
|
zs = encoder_out
|
|
speech_hidden_states = zs[:, :paddle.shape(speech)[1], :]
|
|
if self.text_sfc:
|
|
text_hiddent_states = zs[:, paddle.shape(speech)[1]:, :]
|
|
text_outs = paddle.reshape(
|
|
self.text_sfc(text_hiddent_states),
|
|
(paddle.shape(text_hiddent_states)[0], -1, self.vocab_size))
|
|
if self.sfc is not None:
|
|
before_outs = paddle.reshape(
|
|
self.sfc(speech_hidden_states),
|
|
(paddle.shape(speech_hidden_states)[0], -1, self.odim))
|
|
else:
|
|
before_outs = speech_hidden_states
|
|
if self.postnet is not None:
|
|
after_outs = before_outs + paddle.transpose(
|
|
self.postnet(paddle.transpose(before_outs, [0, 2, 1])),
|
|
[0, 2, 1])
|
|
else:
|
|
after_outs = None
|
|
return before_outs, after_outs, text_outs
|
|
|
|
|
|
class ErnieSAT(nn.Layer):
|
|
def __init__(
|
|
self,
|
|
# network structure related
|
|
idim: int,
|
|
odim: int,
|
|
postnet_layers: int=5,
|
|
postnet_filts: int=5,
|
|
postnet_chans: int=256,
|
|
use_scaled_pos_enc: bool=False,
|
|
encoder_type: str='conformer',
|
|
decoder_type: str='conformer',
|
|
enc_input_layer: str='sega_mlm',
|
|
enc_pre_speech_layer: int=0,
|
|
enc_cnn_module_kernel: int=7,
|
|
enc_attention_dim: int=384,
|
|
enc_attention_heads: int=2,
|
|
enc_linear_units: int=1536,
|
|
enc_num_blocks: int=4,
|
|
enc_dropout_rate: float=0.2,
|
|
enc_positional_dropout_rate: float=0.2,
|
|
enc_attention_dropout_rate: float=0.2,
|
|
enc_normalize_before: bool=True,
|
|
enc_macaron_style: bool=True,
|
|
enc_use_cnn_module: bool=True,
|
|
enc_selfattention_layer_type: str='legacy_rel_selfattn',
|
|
enc_activation_type: str='swish',
|
|
enc_pos_enc_layer_type: str='legacy_rel_pos',
|
|
enc_positionwise_layer_type: str='conv1d',
|
|
enc_positionwise_conv_kernel_size: int=3,
|
|
text_masking: bool=False,
|
|
dec_cnn_module_kernel: int=31,
|
|
dec_attention_dim: int=384,
|
|
dec_attention_heads: int=2,
|
|
dec_linear_units: int=1536,
|
|
dec_num_blocks: int=4,
|
|
dec_dropout_rate: float=0.2,
|
|
dec_positional_dropout_rate: float=0.2,
|
|
dec_attention_dropout_rate: float=0.2,
|
|
dec_macaron_style: bool=True,
|
|
dec_use_cnn_module: bool=True,
|
|
dec_selfattention_layer_type: str='legacy_rel_selfattn',
|
|
dec_activation_type: str='swish',
|
|
dec_pos_enc_layer_type: str='legacy_rel_pos',
|
|
dec_positionwise_layer_type: str='conv1d',
|
|
dec_positionwise_conv_kernel_size: int=3,
|
|
init_type: str="xavier_uniform", ):
|
|
super().__init__()
|
|
# store hyperparameters
|
|
self.odim = odim
|
|
|
|
self.use_scaled_pos_enc = use_scaled_pos_enc
|
|
|
|
# initialize parameters
|
|
initialize(self, init_type)
|
|
|
|
# Encoder
|
|
if encoder_type == "conformer":
|
|
encoder = MLMEncoder(
|
|
idim=odim,
|
|
vocab_size=idim,
|
|
pre_speech_layer=enc_pre_speech_layer,
|
|
attention_dim=enc_attention_dim,
|
|
attention_heads=enc_attention_heads,
|
|
linear_units=enc_linear_units,
|
|
num_blocks=enc_num_blocks,
|
|
dropout_rate=enc_dropout_rate,
|
|
positional_dropout_rate=enc_positional_dropout_rate,
|
|
attention_dropout_rate=enc_attention_dropout_rate,
|
|
input_layer=enc_input_layer,
|
|
normalize_before=enc_normalize_before,
|
|
positionwise_layer_type=enc_positionwise_layer_type,
|
|
positionwise_conv_kernel_size=enc_positionwise_conv_kernel_size,
|
|
macaron_style=enc_macaron_style,
|
|
pos_enc_layer_type=enc_pos_enc_layer_type,
|
|
selfattention_layer_type=enc_selfattention_layer_type,
|
|
activation_type=enc_activation_type,
|
|
use_cnn_module=enc_use_cnn_module,
|
|
cnn_module_kernel=enc_cnn_module_kernel,
|
|
text_masking=text_masking)
|
|
else:
|
|
raise ValueError(f"{encoder_type} is not supported.")
|
|
|
|
# Decoder
|
|
if decoder_type != 'no_decoder':
|
|
decoder = MLMDecoder(
|
|
idim=0,
|
|
input_layer=None,
|
|
cnn_module_kernel=dec_cnn_module_kernel,
|
|
attention_dim=dec_attention_dim,
|
|
attention_heads=dec_attention_heads,
|
|
linear_units=dec_linear_units,
|
|
num_blocks=dec_num_blocks,
|
|
dropout_rate=dec_dropout_rate,
|
|
positional_dropout_rate=dec_positional_dropout_rate,
|
|
macaron_style=dec_macaron_style,
|
|
use_cnn_module=dec_use_cnn_module,
|
|
selfattention_layer_type=dec_selfattention_layer_type,
|
|
activation_type=dec_activation_type,
|
|
pos_enc_layer_type=dec_pos_enc_layer_type,
|
|
positionwise_layer_type=dec_positionwise_layer_type,
|
|
positionwise_conv_kernel_size=dec_positionwise_conv_kernel_size)
|
|
|
|
else:
|
|
decoder = None
|
|
|
|
model_class = MLMDualMaksing if text_masking else MLMEncAsDecoder
|
|
|
|
self.model = model_class(
|
|
odim=odim,
|
|
encoder=encoder,
|
|
decoder=decoder,
|
|
postnet_layers=postnet_layers,
|
|
postnet_filts=postnet_filts,
|
|
postnet_chans=postnet_chans,
|
|
text_masking=text_masking)
|
|
|
|
nn.initializer.set_global_initializer(None)
|
|
|
|
def forward(self,
|
|
speech: paddle.Tensor,
|
|
text: paddle.Tensor,
|
|
masked_pos: paddle.Tensor,
|
|
speech_mask: paddle.Tensor,
|
|
text_mask: paddle.Tensor,
|
|
speech_seg_pos: paddle.Tensor,
|
|
text_seg_pos: paddle.Tensor):
|
|
return self.model(
|
|
speech=speech,
|
|
text=text,
|
|
masked_pos=masked_pos,
|
|
speech_mask=speech_mask,
|
|
text_mask=text_mask,
|
|
speech_seg_pos=speech_seg_pos,
|
|
text_seg_pos=text_seg_pos)
|
|
|
|
def inference(
|
|
self,
|
|
speech: paddle.Tensor,
|
|
text: paddle.Tensor,
|
|
masked_pos: paddle.Tensor,
|
|
speech_mask: paddle.Tensor,
|
|
text_mask: paddle.Tensor,
|
|
speech_seg_pos: paddle.Tensor,
|
|
text_seg_pos: paddle.Tensor,
|
|
span_bdy: List[int],
|
|
use_teacher_forcing: bool=True, ) -> Dict[str, paddle.Tensor]:
|
|
return self.model.inference(
|
|
speech=speech,
|
|
text=text,
|
|
masked_pos=masked_pos,
|
|
speech_mask=speech_mask,
|
|
text_mask=text_mask,
|
|
speech_seg_pos=speech_seg_pos,
|
|
text_seg_pos=text_seg_pos,
|
|
span_bdy=span_bdy,
|
|
use_teacher_forcing=use_teacher_forcing)
|
|
|
|
|
|
class ErnieSATInference(nn.Layer):
|
|
def __init__(self, normalizer, model):
|
|
super().__init__()
|
|
self.normalizer = normalizer
|
|
self.acoustic_model = model
|
|
|
|
def forward(
|
|
self,
|
|
speech: paddle.Tensor,
|
|
text: paddle.Tensor,
|
|
masked_pos: paddle.Tensor,
|
|
speech_mask: paddle.Tensor,
|
|
text_mask: paddle.Tensor,
|
|
speech_seg_pos: paddle.Tensor,
|
|
text_seg_pos: paddle.Tensor,
|
|
span_bdy: List[int],
|
|
use_teacher_forcing: bool=True, ):
|
|
outs = self.acoustic_model.inference(
|
|
speech=speech,
|
|
text=text,
|
|
masked_pos=masked_pos,
|
|
speech_mask=speech_mask,
|
|
text_mask=text_mask,
|
|
speech_seg_pos=speech_seg_pos,
|
|
text_seg_pos=text_seg_pos,
|
|
span_bdy=span_bdy,
|
|
use_teacher_forcing=use_teacher_forcing)
|
|
|
|
normed_mel_pre, normed_mel_masked, normed_mel_post = outs
|
|
logmel_pre = self.normalizer.inverse(normed_mel_pre)
|
|
logmel_masked = self.normalizer.inverse(normed_mel_masked)
|
|
logmel_post = self.normalizer.inverse(normed_mel_post)
|
|
return logmel_pre, logmel_masked, logmel_post
|