Hui Zhang
3 years ago
parent
842f181a8d
commit
1cd4d4bf83
@ -1,262 +0,0 @@
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# Copyright (c) 2021 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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"""Deepspeech2 ASR Model"""
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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 yacs.config import CfgNode
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from deepspeech.modules.conv import ConvStack
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from deepspeech.modules.ctc import CTCDecoder
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from deepspeech.modules.rnn import RNNStack
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from deepspeech.utils import layer_tools
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from deepspeech.utils.checkpoint import Checkpoint
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from deepspeech.utils.log import Log
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logger = Log(__name__).getlog()
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__all__ = ['DeepSpeech2Model']
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class CRNNEncoder(nn.Layer):
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def __init__(self,
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feat_size,
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dict_size,
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num_conv_layers=2,
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num_rnn_layers=3,
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rnn_size=1024,
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use_gru=False,
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share_rnn_weights=True):
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super().__init__()
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self.rnn_size = rnn_size
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self.feat_size = feat_size # 161 for linear
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self.dict_size = dict_size
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self.conv = ConvStack(feat_size, num_conv_layers)
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i_size = self.conv.output_height # H after conv stack
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self.rnn = RNNStack(
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i_size=i_size,
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h_size=rnn_size,
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num_stacks=num_rnn_layers,
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use_gru=use_gru,
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share_rnn_weights=share_rnn_weights)
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@property
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def output_size(self):
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return self.rnn_size * 2
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def forward(self, audio, audio_len):
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"""Compute Encoder outputs
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Args:
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audio (Tensor): [B, Tmax, D]
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text (Tensor): [B, Umax]
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audio_len (Tensor): [B]
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text_len (Tensor): [B]
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Returns:
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x (Tensor): encoder outputs, [B, T, D]
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x_lens (Tensor): encoder length, [B]
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"""
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# [B, T, D] -> [B, D, T]
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audio = audio.transpose([0, 2, 1])
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# [B, D, T] -> [B, C=1, D, T]
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x = audio.unsqueeze(1)
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x_lens = audio_len
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# convolution group
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x, x_lens = self.conv(x, x_lens)
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# convert data from convolution feature map to sequence of vectors
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#B, C, D, T = paddle.shape(x) # not work under jit
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x = x.transpose([0, 3, 1, 2]) #[B, T, C, D]
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#x = x.reshape([B, T, C * D]) #[B, T, C*D] # not work under jit
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x = x.reshape([0, 0, -1]) #[B, T, C*D]
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# remove padding part
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x, x_lens = self.rnn(x, x_lens) #[B, T, D]
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return x, x_lens
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class DeepSpeech2Model(nn.Layer):
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"""The DeepSpeech2 network structure.
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:param audio_data: Audio spectrogram data layer.
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:type audio_data: Variable
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:param text_data: Transcription text data layer.
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:type text_data: Variable
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:param audio_len: Valid sequence length data layer.
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:type audio_len: Variable
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:param masks: Masks data layer to reset padding.
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:type masks: Variable
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:param dict_size: Dictionary size for tokenized transcription.
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:type dict_size: int
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:param num_conv_layers: Number of stacking convolution layers.
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:type num_conv_layers: int
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:param num_rnn_layers: Number of stacking RNN layers.
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:type num_rnn_layers: int
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:param rnn_size: RNN layer size (dimension of RNN cells).
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:type rnn_size: int
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:param use_gru: Use gru if set True. Use simple rnn if set False.
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:type use_gru: bool
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:param share_rnn_weights: Whether to share input-hidden weights between
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forward and backward direction RNNs.
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It is only available when use_gru=False.
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:type share_weights: bool
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:return: A tuple of an output unnormalized log probability layer (
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before softmax) and a ctc cost layer.
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:rtype: tuple of LayerOutput
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"""
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@classmethod
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def params(cls, config: Optional[CfgNode]=None) -> CfgNode:
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default = CfgNode(
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dict(
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num_conv_layers=2, #Number of stacking convolution layers.
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num_rnn_layers=3, #Number of stacking RNN layers.
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rnn_layer_size=1024, #RNN layer size (number of RNN cells).
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use_gru=True, #Use gru if set True. Use simple rnn if set False.
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share_rnn_weights=True #Whether to share input-hidden weights between forward and backward directional RNNs.Notice that for GRU, weight sharing is not supported.
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))
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if config is not None:
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config.merge_from_other_cfg(default)
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return default
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def __init__(self,
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feat_size,
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dict_size,
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num_conv_layers=2,
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num_rnn_layers=3,
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rnn_size=1024,
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use_gru=False,
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share_rnn_weights=True):
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super().__init__()
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self.encoder = CRNNEncoder(
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feat_size=feat_size,
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dict_size=dict_size,
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num_conv_layers=num_conv_layers,
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num_rnn_layers=num_rnn_layers,
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rnn_size=rnn_size,
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use_gru=use_gru,
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share_rnn_weights=share_rnn_weights)
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assert (self.encoder.output_size == rnn_size * 2)
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self.decoder = CTCDecoder(
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odim=dict_size, # <blank> is in vocab
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enc_n_units=self.encoder.output_size,
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blank_id=0, # first token is <blank>
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dropout_rate=0.0,
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reduction=True, # sum
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batch_average=True) # sum / batch_size
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def forward(self, audio, audio_len, text, text_len):
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"""Compute Model loss
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Args:
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audio (Tenosr): [B, T, D]
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audio_len (Tensor): [B]
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text (Tensor): [B, U]
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text_len (Tensor): [B]
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Returns:
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loss (Tenosr): [1]
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"""
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eouts, eouts_len = self.encoder(audio, audio_len)
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loss = self.decoder(eouts, eouts_len, text, text_len)
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return loss
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@paddle.no_grad()
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def decode(self, audio, audio_len, vocab_list, decoding_method,
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lang_model_path, beam_alpha, beam_beta, beam_size, cutoff_prob,
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cutoff_top_n, num_processes):
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# init once
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# decoders only accept string encoded in utf-8
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self.decoder.init_decode(
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beam_alpha=beam_alpha,
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beam_beta=beam_beta,
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lang_model_path=lang_model_path,
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vocab_list=vocab_list,
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decoding_method=decoding_method)
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eouts, eouts_len = self.encoder(audio, audio_len)
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probs = self.decoder.softmax(eouts)
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return self.decoder.decode_probs(
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probs.numpy(), eouts_len, vocab_list, decoding_method,
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lang_model_path, beam_alpha, beam_beta, beam_size, cutoff_prob,
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cutoff_top_n, num_processes)
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@classmethod
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def from_pretrained(cls, dataloader, config, checkpoint_path):
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"""Build a DeepSpeech2Model model from a pretrained model.
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Parameters
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----------
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dataloader: paddle.io.DataLoader
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config: yacs.config.CfgNode
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model configs
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checkpoint_path: Path or str
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the path of pretrained model checkpoint, without extension name
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Returns
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-------
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DeepSpeech2Model
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The model built from pretrained result.
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"""
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model = cls(feat_size=dataloader.collate_fn.feature_size,
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dict_size=dataloader.collate_fn.vocab_size,
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num_conv_layers=config.model.num_conv_layers,
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num_rnn_layers=config.model.num_rnn_layers,
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rnn_size=config.model.rnn_layer_size,
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use_gru=config.model.use_gru,
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share_rnn_weights=config.model.share_rnn_weights)
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infos = Checkpoint().load_parameters(
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model, checkpoint_path=checkpoint_path)
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logger.info(f"checkpoint info: {infos}")
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layer_tools.summary(model)
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return model
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class DeepSpeech2InferModel(DeepSpeech2Model):
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def __init__(self,
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feat_size,
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dict_size,
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num_conv_layers=2,
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num_rnn_layers=3,
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rnn_size=1024,
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use_gru=False,
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share_rnn_weights=True):
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super().__init__(
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feat_size=feat_size,
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dict_size=dict_size,
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num_conv_layers=num_conv_layers,
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num_rnn_layers=num_rnn_layers,
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rnn_size=rnn_size,
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use_gru=use_gru,
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share_rnn_weights=share_rnn_weights)
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def forward(self, audio, audio_len):
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"""export model function
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Args:
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audio (Tensor): [B, T, D]
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audio_len (Tensor): [B]
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Returns:
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probs: probs after softmax
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"""
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eouts, eouts_len = self.encoder(audio, audio_len)
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probs = self.decoder.softmax(eouts)
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return probs
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@ -0,0 +1,81 @@
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# Copyright (c) 2021 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 Any
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from typing import Dict
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from typing import Text
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from paddle.optimizer import Optimizer
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from paddle.regularizer import L2Decay
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from deepspeech.training.gradclip import ClipGradByGlobalNormWithLog
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from deepspeech.utils.dynamic_import import dynamic_import
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from deepspeech.utils.log import Log
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__all__ = ["OptimizerFactory"]
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logger = Log(__name__).getlog()
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OPTIMIZER_DICT = {
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"sgd": "paddle.optimizer:SGD",
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"momentum": "paddle.optimizer:Momentum",
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"adadelta": "paddle.optimizer:Adadelta",
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"adam": "paddle.optimizer:Adam",
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"adamw": "paddle.optimizer:AdamW",
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}
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def register_optimizer(cls):
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"""Register optimizer."""
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alias = cls.__name__.lower()
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OPTIMIZER_DICT[cls.__name__.lower()] = cls.__module__ + ":" + cls.__name__
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return cls
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def dynamic_import_optimizer(module):
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"""Import Optimizer class dynamically.
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Args:
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module (str): module_name:class_name or alias in `OPTIMIZER_DICT`
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Returns:
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type: Optimizer class
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"""
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module_class = dynamic_import(module, OPTIMIZER_DICT)
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assert issubclass(module_class,
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Optimizer), f"{module} does not implement Optimizer"
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return module_class
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class OptimizerFactory():
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@classmethod
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def from_args(cls, name: str, args: Dict[Text, Any]):
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assert "parameters" in args, "parameters not in args."
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assert "learning_rate" in args, "learning_rate not in args."
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grad_clip = ClipGradByGlobalNormWithLog(
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args['grad_clip']) if "grad_clip" in args else None
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weight_decay = L2Decay(
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args['weight_decay']) if "weight_decay" in args else None
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module_class = dynamic_import_optimizer(name.lower())
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if weight_decay:
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logger.info(f'WeightDecay: {weight_decay}')
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if grad_clip:
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logger.info(f'GradClip: {grad_clip}')
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logger.info(
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f"Optimizer: {module_class.__name__} {args['learning_rate']}")
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args.update({"grad_clip": grad_clip, "weight_decay": weight_decay})
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return module_class(**args)
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@ -0,0 +1,50 @@
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# Copyright (c) 2021 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 importlib
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from typing import Any
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from typing import Dict
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from typing import Text
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from deepspeech.utils.log import Log
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logger = Log(__name__).getlog()
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__all__ = ["dynamic_import", "instance_class"]
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def dynamic_import(import_path, alias=dict()):
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"""dynamic import module and class
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:param str import_path: syntax 'module_name:class_name'
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e.g., 'deepspeech.models.u2:U2Model'
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:param dict alias: shortcut for registered class
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:return: imported class
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"""
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if import_path not in alias and ":" not in import_path:
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raise ValueError("import_path should be one of {} or "
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'include ":", e.g. "deepspeech.models.u2:U2Model" : '
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"{}".format(set(alias), import_path))
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if ":" not in import_path:
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import_path = alias[import_path]
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module_name, objname = import_path.split(":")
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m = importlib.import_module(module_name)
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return getattr(m, objname)
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def instance_class(module_class, args: Dict[Text, Any]):
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# filter out `val` which is None
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new_args = {key: val for key, val in args.items() if val is not None}
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logger.info(f"Instance: {module_class.__name__} {new_args}.")
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return module_class(**new_args)
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