Refactor CTC module, add embedding and fix log (#549)
* add acts, refactor ctc, add pos embed * fix export, dataloader time log * fix egs * fix libri readmepull/550/head
Hui Zhang
4 years ago
committed by
GitHub
parent
00889bfaf2
commit
1539f3e0a3
@ -0,0 +1,238 @@
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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 logging
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from typeguard import check_argument_types
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import paddle
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from paddle import nn
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from paddle.nn import functional as F
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from paddle.nn import initializer as I
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from deepspeech.decoders.swig_wrapper import Scorer
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from deepspeech.decoders.swig_wrapper import ctc_greedy_decoder
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from deepspeech.decoders.swig_wrapper import ctc_beam_search_decoder_batch
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from deepspeech.modules.loss import CTCLoss
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logger = logging.getLogger(__name__)
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__all__ = ['CTCDecoder']
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class CTCDecoder(nn.Layer):
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def __init__(self,
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enc_n_units,
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odim,
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blank_id=0,
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dropout_rate: float=0.0,
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reduction: bool=True):
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"""CTC decoder
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Args:
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enc_n_units ([int]): encoder output dimention
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vocab_size ([int]): text vocabulary size
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dropout_rate (float): dropout rate (0.0 ~ 1.0)
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reduction (bool): reduce the CTC loss into a scalar
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"""
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assert check_argument_types()
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super().__init__()
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self.blank_id = blank_id
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self.odim = odim
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self.dropout_rate = dropout_rate
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self.ctc_lo = nn.Linear(enc_n_units, self.odim)
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reduction_type = "sum" if reduction else "none"
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self.criterion = CTCLoss(blank=self.blank_id, reduction=reduction_type)
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# CTCDecoder LM Score handle
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self._ext_scorer = None
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def forward(self, hs_pad, hlens, ys_pad, ys_lens):
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"""Calculate CTC loss.
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Args:
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hs_pad (Tensor): batch of padded hidden state sequences (B, Tmax, D)
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hlens (Tensor): batch of lengths of hidden state sequences (B)
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ys_pad (Tenosr): batch of padded character id sequence tensor (B, Lmax)
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ys_lens (Tensor): batch of lengths of character sequence (B)
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Returns:
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loss (Tenosr): scalar.
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"""
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logits = self.ctc_lo(F.dropout(hs_pad, p=self.dropout_rate))
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loss = self.criterion(logits, ys_pad, hlens, ys_lens)
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return loss
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def probs(self, eouts: paddle.Tensor, temperature: float=1.0):
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"""Get CTC probabilities.
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Args:
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eouts (FloatTensor): `[B, T, enc_units]`
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Returns:
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probs (FloatTensor): `[B, T, odim]`
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"""
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return F.softmax(self.ctc_lo(eouts) / temperature, axis=-1)
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def scores(self, eouts: paddle.Tensor, temperature: float=1.0):
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"""Get log-scale CTC probabilities.
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Args:
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eouts (FloatTensor): `[B, T, enc_units]`
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Returns:
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log_probs (FloatTensor): `[B, T, odim]`
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"""
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return F.log_softmax(self.ctc_lo(eouts) / temperature, axis=-1)
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def log_softmax(self, hs_pad: paddle.Tensor) -> paddle.Tensor:
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"""log_softmax of frame activations
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Args:
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Tensor hs_pad: 3d tensor (B, Tmax, eprojs)
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Returns:
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paddle.Tensor: log softmax applied 3d tensor (B, Tmax, odim)
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"""
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return self.scores(hs_pad)
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def argmax(self, hs_pad: paddle.Tensor) -> paddle.Tensor:
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"""argmax of frame activations
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Args:
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paddle.Tensor hs_pad: 3d tensor (B, Tmax, eprojs)
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Returns:
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paddle.Tensor: argmax applied 2d tensor (B, Tmax)
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"""
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return paddle.argmax(self.ctc_lo(hs_pad), dim=2)
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def _decode_batch_greedy(self, probs_split, vocab_list):
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"""Decode by best path for a batch of probs matrix input.
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:param probs_split: List of 2-D probability matrix, and each consists
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of prob vectors for one speech utterancce.
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:param probs_split: List of matrix
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:param vocab_list: List of tokens in the vocabulary, for decoding.
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:type vocab_list: list
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:return: List of transcription texts.
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:rtype: List of str
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"""
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results = []
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for i, probs in enumerate(probs_split):
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output_transcription = ctc_greedy_decoder(
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probs_seq=probs, vocabulary=vocab_list)
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results.append(output_transcription)
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return results
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def _init_ext_scorer(self, beam_alpha, beam_beta, language_model_path,
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vocab_list):
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"""Initialize the external scorer.
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:param beam_alpha: Parameter associated with language model.
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:type beam_alpha: float
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:param beam_beta: Parameter associated with word count.
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:type beam_beta: float
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:param language_model_path: Filepath for language model. If it is
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empty, the external scorer will be set to
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None, and the decoding method will be pure
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beam search without scorer.
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:type language_model_path: str|None
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:param vocab_list: List of tokens in the vocabulary, for decoding.
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:type vocab_list: list
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"""
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# init once
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if self._ext_scorer != None:
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return
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if language_model_path != '':
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logger.info("begin to initialize the external scorer "
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"for decoding")
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self._ext_scorer = Scorer(beam_alpha, beam_beta,
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language_model_path, vocab_list)
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lm_char_based = self._ext_scorer.is_character_based()
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lm_max_order = self._ext_scorer.get_max_order()
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lm_dict_size = self._ext_scorer.get_dict_size()
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logger.info("language model: "
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"is_character_based = %d," % lm_char_based +
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" max_order = %d," % lm_max_order + " dict_size = %d" %
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lm_dict_size)
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logger.info("end initializing scorer")
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else:
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self._ext_scorer = None
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logger.info("no language model provided, "
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"decoding by pure beam search without scorer.")
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def _decode_batch_beam_search(self, probs_split, beam_alpha, beam_beta,
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beam_size, cutoff_prob, cutoff_top_n,
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vocab_list, num_processes):
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"""Decode by beam search for a batch of probs matrix input.
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:param probs_split: List of 2-D probability matrix, and each consists
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of prob vectors for one speech utterancce.
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:param probs_split: List of matrix
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:param beam_alpha: Parameter associated with language model.
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:type beam_alpha: float
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:param beam_beta: Parameter associated with word count.
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:type beam_beta: float
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:param beam_size: Width for Beam search.
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:type beam_size: int
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:param cutoff_prob: Cutoff probability in pruning,
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default 1.0, no pruning.
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:type cutoff_prob: float
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:param cutoff_top_n: Cutoff number in pruning, only top cutoff_top_n
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characters with highest probs in vocabulary will be
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used in beam search, default 40.
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:type cutoff_top_n: int
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:param vocab_list: List of tokens in the vocabulary, for decoding.
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:type vocab_list: list
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:param num_processes: Number of processes (CPU) for decoder.
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:type num_processes: int
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:return: List of transcription texts.
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:rtype: List of str
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"""
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if self._ext_scorer != None:
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self._ext_scorer.reset_params(beam_alpha, beam_beta)
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# beam search decode
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num_processes = min(num_processes, len(probs_split))
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beam_search_results = ctc_beam_search_decoder_batch(
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probs_split=probs_split,
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vocabulary=vocab_list,
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beam_size=beam_size,
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num_processes=num_processes,
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ext_scoring_func=self._ext_scorer,
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cutoff_prob=cutoff_prob,
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cutoff_top_n=cutoff_top_n)
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results = [result[0][1] for result in beam_search_results]
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return results
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def init_decode(self, beam_alpha, beam_beta, lang_model_path, vocab_list,
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decoding_method):
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if decoding_method == "ctc_beam_search":
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self._init_ext_scorer(beam_alpha, beam_beta, lang_model_path,
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vocab_list)
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def decode_probs(self, probs, logits_lens, vocab_list, decoding_method,
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lang_model_path, beam_alpha, beam_beta, beam_size,
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cutoff_prob, cutoff_top_n, num_processes):
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""" probs: activation after softmax
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logits_len: audio output lens
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"""
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probs_split = [probs[i, :l, :] for i, l in enumerate(logits_lens)]
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if decoding_method == "ctc_greedy":
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result_transcripts = self._decode_batch_greedy(
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probs_split=probs_split, vocab_list=vocab_list)
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elif decoding_method == "ctc_beam_search":
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result_transcripts = self._decode_batch_beam_search(
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probs_split=probs_split,
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beam_alpha=beam_alpha,
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beam_beta=beam_beta,
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beam_size=beam_size,
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cutoff_prob=cutoff_prob,
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cutoff_top_n=cutoff_top_n,
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vocab_list=vocab_list,
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num_processes=num_processes)
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else:
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raise ValueError(f"Not support: {decoding_method}")
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return result_transcripts
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@ -0,0 +1,132 @@
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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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"""Positonal Encoding Module."""
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import math
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import logging
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import numpy as np
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from typing import Tuple
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import paddle
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from paddle import nn
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from paddle.nn import functional as F
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from paddle.nn import initializer as I
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logger = logging.getLogger(__name__)
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__all__ = ["PositionalEncoding", "RelPositionalEncoding"]
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# TODO(Hui Zhang): remove this hack
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paddle.float32 = 'float32'
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class PositionalEncoding(nn.Layer):
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def __init__(self,
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d_model: int,
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dropout_rate: float,
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max_len: int=5000,
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reverse: bool=False):
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"""Positional encoding.
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PE(pos, 2i) = sin(pos/(10000^(2i/dmodel)))
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PE(pos, 2i+1) = cos(pos/(10000^(2i/dmodel)))
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Args:
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d_model (int): embedding dim.
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dropout_rate (float): dropout rate.
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max_len (int, optional): maximum input length. Defaults to 5000.
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reverse (bool, optional): Not used. Defaults to False.
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"""
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super().__init__()
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self.d_model = d_model
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self.max_len = max_len
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self.xscale = paddle.to_tensor(math.sqrt(self.d_model))
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self.dropout = nn.Dropout(p=dropout_rate)
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self.pe = paddle.zeros(self.max_len, self.d_model) #[T,D]
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position = paddle.arange(
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0, self.max_len, dtype=paddle.float32).unsqueeze(1)
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div_term = paddle.exp(
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paddle.arange(0, self.d_model, 2, dtype=paddle.float32) *
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-(math.log(10000.0) / self.d_model))
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self.pe[:, 0::2] = paddle.sin(position * div_term)
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self.pe[:, 1::2] = paddle.cos(position * div_term)
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self.pe = self.pe.unsqueeze(0) #[1, T, D]
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def forward(self, x: paddle.Tensor,
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offset: int=0) -> Tuple[paddle.Tensor, paddle.Tensor]:
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"""Add positional encoding.
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Args:
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x (paddle.Tensor): Input. Its shape is (batch, time, ...)
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offset (int): position offset
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Returns:
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paddle.Tensor: Encoded tensor. Its shape is (batch, time, ...)
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paddle.Tensor: for compatibility to RelPositionalEncoding
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"""
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T = paddle.shape(x)[1]
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assert offset + T < self.max_len
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#assert offset + x.size(1) < self.max_len
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#self.pe = self.pe.to(x.device)
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#pos_emb = self.pe[:, offset:offset + x.size(1)]
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pos_emb = self.pe[:, offset:offset + T]
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x = x * self.xscale + pos_emb
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return self.dropout(x), self.dropout(pos_emb)
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def position_encoding(self, offset: int, size: int) -> paddle.Tensor:
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""" For getting encoding in a streaming fashion
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Attention!!!!!
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we apply dropout only once at the whole utterance level in a none
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streaming way, but will call this function several times with
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increasing input size in a streaming scenario, so the dropout will
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be applied several times.
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Args:
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offset (int): start offset
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size (int): requried size of position encoding
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Returns:
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paddle.Tensor: Corresponding encoding
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"""
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assert offset + size < self.max_len
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return self.dropout(self.pe[:, offset:offset + size])
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class RelPositionalEncoding(PositionalEncoding):
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"""Relative positional encoding module.
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See : Appendix B in https://arxiv.org/abs/1901.02860
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"""
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def __init__(self, d_model: int, dropout_rate: float, max_len: int=5000):
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"""
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Args:
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d_model (int): Embedding dimension.
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dropout_rate (float): Dropout rate.
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max_len (int, optional): [Maximum input length.]. Defaults to 5000.
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"""
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super().__init__(d_model, dropout_rate, max_len, reverse=True)
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def forward(self, x: paddle.Tensor,
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offset: int=0) -> Tuple[paddle.Tensor, paddle.Tensor]:
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"""Compute positional encoding.
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Args:
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x (paddle.Tensor): Input tensor (batch, time, `*`).
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Returns:
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paddle.Tensor: Encoded tensor (batch, time, `*`).
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paddle.Tensor: Positional embedding tensor (1, time, `*`).
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"""
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T = paddle.shape()[1]
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assert offset + T < self.max_len
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#assert offset + x.size(1) < self.max_len
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#self.pe = self.pe.to(x.device)
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x = x * self.xscale
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#pos_emb = self.pe[:, offset:offset + x.size(1)]
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pos_emb = self.pe[:, offset:offset + T]
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return self.dropout(x), self.dropout(pos_emb)
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@ -1,31 +0,0 @@
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#! /usr/bin/env bash
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# download language model
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bash local/download_lm_ch.sh
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if [ $? -ne 0 ]; then
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exit 1
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fi
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# download well-trained model
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bash local/download_model.sh
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if [ $? -ne 0 ]; then
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exit 1
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fi
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# infer
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CUDA_VISIBLE_DEVICES=0 \
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python3 -u ${BIN_DIR}/infer.py \
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--device 'gpu' \
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--nproc 1 \
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--config conf/deepspeech2.yaml \
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--checkpoint_path data/pretrain/params.pdparams \
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--opts data.mean_std_filepath data/pretrain/mean_std.npz \
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--opts data.vocab_filepath data/pretrain/vocab.txt
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if [ $? -ne 0 ]; then
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echo "Failed in inference!"
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exit 1
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fi
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exit 0
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@ -1,31 +0,0 @@
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#! /usr/bin/env bash
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# download language model
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bash local/download_lm_ch.sh
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if [ $? -ne 0 ]; then
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exit 1
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fi
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# download well-trained model
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bash local/download_model.sh
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if [ $? -ne 0 ]; then
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exit 1
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fi
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# evaluate model
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CUDA_VISIBLE_DEVICES=0 \
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python3 -u ${BIN_DIR}/test.py \
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--device 'gpu' \
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--nproc 1 \
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--config conf/deepspeech2.yaml \
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--checkpoint_path data/pretrain/params.pdparams \
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--opts data.mean_std_filepath data/pretrain/mean_std.npz \
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--opts data.vocab_filepath data/pretrain/vocab.txt
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if [ $? -ne 0 ]; then
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echo "Failed in evaluation!"
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exit 1
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fi
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exit 0
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@ -1,7 +1,7 @@
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# LibriSpeech
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## CTC
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| Model | Config | Test set | CER |
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| Model | Config | Test set | WER |
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| --- | --- | --- | --- |
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| DeepSpeech2 | conf/deepspeech2.yaml | test-clean | 0.073973 |
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| DeepSpeech2 | release 1.8.5 | test-clean | 0.074939 |
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@ -0,0 +1,20 @@
|
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#! /usr/bin/env bash
|
||||
|
||||
if [ $# != 2 ];then
|
||||
echo "usage: export ckpt_path jit_model_path"
|
||||
exit -1
|
||||
fi
|
||||
|
||||
python3 -u ${BIN_DIR}/export.py \
|
||||
--config conf/deepspeech2.yaml \
|
||||
--checkpoint_path ${1} \
|
||||
--export_path ${2}
|
||||
|
||||
|
||||
if [ $? -ne 0 ]; then
|
||||
echo "Failed in evaluation!"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
|
||||
exit 0
|
||||
Loading…
Reference in new issue