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323 lines
13 KiB
323 lines
13 KiB
# Copyright (c) 2023 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 collections import defaultdict
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from turtle import Turtle
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from typing import Dict
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from typing import List
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from typing import Tuple
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddlespeech.s2t.models.wav2vec2.modules.modeling_wav2vec2 import Wav2Vec2ConfigPure
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from paddlespeech.s2t.models.wav2vec2.modules.modeling_wav2vec2 import Wav2Vec2Model
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from paddlespeech.s2t.models.wav2vec2.modules.VanillaNN import VanillaNN
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from paddlespeech.s2t.models.wav2vec2.processing.speech_augmentation import SpecAugment
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from paddlespeech.s2t.modules.ctc import CTCDecoderBase as CTC
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from paddlespeech.s2t.modules.initializer import DefaultInitializerContext
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from paddlespeech.s2t.utils.ctc_utils import remove_duplicates_and_blank
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from paddlespeech.s2t.utils.utility import log_add
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class Wav2vec2ASR(nn.Layer):
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def __init__(self, config: dict):
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super().__init__()
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init_type = config.get("init_type", None)
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with DefaultInitializerContext(init_type):
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self.config = config
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wav2vec2_config = Wav2Vec2ConfigPure(config)
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wav2vec2 = Wav2Vec2Model(wav2vec2_config)
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self.normalize_wav = config.normalize_wav
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self.output_norm = config.output_norm
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if hasattr(config, 'spec_augment'):
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self.spec_augment = SpecAugment(**config.spec_augment)
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if config.freeze_wav2vec2:
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wav2vec2.eval()
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for parm in wav2vec2.parameters():
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parm.trainable = False
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self.wav2vec2 = wav2vec2
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self.enc = VanillaNN(**config.enc)
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self.ctc = CTC(**config.ctc,
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odim=config.output_dim,
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batch_average=False,
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reduction='mean')
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def forward(self, wav, wavs_lens_rate, target, target_lens):
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if self.normalize_wav:
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wav = F.layer_norm(wav, wav.shape)
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# Extract wav2vec output
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out = self.wav2vec2(wav)[0]
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# We normalize the output if required
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if self.output_norm:
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out = F.layer_norm(out, out.shape)
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if self.training and hasattr(self.config, 'spec_augment'):
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feats = self.spec_augment(out)
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else:
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feats = out
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x = self.enc(feats)
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x_lens = (wavs_lens_rate * x.shape[1]).round().astype(paddle.int64)
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ctc_loss = self.ctc(x, x_lens, target, target_lens)
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return ctc_loss
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@paddle.no_grad()
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def decode(self,
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feats: paddle.Tensor,
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text_feature: Dict[str, int],
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decoding_method: str,
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beam_size: int,
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tokenizer: str=None,
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sb_pipeline=False):
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batch_size = feats.shape[0]
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if decoding_method == 'ctc_prefix_beam_search' and batch_size > 1:
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logger.error(
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f"decoding mode {decoding_method} must be running with batch_size == 1"
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)
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logger.error(f"current batch_size is {batch_size}")
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if decoding_method == 'ctc_greedy_search':
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if tokenizer is None and sb_pipeline is False:
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hyps = self.ctc_greedy_search(feats)
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res = [text_feature.defeaturize(hyp) for hyp in hyps]
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res_tokenids = [hyp for hyp in hyps]
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else:
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if sb_pipeline is True:
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hyps = self.ctc_greedy_search(feats.unsqueeze(-1))
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else:
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hyps = self.ctc_greedy_search(feats)
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res = []
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res_tokenids = []
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for sequence in hyps:
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# Decode token terms to words
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predicted_tokens = text_feature.convert_ids_to_tokens(
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sequence)
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tmp_res = []
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tmp_res_tokenids = []
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for c in predicted_tokens:
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if c == "[CLS]":
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continue
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elif c == "[SEP]" or c == "[PAD]":
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break
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else:
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tmp_res.append(c)
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tmp_res_tokenids.append(text_feature.vocab[c])
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res.append(''.join(tmp_res))
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res_tokenids.append(tmp_res_tokenids)
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# ctc_prefix_beam_search and attention_rescoring only return one
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# result in List[int], change it to List[List[int]] for compatible
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# with other batch decoding mode
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elif decoding_method == 'ctc_prefix_beam_search':
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assert feats.shape[0] == 1
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if tokenizer is None and sb_pipeline is False:
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hyp = self.ctc_prefix_beam_search(feats, beam_size)
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res = [text_feature.defeaturize(hyp)]
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res_tokenids = [hyp]
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else:
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if sb_pipeline is True:
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hyp = self.ctc_prefix_beam_search(
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feats.unsqueeze(-1), beam_size)
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else:
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hyp = self.ctc_prefix_beam_search(feats, beam_size)
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res = []
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res_tokenids = []
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predicted_tokens = text_feature.convert_ids_to_tokens(hyp)
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tmp_res = []
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tmp_res_tokenids = []
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for c in predicted_tokens:
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if c == "[CLS]":
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continue
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elif c == "[SEP]" or c == "[PAD]":
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break
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else:
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tmp_res.append(c)
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tmp_res_tokenids.append(text_feature.vocab[c])
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res.append(''.join(tmp_res))
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res_tokenids.append(tmp_res_tokenids)
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else:
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raise ValueError(
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f"wav2vec2 not support decoding method: {decoding_method}")
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return res, res_tokenids
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@classmethod
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def from_config(cls, config):
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model = cls(config)
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return model
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def ctc_greedy_search(self, wav) -> List[List[int]]:
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""" Apply CTC greedy search
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Args:
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speech (paddle.Tensor): (batch, max_len)
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speech_length (paddle.Tensor): (batch, )
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Returns:
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List[List[int]]: best path result
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"""
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batch_size = wav.shape[0]
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wav = wav[:, :, 0]
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if self.normalize_wav:
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wav = F.layer_norm(wav, wav.shape[1:])
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# Extract wav2vec output
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out = self.wav2vec2(wav)[0]
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# We normalize the output if required
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if self.output_norm:
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out = F.layer_norm(out, out.shape[1:])
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feats = out
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x = self.enc(feats)
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x_lens = x.shape[1]
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ctc_probs = self.ctc.log_softmax(x) # (B, maxlen, vocab_size)
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topk_prob, topk_index = ctc_probs.topk(1, axis=2) # (B, maxlen, 1)
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topk_index = topk_index.view(batch_size, x_lens) # (B, maxlen)
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hyps = [hyp.tolist() for hyp in topk_index]
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hyps = [remove_duplicates_and_blank(hyp) for hyp in hyps]
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return hyps
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def _ctc_prefix_beam_search(
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self,
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wav,
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beam_size,
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blank_id: int=0, ) -> Tuple[List[Tuple[int, float]], paddle.Tensor]:
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""" CTC prefix beam search inner implementation
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Args:
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speech (paddle.Tensor): (batch, max_len, feat_dim)
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speech_length (paddle.Tensor): (batch, )
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beam_size (int): beam size for beam search
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decoding_chunk_size (int): decoding chunk for dynamic chunk
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trained model.
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<0: for decoding, use full chunk.
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>0: for decoding, use fixed chunk size as set.
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0: used for training, it's prohibited here
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simulate_streaming (bool): whether do encoder forward in a
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streaming fashion
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Returns:
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List[Tuple[int, float]]: nbest results, (N,1), (text, likelihood)
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paddle.Tensor: encoder output, (1, max_len, encoder_dim),
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it will be used for rescoring in attention rescoring mode
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"""
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wav = wav[:, :, 0]
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if self.normalize_wav:
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wav = F.layer_norm(wav, wav.shape[1:])
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# Extract wav2vec output
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out = self.wav2vec2(wav)[0]
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# We normalize the output if required
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if self.output_norm:
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out = F.layer_norm(out, out.shape[1:])
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feats = out
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x = self.enc(feats)
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maxlen = x.shape[1]
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ctc_probs = self.ctc.log_softmax(x) # (1, maxlen, vocab_size)
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ctc_probs = ctc_probs.squeeze(0)
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# cur_hyps: (prefix, (blank_ending_score, none_blank_ending_score))
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# blank_ending_score and none_blank_ending_score in ln domain
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cur_hyps = [(tuple(), (0.0, -float('inf')))]
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# 2. CTC beam search step by step
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for t in range(0, maxlen):
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logp = ctc_probs[t] # (vocab_size,)
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# key: prefix, value (pb, pnb), default value(-inf, -inf)
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next_hyps = defaultdict(lambda: (-float('inf'), -float('inf')))
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# 2.1 First beam prune: select topk best
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top_k_logp, top_k_index = logp.topk(beam_size) # (beam_size,)
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for s in top_k_index:
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s = s.item()
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ps = logp[s].item()
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for prefix, (pb, pnb) in cur_hyps:
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last = prefix[-1] if len(prefix) > 0 else None
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if s == blank_id: # blank
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n_pb, n_pnb = next_hyps[prefix]
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n_pb = log_add([n_pb, pb + ps, pnb + ps])
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next_hyps[prefix] = (n_pb, n_pnb)
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elif s == last:
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# Update *ss -> *s;
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n_pb, n_pnb = next_hyps[prefix]
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n_pnb = log_add([n_pnb, pnb + ps])
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next_hyps[prefix] = (n_pb, n_pnb)
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# Update *s-s -> *ss, - is for blank
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n_prefix = prefix + (s, )
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n_pb, n_pnb = next_hyps[n_prefix]
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n_pnb = log_add([n_pnb, pb + ps])
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next_hyps[n_prefix] = (n_pb, n_pnb)
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else:
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n_prefix = prefix + (s, )
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n_pb, n_pnb = next_hyps[n_prefix]
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n_pnb = log_add([n_pnb, pb + ps, pnb + ps])
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next_hyps[n_prefix] = (n_pb, n_pnb)
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# 2.2 Second beam prune
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next_hyps = sorted(
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next_hyps.items(),
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key=lambda x: log_add(list(x[1])),
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reverse=True)
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cur_hyps = next_hyps[:beam_size]
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hyps = [(y[0], log_add([y[1][0], y[1][1]])) for y in cur_hyps]
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return hyps
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def ctc_prefix_beam_search(self, wav, beam_size) -> List[int]:
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""" Apply CTC prefix beam search
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Args:
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speech (paddle.Tensor): (batch, max_len, feat_dim)
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speech_length (paddle.Tensor): (batch, )
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beam_size (int): beam size for beam search
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decoding_chunk_size (int): decoding chunk for dynamic chunk
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trained model.
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<0: for decoding, use full chunk.
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>0: for decoding, use fixed chunk size as set.
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0: used for training, it's prohibited here
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simulate_streaming (bool): whether do encoder forward in a
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streaming fashion
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Returns:
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List[int]: CTC prefix beam search nbest results
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"""
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hyps = self._ctc_prefix_beam_search(wav, beam_size)
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return hyps[0][0]
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class Wav2vec2Base(nn.Layer):
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"""Wav2vec2 model"""
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def __init__(self, config: dict):
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super().__init__()
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wav2vec2_config = Wav2Vec2ConfigPure(config)
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wav2vec2 = Wav2Vec2Model(wav2vec2_config)
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self.wav2vec2 = wav2vec2
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@classmethod
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def from_config(cls, configs: dict):
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"""init model.
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Args:
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configs (dict): config dict.
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Raises:
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ValueError: raise when using not support encoder type.
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Returns:
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nn.Layer: Wav2Vec2Base
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"""
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model = cls(configs)
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return model
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def forward(self, wav):
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out = self.wav2vec2(wav)
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return out
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