我们知道CTC是非自回归，而像transformer中解码是自回归的，所以transformer很大的一个缺陷就是解码速度慢。
在最近几年CTC和注意力机制联合训练得到的性能效果得到极大的提升，在训练过程中主要的操作就是将encoder的输出分别作为decoder的输入和CTC的输入，
通过两种不同的解码方式得到两个不同的损失函数，然后根据不同的权重大小将两个损失相加得到模型整体的损失函数，从而进行反向传播梯度更新参数。比如：
Loss = 0.3*lossctc + 0.6*lossattn
另外在这种联合训练的思想进一步发展，decoder在解码时标签输入可能语言模型这种强化作用，但是在打分过程中不存在这样的输入，所以WeNet中引用的这种重打分机制感觉就特别牛，
他的操作过程是encoder端的输出经过CTC prefix beam search，得到n个最好的结果，然后将这n个最好的结果作为decoder的标签输入，这样就完美解决了在打分阶段decoder端没有先验知识的问题。
当然在这里需要CTC得到的结果越好对最终的打分结果更好，这个_ctc_prefix_beam_search也是非常有意思的，他的搜索方法很有意思。
代码如下：
def _ctc_prefix_beam_search( self, speech: torch.Tensor, speech_lengths: torch.Tensor, beam_size: int, decoding_chunk_size: int = -1, num_decoding_left_chunks: int = -1, simulate_streaming: bool = False, ) -> Tuple[List[List[int]], torch.Tensor]: """ CTC prefix beam search inner implementation Args: speech (torch.Tensor): (batch, max_len, feat_dim) speech_length (torch.Tensor): (batch, ) beam_size (int): beam size for beam search decoding_chunk_size (int): decoding chunk for dynamic chunk trained model. <0: for decoding, use full chunk. >0: for decoding, use fixed chunk size as set. 0: used for training, it's prohibited here simulate_streaming (bool): whether do encoder forward in a streaming fashion Returns: List[List[int]]: nbest results torch.Tensor: encoder output, (1, max_len, encoder_dim), it will be used for rescoring in attention rescoring mode """ assert speech.shape[0] == speech_lengths.shape[0] assert decoding_chunk_size != 0 batch_size = speech.shape[0] # For CTC prefix beam search, we only support batch_size=1 assert batch_size == 1 # Let's assume B = batch_size and N = beam_size # 1. Encoder forward and get CTC score encoder_out, encoder_mask = self._forward_encoder( speech, speech_lengths, decoding_chunk_size, num_decoding_left_chunks, simulate_streaming) # (B, maxlen, encoder_dim) maxlen = encoder_out.size(1) ctc_probs = self.ctc.log_softmax( encoder_out) # (1, maxlen, vocab_size) ctc_probs = ctc_probs.squeeze(0) # cur_hyps: (prefix, (blank_ending_score, none_blank_ending_score)) cur_hyps = [(tuple(), (0.0, -float('inf')))] # 2. CTC beam search step by step for t in range(0, maxlen): logp = ctc_probs[t] # (vocab_size,) # key: prefix, value (pb, pnb), default value(-inf, -inf) next_hyps = defaultdict(lambda: (-float('inf'), -float('inf'))) # 2.1 First beam prune: select topk best top_k_logp, top_k_index = logp.topk(beam_size) # (beam_size,) for s in top_k_index: s = s.item() ps = logp[s].item() for prefix, (pb, pnb) in cur_hyps: last = prefix[-1] if len(prefix) > 0 else None if s == 0: # blank n_pb, n_pnb = next_hyps[prefix] n_pb = log_add([n_pb, pb + ps, pnb + ps]) next_hyps[prefix] = (n_pb, n_pnb) elif s == last: # Update *ss -> *s; n_pb, n_pnb = next_hyps[prefix] n_pnb = log_add([n_pnb, pnb + ps]) next_hyps[prefix] = (n_pb, n_pnb) # Update *s-s -> *ss, - is for blank n_prefix = prefix + (s, ) n_pb, n_pnb = next_hyps[n_prefix] n_pnb = log_add([n_pnb, pb + ps]) next_hyps[n_prefix] = (n_pb, n_pnb) else: n_prefix = prefix + (s, ) n_pb, n_pnb = next_hyps[n_prefix] n_pnb = log_add([n_pnb, pb + ps, pnb + ps]) next_hyps[n_prefix] = (n_pb, n_pnb) # 2.2 Second beam prune next_hyps = sorted(next_hyps.items(), key=lambda x: log_add(list(x[1])), reverse=True) cur_hyps = next_hyps[:beam_size] hyps = [(y[0], log_add([y[1][0], y[1][1]])) for y in cur_hyps] return hyps, encoder_out def ctc_prefix_beam_search( self, speech: torch.Tensor, speech_lengths: torch.Tensor, beam_size: int, decoding_chunk_size: int = -1, num_decoding_left_chunks: int = -1, simulate_streaming: bool = False, ) -> List[int]: """ Apply CTC prefix beam search Args: speech (torch.Tensor): (batch, max_len, feat_dim) speech_length (torch.Tensor): (batch, ) beam_size (int): beam size for beam search decoding_chunk_size (int): decoding chunk for dynamic chunk trained model. <0: for decoding, use full chunk. >0: for decoding, use fixed chunk size as set. 0: used for training, it's prohibited here simulate_streaming (bool): whether do encoder forward in a streaming fashion Returns: List[int]: CTC prefix beam search nbest results """ hyps, _ = self._ctc_prefix_beam_search(speech, speech_lengths, beam_size, decoding_chunk_size, num_decoding_left_chunks, simulate_streaming) return hyps[0] def attention_rescoring( self, speech: torch.Tensor, speech_lengths: torch.Tensor, beam_size: int, decoding_chunk_size: int = -1, num_decoding_left_chunks: int = -1, ctc_weight: float = 0.0, simulate_streaming: bool = False, reverse_weight: float = 0.0, ) -> List[int]: """ Apply attention rescoring decoding, CTC prefix beam search is applied first to get nbest, then we resoring the nbest on attention decoder with corresponding encoder out Args: speech (torch.Tensor): (batch, max_len, feat_dim) speech_length (torch.Tensor): (batch, ) beam_size (int): beam size for beam search decoding_chunk_size (int): decoding chunk for dynamic chunk trained model. <0: for decoding, use full chunk. >0: for decoding, use fixed chunk size as set. 0: used for training, it's prohibited here simulate_streaming (bool): whether do encoder forward in a streaming fashion reverse_weight (float): right to left decoder weight ctc_weight (float): ctc score weight Returns: List[int]: Attention rescoring result """ assert speech.shape[0] == speech_lengths.shape[0] assert decoding_chunk_size != 0 if reverse_weight > 0.0: # decoder should be a bitransformer decoder if reverse_weight > 0.0 assert hasattr(self.decoder, 'right_decoder') device = speech.device batch_size = speech.shape[0] # For attention rescoring we only support batch_size=1 assert batch_size == 1 # encoder_out: (1, maxlen, encoder_dim), len(hyps) = beam_size hyps, encoder_out = self._ctc_prefix_beam_search( speech, speech_lengths, beam_size, decoding_chunk_size, num_decoding_left_chunks, simulate_streaming) assert len(hyps) == beam_size hyps_pad = pad_sequence([ torch.tensor(hyp[0], device=device, dtype=torch.long) for hyp in hyps ], True, self.ignore_id) # (beam_size, max_hyps_len) ori_hyps_pad = hyps_pad hyps_lens = torch.tensor([len(hyp[0]) for hyp in hyps], device=device, dtype=torch.long) # (beam_size,) hyps_pad, _ = add_sos_eos(hyps_pad, self.sos, self.eos, self.ignore_id) hyps_lens = hyps_lens + 1 # Add <sos> at begining encoder_out = encoder_out.repeat(beam_size, 1, 1) encoder_mask = torch.ones(beam_size, 1, encoder_out.size(1), dtype=torch.bool, device=device) # used for right to left decoder r_hyps_pad = reverse_pad_list(ori_hyps_pad, hyps_lens, self.ignore_id) r_hyps_pad, _ = add_sos_eos(r_hyps_pad, self.sos, self.eos, self.ignore_id) decoder_out, r_decoder_out, _ = self.decoder( encoder_out, encoder_mask, hyps_pad, hyps_lens, r_hyps_pad, reverse_weight) # (beam_size, max_hyps_len, vocab_size) decoder_out = torch.nn.functional.log_softmax(decoder_out, dim=-1) decoder_out = decoder_out.cpu().numpy() # r_decoder_out will be 0.0, if reverse_weight is 0.0 or decoder is a # conventional transformer decoder. r_decoder_out = torch.nn.functional.log_softmax(r_decoder_out, dim=-1) r_decoder_out = r_decoder_out.cpu().numpy() # Only use decoder score for rescoring best_score = -float('inf') best_index = 0 for i, hyp in enumerate(hyps): score = 0.0 for j, w in enumerate(hyp[0]): score += decoder_out[i][j][w] score += decoder_out[i][len(hyp[0])][self.eos] # add right to left decoder score if reverse_weight > 0: r_score = 0.0 for j, w in enumerate(hyp[0]): r_score += r_decoder_out[i][len(hyp[0]) - j - 1][w] r_score += r_decoder_out[i][len(hyp[0])][self.eos] score = score * (1 - reverse_weight) + r_score * reverse_weight # add ctc score score += hyp[1] * ctc_weight if score > best_score: best_score = score best_index = i return hyps[best_index][0], best_score
原文地址：http://www.cnblogs.com/Uriel-w/p/16923736.html
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