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120 lines
4.3 KiB
120 lines
4.3 KiB
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections import defaultdict
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from paddlespeech.cli.log import logger
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from paddlespeech.s2t.utils.utility import log_add
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__all__ = ['CTCPrefixBeamSearch']
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class CTCPrefixBeamSearch:
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def __init__(self, config):
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"""Implement the ctc prefix beam search
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Args:
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config (_type_): _description_
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"""
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self.config = config
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self.reset()
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def search(self, xs, ctc_probs, device, blank_id=0):
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"""ctc prefix beam search method decode a chunk feature
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Args:
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xs (paddle.Tensor): feature data
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ctc_probs (paddle.Tensor): the ctc probability of all the tokens
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encoder_out (paddle.Tensor): _description_
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encoder_mask (_type_): _description_
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blank_id (int, optional): the blank id in the vocab. Defaults to 0.
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Returns:
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list: the search result
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"""
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# decode
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logger.info("start to ctc prefix search")
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# device = xs.place
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batch_size = xs.shape[0]
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beam_size = self.config.beam_size
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maxlen = ctc_probs.shape[0]
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assert len(ctc_probs.shape) == 2
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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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if self.cur_hyps is None:
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self.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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# do token passing process
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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 self.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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self.cur_hyps = next_hyps[:beam_size]
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self.hyps = [(y[0], log_add([y[1][0], y[1][1]])) for y in self.cur_hyps]
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logger.info("ctc prefix search success")
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return self.hyps
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def get_one_best_hyps(self):
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"""Return the one best result
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Returns:
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list: the one best result
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"""
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return [self.hyps[0][0]]
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def get_hyps(self):
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return self.hyps
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def reset(self):
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"""Rest the search cache value
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"""
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self.cur_hyps = None
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self.hyps = None
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