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209 lines
8.4 KiB
209 lines
8.4 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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import copy
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from collections import defaultdict
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import paddle
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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 (yacs.config.CfgNode): the ctc prefix beam search configuration
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"""
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self.config = config
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# beam size
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self.first_beam_size = self.config.beam_size
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# TODO(support second beam size)
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self.second_beam_size = int(self.first_beam_size * 1.0)
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logger.info(
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f"first and second beam size: {self.first_beam_size}, {self.second_beam_size}"
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)
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# state
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self.cur_hyps = None
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self.hyps = None
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self.abs_time_step = 0
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self.reset()
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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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self.abs_time_step = 0
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@paddle.no_grad()
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def search(self, 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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device (paddle.fluid.core_avx.Place): the feature host device, such as CUDAPlace(0).
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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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assert len(ctc_probs.shape) == 2
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batch_size = 1
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vocab_size = ctc_probs.shape[1]
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first_beam_size = min(self.first_beam_size, vocab_size)
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second_beam_size = min(self.second_beam_size, vocab_size)
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logger.info(
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f"effect first and second beam size: {self.first_beam_size}, {self.second_beam_size}"
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)
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maxlen = ctc_probs.shape[0]
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# cur_hyps: (prefix, (blank_ending_score, none_blank_ending_score))
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# 0. blank_ending_score,
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# 1. none_blank_ending_score,
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# 2. viterbi_blank ending,
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# 3. viterbi_non_blank,
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# 4. current_token_prob,
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# 5. times_viterbi_blank,
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# 6. times_titerbi_non_blank
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if self.cur_hyps is None:
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self.cur_hyps = [(tuple(), (0.0, -float('inf'), 0.0, 0.0,
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-float('inf'), [], []))]
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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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# next_hyps = defaultdict(lambda: (-float('inf'), -float('inf')))
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next_hyps = defaultdict(
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lambda: (-float('inf'), -float('inf'), -float('inf'), -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(
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first_beam_size) # (first_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, v_b_s, v_nb_s, cur_token_prob, times_s,
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times_ns) 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, n_v_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_ns = next_hyps[
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prefix]
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n_pb = log_add([n_pb, pb + ps, pnb + ps])
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pre_times = times_s if v_b_s > v_nb_s else times_ns
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n_times_s = copy.deepcopy(pre_times)
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viterbi_score = v_b_s if v_b_s > v_nb_s else v_nb_s
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n_v_s = viterbi_score + ps
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next_hyps[prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
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n_cur_token_prob, n_times_s,
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n_times_ns)
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elif s == last:
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# Update *ss -> *s;
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# case1: *a + a => *a
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n_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_ns = next_hyps[
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prefix]
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n_pnb = log_add([n_pnb, pnb + ps])
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if n_v_ns < v_nb_s + ps:
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n_v_ns = v_nb_s + ps
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if n_cur_token_prob < ps:
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n_cur_token_prob = ps
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n_times_ns = copy.deepcopy(times_ns)
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n_times_ns[
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-1] = self.abs_time_step # 注意，这里要重新使用绝对时间
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next_hyps[prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
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n_cur_token_prob, n_times_s,
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n_times_ns)
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# Update *s-s -> *ss, - is for blank
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# Case 2: *aε + a => *aa
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n_prefix = prefix + (s, )
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n_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_ns = next_hyps[
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n_prefix]
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if n_v_ns < v_b_s + ps:
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n_v_ns = v_b_s + ps
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n_cur_token_prob = ps
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n_times_ns = copy.deepcopy(times_s)
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n_times_ns.append(self.abs_time_step)
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n_pnb = log_add([n_pnb, pb + ps])
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next_hyps[n_prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
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n_cur_token_prob, n_times_s,
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n_times_ns)
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else:
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# Case 3: *a + b => *ab, *aε + b => *ab
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n_prefix = prefix + (s, )
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n_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_ns = next_hyps[
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n_prefix]
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viterbi_score = v_b_s if v_b_s > v_nb_s else v_nb_s
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pre_times = times_s if v_b_s > v_nb_s else times_ns
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if n_v_ns < viterbi_score + ps:
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n_v_ns = viterbi_score + ps
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n_cur_token_prob = ps
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n_times_ns = copy.deepcopy(pre_times)
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n_times_ns.append(self.abs_time_step)
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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, n_v_s, n_v_ns,
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n_cur_token_prob, n_times_s,
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n_times_ns)
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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([x[1][0], x[1][1]]),
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reverse=True)
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self.cur_hyps = next_hyps[:second_beam_size]
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# 2.3 update the absolute time step
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self.abs_time_step += 1
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self.hyps = [(y[0], log_add([y[1][0], y[1][1]]), y[1][2], y[1][3],
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y[1][4], y[1][5], y[1][6]) 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, List[str]
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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 the search hyps
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Returns:
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list: return the search hyps, List[Tuple[str, float, ...]]
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"""
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return self.hyps
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def finalize_search(self):
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"""do nothing in ctc_prefix_beam_search
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"""
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pass
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