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@ -86,8 +86,8 @@ class CTCPrefixBeamSearch:
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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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# 5. times_viterbi_blank, times_b
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# 6. times_titerbi_non_blank, times_nb
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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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@ -106,69 +106,69 @@ class CTCPrefixBeamSearch:
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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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for prefix, (pb, pnb, v_b_s, v_nb_s, cur_token_prob, times_b,
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times_nb) 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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n_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_b, n_times_nb = 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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pre_times = times_b if v_b_s > v_nb_s else times_nb
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n_times_b = 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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n_cur_token_prob, n_times_b,
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n_times_nb)
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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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n_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_b, n_times_nb = 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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n_times_nb = copy.deepcopy(times_nb)
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n_times_nb[
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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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n_cur_token_prob, n_times_b,
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n_times_nb)
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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_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_b, n_times_nb = 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_times_nb = copy.deepcopy(times_b)
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n_times_nb.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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n_cur_token_prob, n_times_b,
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n_times_nb)
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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_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_b, n_times_nb = 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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pre_times = times_b if v_b_s > v_nb_s else times_nb
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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_times_nb = copy.deepcopy(pre_times)
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n_times_nb.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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n_cur_token_prob, n_times_b,
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n_times_nb)
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# 2.2 Second beam prune
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next_hyps = sorted(
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