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[server][asr]rename time_s and time_ns to time_b and time_nb #2133

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Jul 11, 2022
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[server][asr]rename time_s and time_ns to time_b and time_nb #2133

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f4b11b1
rename time_s and time_ns to time_b and time_nb
zh794390558 Jul 11, 2022
f8450c3
rename n_v_s to n_v_b, n_v_ns to n_v_nb
zh794390558 Jul 11, 2022
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5 changes: 4 additions & 1 deletion paddlespeech/server/engine/acs/python/acs_engine.py
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Original file line number Diff line number Diff line change
Expand Up @@ -192,12 +192,15 @@ def get_macthed_word(self, msg):

# search for each word in self.word_list
offset = self.config.offset
# last time in time_stamp
max_ed = time_stamp[-1]['ed']
for w in self.word_list:
# search the w in asr_result and the index in asr_result
# https://docs.python.org/3/library/re.html#re.finditer
for m in re.finditer(w, asr_result):
# match start and end char index in timestamp
# https://docs.python.org/3/library/re.html#re.Match.start
start = max(time_stamp[m.start(0)]['bg'] - offset, 0)

end = min(time_stamp[m.end(0) - 1]['ed'] + offset, max_ed)
logger.debug(f'start: {start}, end: {end}')
acs_result.append({'w': w, 'bg': start, 'ed': end})
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76 changes: 38 additions & 38 deletions paddlespeech/server/engine/asr/online/ctc_search.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,11 +83,11 @@ def search(self, ctc_probs, device, blank_id=0):
# cur_hyps: (prefix, (blank_ending_score, none_blank_ending_score))
# 0. blank_ending_score,
# 1. none_blank_ending_score,
# 2. viterbi_blank ending,
# 3. viterbi_non_blank,
# 2. viterbi_blank ending score,
# 3. viterbi_non_blank score,
# 4. current_token_prob,
# 5. times_viterbi_blank,
# 6. times_titerbi_non_blank
# 5. times_viterbi_blank, times_b
# 6. times_titerbi_non_blank, times_nb
if self.cur_hyps is None:
self.cur_hyps = [(tuple(), (0.0, -float('inf'), 0.0, 0.0,
-float('inf'), [], []))]
Expand All @@ -106,69 +106,69 @@ def search(self, ctc_probs, device, blank_id=0):
for s in top_k_index:
s = s.item()
ps = logp[s].item()
for prefix, (pb, pnb, v_b_s, v_nb_s, cur_token_prob, times_s,
times_ns) in self.cur_hyps:
for prefix, (pb, pnb, v_b_s, v_nb_s, cur_token_prob, times_b,
times_nb) in self.cur_hyps:
last = prefix[-1] if len(prefix) > 0 else None
if s == blank_id: # blank
n_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_ns = next_hyps[
n_pb, n_pnb, n_v_b, n_v_nb, n_cur_token_prob, n_times_b, n_times_nb = next_hyps[
prefix]
n_pb = log_add([n_pb, pb + ps, pnb + ps])

pre_times = times_s if v_b_s > v_nb_s else times_ns
n_times_s = copy.deepcopy(pre_times)
pre_times = times_b if v_b_s > v_nb_s else times_nb
n_times_b = copy.deepcopy(pre_times)
viterbi_score = v_b_s if v_b_s > v_nb_s else v_nb_s
n_v_s = viterbi_score + ps
next_hyps[prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
n_cur_token_prob, n_times_s,
n_times_ns)
n_v_b = viterbi_score + ps
next_hyps[prefix] = (n_pb, n_pnb, n_v_b, n_v_nb,
n_cur_token_prob, n_times_b,
n_times_nb)
elif s == last:
# Update *ss -> *s;
# case1: *a + a => *a
n_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_ns = next_hyps[
n_pb, n_pnb, n_v_b, n_v_nb, n_cur_token_prob, n_times_b, n_times_nb = next_hyps[
prefix]
n_pnb = log_add([n_pnb, pnb + ps])
if n_v_ns < v_nb_s + ps:
n_v_ns = v_nb_s + ps
if n_v_nb < v_nb_s + ps:
n_v_nb = v_nb_s + ps
if n_cur_token_prob < ps:
n_cur_token_prob = ps
n_times_ns = copy.deepcopy(times_ns)
n_times_ns[
n_times_nb = copy.deepcopy(times_nb)
n_times_nb[
-1] = self.abs_time_step # 注意，这里要重新使用绝对时间
next_hyps[prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
n_cur_token_prob, n_times_s,
n_times_ns)
next_hyps[prefix] = (n_pb, n_pnb, n_v_b, n_v_nb,
n_cur_token_prob, n_times_b,
n_times_nb)

# Update *s-s -> *ss, - is for blank
# Case 2: *aε + a => *aa
n_prefix = prefix + (s, )
n_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_ns = next_hyps[
n_pb, n_pnb, n_v_b, n_v_nb, n_cur_token_prob, n_times_b, n_times_nb = next_hyps[
n_prefix]
if n_v_ns < v_b_s + ps:
n_v_ns = v_b_s + ps
if n_v_nb < v_b_s + ps:
n_v_nb = v_b_s + ps
n_cur_token_prob = ps
n_times_ns = copy.deepcopy(times_s)
n_times_ns.append(self.abs_time_step)
n_times_nb = copy.deepcopy(times_b)
n_times_nb.append(self.abs_time_step)
n_pnb = log_add([n_pnb, pb + ps])
next_hyps[n_prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
n_cur_token_prob, n_times_s,
n_times_ns)
next_hyps[n_prefix] = (n_pb, n_pnb, n_v_b, n_v_nb,
n_cur_token_prob, n_times_b,
n_times_nb)
else:
# Case 3: *a + b => *ab, *aε + b => *ab
n_prefix = prefix + (s, )
n_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_ns = next_hyps[
n_pb, n_pnb, n_v_b, n_v_nb, n_cur_token_prob, n_times_b, n_times_nb = next_hyps[
n_prefix]
viterbi_score = v_b_s if v_b_s > v_nb_s else v_nb_s
pre_times = times_s if v_b_s > v_nb_s else times_ns
if n_v_ns < viterbi_score + ps:
n_v_ns = viterbi_score + ps
pre_times = times_b if v_b_s > v_nb_s else times_nb
if n_v_nb < viterbi_score + ps:
n_v_nb = viterbi_score + ps
n_cur_token_prob = ps
n_times_ns = copy.deepcopy(pre_times)
n_times_ns.append(self.abs_time_step)
n_times_nb = copy.deepcopy(pre_times)
n_times_nb.append(self.abs_time_step)

n_pnb = log_add([n_pnb, pb + ps, pnb + ps])
next_hyps[n_prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
n_cur_token_prob, n_times_s,
n_times_ns)
next_hyps[n_prefix] = (n_pb, n_pnb, n_v_b, n_v_nb,
n_cur_token_prob, n_times_b,
n_times_nb)

# 2.2 Second beam prune
next_hyps = sorted(
Expand Down