msb-public
/
PaddleSpeech
mirror of https://github.com/PaddlePaddle/PaddleSpeech
1
0
Code Issues Projects Releases Wiki Activity

update the time stamp type, test=doc

Browse Source
pull/1850/head
xiongxinlei 2 years ago
parent cbd8383d0e
commit f082fcbbec
2 changed files with 26 additions and 17 deletions
Show Stats Download Patch File Download Diff File

18
paddlespeech/server/engine/asr/online/asr_engine.py
Unescape View File

@ -296,6 +296,8 @@ class PaddleASRConnectionHanddler:
self.chunk_num = 0 self.chunk_num = 0
self.global_frame_offset = 0 self.global_frame_offset = 0
self.result_transcripts = [''] self.result_transcripts = ['']
self.word_time_stamp = []
self.time_stamp = []
self.first_char_occur_elapsed = None self.first_char_occur_elapsed = None
self.word_time_stamp = None self.word_time_stamp = None
@ -514,10 +516,7 @@ class PaddleASRConnectionHanddler:
return '' return ''
def get_word_time_stamp(self): def get_word_time_stamp(self):
if self.word_time_stamp is None: return self.word_time_stamp
return []
else:
return self.word_time_stamp
@paddle.no_grad() @paddle.no_grad()
def rescoring(self): def rescoring(self):
@ -581,7 +580,18 @@ class PaddleASRConnectionHanddler:
best_index = i best_index = i
# update the one best result # update the one best result
# hyps stored the beam results and each fields is:
logger.info(f"best index: {best_index}") logger.info(f"best index: {best_index}")
# logger.info(f'best result: {hyps[best_index]}')
# the field of the hyps is:
# hyps[0][0]: the sentence word-id in the vocab with a tuple
# hyps[0][1]: the sentence decoding probability with all paths
# hyps[0][2]: viterbi_blank ending probability
# hyps[0][3]: viterbi_non_blank probability
# hyps[0][4]: current_token_prob,
# hyps[0][5]: times_viterbi_blank,
# hyps[0][6]: times_titerbi_non_blank
self.hyps = [hyps[best_index][0]] self.hyps = [hyps[best_index][0]]
# update the hyps time stamp # update the hyps time stamp

25
paddlespeech/server/engine/asr/online/ctc_search.py
Unescape View File

@ -27,7 +27,7 @@ class CTCPrefixBeamSearch:
"""Implement the ctc prefix beam search """Implement the ctc prefix beam search
Args: Args:
config (yacs.config.CfgNode): _description_ config (yacs.config.CfgNode): the ctc prefix beam search configuration
""" """
self.config = config self.config = config
self.reset() self.reset()
@ -69,7 +69,6 @@ class CTCPrefixBeamSearch:
# 2. CTC beam search step by step # 2. CTC beam search step by step
for t in range(0, maxlen): for t in range(0, maxlen):
logp = ctc_probs[t] # (vocab_size,) logp = ctc_probs[t] # (vocab_size,)
# key: prefix, value (pb, pnb), default value(-inf, -inf)
# next_hyps = defaultdict(lambda: (-float('inf'), -float('inf'))) # next_hyps = defaultdict(lambda: (-float('inf'), -float('inf')))
next_hyps = defaultdict( next_hyps = defaultdict(
lambda: (-float('inf'), -float('inf'), -float('inf'), -float('inf'), -float('inf'), [], [])) lambda: (-float('inf'), -float('inf'), -float('inf'), -float('inf'), -float('inf'), [], []))
@ -80,7 +79,7 @@ class CTCPrefixBeamSearch:
for s in top_k_index: for s in top_k_index:
s = s.item() s = s.item()
ps = logp[s].item() ps = logp[s].item()
for prefix, (pb, pnb, v_s, v_ns, cur_token_prob, times_s, for prefix, (pb, pnb, v_b_s, v_nb_s, cur_token_prob, times_s,
times_ns) in self.cur_hyps: times_ns) in self.cur_hyps:
last = prefix[-1] if len(prefix) > 0 else None last = prefix[-1] if len(prefix) > 0 else None
if s == blank_id: # blank if s == blank_id: # blank
@ -88,9 +87,9 @@ class CTCPrefixBeamSearch:
prefix] prefix]
n_pb = log_add([n_pb, pb + ps, pnb + ps]) n_pb = log_add([n_pb, pb + ps, pnb + ps])
pre_times = times_s if v_s > v_ns else times_ns pre_times = times_s if v_b_s > v_nb_s else times_ns
n_times_s = copy.deepcopy(pre_times) n_times_s = copy.deepcopy(pre_times)
viterbi_score = v_s if v_s > v_ns else v_ns viterbi_score = v_b_s if v_b_s > v_nb_s else v_nb_s
n_v_s = viterbi_score + ps n_v_s = viterbi_score + ps
next_hyps[prefix] = (n_pb, n_pnb, n_v_s, n_v_ns, next_hyps[prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
n_cur_token_prob, n_times_s, n_cur_token_prob, n_times_s,
@ -101,8 +100,8 @@ class CTCPrefixBeamSearch:
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_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_ns = next_hyps[
prefix] prefix]
n_pnb = log_add([n_pnb, pnb + ps]) n_pnb = log_add([n_pnb, pnb + ps])
if n_v_ns < v_ns + ps: if n_v_ns < v_nb_s + ps:
n_v_ns = v_ns + ps n_v_ns = v_nb_s + ps
if n_cur_token_prob < ps: if n_cur_token_prob < ps:
n_cur_token_prob = ps n_cur_token_prob = ps
n_times_ns = copy.deepcopy(times_ns) n_times_ns = copy.deepcopy(times_ns)
@ -117,8 +116,8 @@ class CTCPrefixBeamSearch:
n_prefix = prefix + (s, ) 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_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_ns = next_hyps[
n_prefix] n_prefix]
if n_v_ns < v_s + ps: if n_v_ns < v_b_s + ps:
n_v_ns = v_s + ps n_v_ns = v_b_s + ps
n_cur_token_prob = ps n_cur_token_prob = ps
n_times_ns = copy.deepcopy(times_s) n_times_ns = copy.deepcopy(times_s)
n_times_ns.append(self.abs_time_step) n_times_ns.append(self.abs_time_step)
@ -129,10 +128,10 @@ class CTCPrefixBeamSearch:
else: else:
# Case 3: *a + b => *ab, *aε + b => *ab # Case 3: *a + b => *ab, *aε + b => *ab
n_prefix = prefix + (s, ) n_prefix = prefix + (s, )
n_pb, n_pnb, n_v_s, n_v_ns, n_cur_token_prob, n_times_s, n_times_n = next_hyps[ 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_prefix]
viterbi_score = v_s if v_s > v_ns else v_ns viterbi_score = v_b_s if v_b_s > v_nb_s else v_nb_s
pre_times = times_s if v_s > v_ns else times_ns pre_times = times_s if v_b_s > v_nb_s else times_ns
if n_v_ns < viterbi_score + ps: if n_v_ns < viterbi_score + ps:
n_v_ns = viterbi_score + ps n_v_ns = viterbi_score + ps
n_cur_token_prob = ps n_cur_token_prob = ps
@ -153,7 +152,7 @@ class CTCPrefixBeamSearch:
# 2.3 update the absolute time step # 2.3 update the absolute time step
self.abs_time_step += 1 self.abs_time_step += 1
# self.hyps = [(y[0], log_add([y[1][0], y[1][1]])) for y in self.cur_hyps]
self.hyps = [(y[0], log_add([y[1][0], y[1][1]]), y[1][2], y[1][3], self.hyps = [(y[0], log_add([y[1][0], y[1][1]]), y[1][2], y[1][3],
y[1][4], y[1][5], y[1][6]) for y in self.cur_hyps] y[1][4], y[1][5], y[1][6]) for y in self.cur_hyps]

Write Preview
Loading…
Cancel
Save