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Merge pull request #1838 from Honei/asr_time

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[asr][server]streaming asr server add time stamp
pull/1853/head
Hui Zhang 2 years ago committed by GitHub
parent 1c729dd029 b7a77eebca
commit a58831ddf7
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 120 additions and 18 deletions
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44
paddlespeech/server/engine/asr/online/asr_engine.py
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@ -13,7 +13,6 @@
# limitations under the License.
import copy
import os
import time
from typing import Optional
import numpy as np
@ -297,6 +296,8 @@ class PaddleASRConnectionHanddler:
self.chunk_num = 0
self.global_frame_offset = 0
self.result_transcripts = ['']
self.word_time_stamp = []
self.time_stamp = []
self.first_char_occur_elapsed = None
def decode(self, is_finished=False):
@ -513,6 +514,9 @@ class PaddleASRConnectionHanddler:
else:
return ''
def get_word_time_stamp(self):
return self.word_time_stamp
@paddle.no_grad()
def rescoring(self):
if "deepspeech2online" in self.model_type or "deepspeech2offline" in self.model_type:
@ -575,10 +579,48 @@ class PaddleASRConnectionHanddler:
best_index = i
# 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 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]]
# update the hyps time stamp
self.time_stamp = hyps[best_index][5] if hyps[best_index][2] > hyps[
best_index][3] else hyps[best_index][6]
logger.info(f"time stamp: {self.time_stamp}")
self.update_result()
# update each word start and end time stamp
frame_shift_in_ms = self.model.encoder.embed.subsampling_rate * self.n_shift / self.sample_rate
logger.info(f"frame shift ms: {frame_shift_in_ms}")
word_time_stamp = []
for idx, _ in enumerate(self.time_stamp):
start = (self.time_stamp[idx - 1] + self.time_stamp[idx]
) / 2.0 if idx > 0 else 0
start = start * frame_shift_in_ms
end = (self.time_stamp[idx] + self.time_stamp[idx + 1]
) / 2.0 if idx < len(self.time_stamp) - 1 else self.offset
end = end * frame_shift_in_ms
word_time_stamp.append({
"w": self.result_transcripts[0][idx],
"bg": start,
"ed": end
})
# logger.info(f"{self.result_transcripts[0][idx]}, start: {start}, end: {end}")
self.word_time_stamp = word_time_stamp
logger.info(f"word time stamp: {self.word_time_stamp}")
class ASRServerExecutor(ASRExecutor):
def __init__(self):

90
paddlespeech/server/engine/asr/online/ctc_search.py
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@ -11,6 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
from collections import defaultdict
import paddle
@ -26,7 +27,7 @@ class CTCPrefixBeamSearch:
"""Implement the ctc prefix beam search
Args:
config (yacs.config.CfgNode): _description_
config (yacs.config.CfgNode): the ctc prefix beam search configuration
"""
self.config = config
self.reset()
@ -54,14 +55,23 @@ class CTCPrefixBeamSearch:
assert len(ctc_probs.shape) == 2
# cur_hyps: (prefix, (blank_ending_score, none_blank_ending_score))
# blank_ending_score and none_blank_ending_score in ln domain
# 0. blank_ending_score,
# 1. none_blank_ending_score,
# 2. viterbi_blank ending,
# 3. viterbi_non_blank,
# 4. current_token_prob,
# 5. times_viterbi_blank,
# 6. times_titerbi_non_blank
if self.cur_hyps is None:
self.cur_hyps = [(tuple(), (0.0, -float('inf')))]
self.cur_hyps = [(tuple(), (0.0, -float('inf'), 0.0, 0.0,
-float('inf'), [], []))]
# self.cur_hyps = [(tuple(), (0.0, -float('inf')))]
# 2. CTC beam search step by step
for t in range(0, maxlen):
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(
lambda: (-float('inf'), -float('inf'), -float('inf'), -float('inf'), -float('inf'), [], []))
# 2.1 First beam prune: select topk best
# do token passing process
@ -69,36 +79,83 @@ class CTCPrefixBeamSearch:
for s in top_k_index:
s = s.item()
ps = logp[s].item()
for prefix, (pb, pnb) in self.cur_hyps:
for prefix, (pb, pnb, v_b_s, v_nb_s, cur_token_prob, times_s,
times_ns) in self.cur_hyps:
last = prefix[-1] if len(prefix) > 0 else None
if s == blank_id: # blank
n_pb, n_pnb = 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 = log_add([n_pb, pb + ps, pnb + ps])
next_hyps[prefix] = (n_pb, n_pnb)
pre_times = times_s if v_b_s > v_nb_s else times_ns
n_times_s = 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)
elif s == last:
# Update *ss -> *s;
n_pb, n_pnb = next_hyps[prefix]
# 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[
prefix]
n_pnb = log_add([n_pnb, pnb + ps])
next_hyps[prefix] = (n_pb, n_pnb)
if n_v_ns < v_nb_s + ps:
n_v_ns = 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[
-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)
# Update *s-s -> *ss, - is for blank
# Case 2: *aε + a => *aa
n_prefix = prefix + (s, )
n_pb, n_pnb = 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]
if n_v_ns < v_b_s + ps:
n_v_ns = 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_pnb = log_add([n_pnb, pb + ps])
next_hyps[n_prefix] = (n_pb, n_pnb)
next_hyps[n_prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
n_cur_token_prob, n_times_s,
n_times_ns)
else:
# Case 3: *a + b => *ab, *aε + b => *ab
n_prefix = prefix + (s, )
n_pb, n_pnb = 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]
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
n_cur_token_prob = ps
n_times_ns = copy.deepcopy(pre_times)
n_times_ns.append(self.abs_time_step)
n_pnb = log_add([n_pnb, pb + ps, pnb + ps])
next_hyps[n_prefix] = (n_pb, n_pnb)
next_hyps[n_prefix] = (n_pb, n_pnb, n_v_s, n_v_ns,
n_cur_token_prob, n_times_s,
n_times_ns)
# 2.2 Second beam prune
next_hyps = sorted(
next_hyps.items(),
key=lambda x: log_add(list(x[1])),
key=lambda x: log_add([x[1][0], x[1][1]]),
reverse=True)
self.cur_hyps = next_hyps[:beam_size]
self.hyps = [(y[0], log_add([y[1][0], y[1][1]])) for y in self.cur_hyps]
# 2.3 update the absolute time step
self.abs_time_step += 1
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]
logger.info("ctc prefix search success")
return self.hyps
@ -123,6 +180,7 @@ class CTCPrefixBeamSearch:
"""
self.cur_hyps = None
self.hyps = None
self.abs_time_step = 0
def finalize_search(self):
"""do nothing in ctc_prefix_beam_search

4
paddlespeech/server/ws/asr_socket.py
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@ -78,12 +78,14 @@ async def websocket_endpoint(websocket: WebSocket):
connection_handler.decode(is_finished=True)
connection_handler.rescoring()
asr_results = connection_handler.get_result()
word_time_stamp = connection_handler.get_word_time_stamp()
connection_handler.reset()
resp = {
"status": "ok",
"signal": "finished",
'result': asr_results
'result': asr_results,
'times': word_time_stamp
}
await websocket.send_json(resp)
break

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