speech_recognition/FunASR
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remove useless code

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This commit is contained in:
lzr265946 2023-02-16 15:22:14 +08:00
parent 8a391c58c1
commit 546262a0c6
3 changed files with 4 additions and 91 deletions
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1
funasr/bin/asr_inference_paraformer_vad.py
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@ -38,7 +38,6 @@ from funasr.utils.types import str_or_none
from funasr.utils import asr_utils, wav_utils, postprocess_utils
from funasr.models.frontend.wav_frontend import WavFrontend
from funasr.tasks.vad import VADTask
from funasr.utils.timestamp_tools import time_stamp_lfr6
from funasr.bin.punctuation_infer import Text2Punc
from funasr.bin.asr_inference_paraformer_vad_punc import Speech2Text
from funasr.bin.asr_inference_paraformer_vad_punc import Speech2VadSegment

12
funasr/bin/asr_inference_paraformer_vad_punc.py
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@ -39,7 +39,7 @@ from funasr.utils.types import str_or_none
from funasr.utils import asr_utils, wav_utils, postprocess_utils
from funasr.models.frontend.wav_frontend import WavFrontend
from funasr.tasks.vad import VADTask
from funasr.utils.timestamp_tools import time_stamp_lfr6, time_stamp_lfr6_pl
from funasr.utils.timestamp_tools import time_stamp_lfr6_pl
from funasr.bin.punctuation_infer import Text2Punc
from funasr.models.e2e_asr_paraformer import BiCifParaformer
@ -282,12 +282,8 @@ class Speech2Text:
else:
text = None
if isinstance(self.asr_model, BiCifParaformer):
timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time)
results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor))
else:
time_stamp = time_stamp_lfr6(alphas[i:i + 1, ], enc_len[i:i + 1, ], copy.copy(token), begin_time, end_time)
results.append((text, token, token_int, time_stamp, enc_len_batch_total, lfr_factor))
timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time)
results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor))
# assert check_return_type(results)
return results
@ -617,7 +613,7 @@ def inference_modelscope(
result = result_segments[0]
text, token, token_int = result[0], result[1], result[2]
time_stamp = None if len(result) < 4 else result[3]
if use_timestamp and time_stamp is not None:
postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
else:

82
funasr/utils/timestamp_tools.py
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@ -4,88 +4,6 @@ import logging
import numpy as np
from typing import Any, List, Tuple, Union
def cut_interval(alphas: torch.Tensor, start: int, end: int, tail: bool):
if not tail:
if end == start + 1:
cut = (end + start) / 2.0
else:
alpha = alphas[start+1: end].tolist()
reverse_steps = 1
for reverse_alpha in alpha[::-1]:
if reverse_alpha > 0.35:
reverse_steps += 1
else:
break
cut = end - reverse_steps
else:
if end != len(alphas) - 1:
cut = end + 1
else:
cut = start + 1
return float(cut)
def time_stamp_lfr6(alphas: torch.Tensor, speech_lengths: torch.Tensor, raw_text: List[str], begin: int = 0, end: int = None):
time_stamp_list = []
alphas = alphas[0]
text = copy.deepcopy(raw_text)
if end is None:
time = speech_lengths * 60 / 1000
sacle_rate = (time / speech_lengths[0]).tolist()
else:
time = (end - begin) / 1000
sacle_rate = (time / speech_lengths[0]).tolist()
predictor = (alphas > 0.5).int()
fire_places = torch.nonzero(predictor == 1).squeeze(1).tolist()
cuts = []
npeak = int(predictor.sum())
nchar = len(raw_text)
if npeak - 1 == nchar:
fire_places = torch.where((alphas > 0.5) == 1)[0].tolist()
for i in range(len(fire_places)):
if fire_places[i] < len(alphas) - 1:
if 0.05 < alphas[fire_places[i]+1] < 0.5:
fire_places[i] += 1
elif npeak < nchar:
lost_num = nchar - npeak
lost_fire = speech_lengths[0].tolist() - fire_places[-1]
interval_distance = lost_fire // (lost_num + 1)
for i in range(1, lost_num + 1):
fire_places.append(fire_places[-1] + interval_distance)
elif npeak - 1 > nchar:
redundance_num = npeak - 1 - nchar
for i in range(redundance_num):
fire_places.pop()
cuts.append(0)
start_sil = True
if start_sil:
text.insert(0, '<sil>')
for i in range(len(fire_places)-1):
cuts.append(cut_interval(alphas, fire_places[i], fire_places[i+1], tail=(i==len(fire_places)-2)))
for i in range(2, len(fire_places)-2):
if fire_places[i-2] == fire_places[i-1] - 1 and fire_places[i-1] != fire_places[i] - 1:
cuts[i-1] += 1
if cuts[-1] != len(alphas) - 1:
text.append('<sil>')
cuts.append(speech_lengths[0].tolist())
cuts.insert(-1, (cuts[-1] + cuts[-2]) * 0.5)
sec_fire_places = np.array(cuts) * sacle_rate
for i in range(1, len(sec_fire_places) - 1):
start, end = sec_fire_places[i], sec_fire_places[i+1]
if i == len(sec_fire_places) - 2:
end = time
time_stamp_list.append([int(round(start, 2) * 1000) + begin, int(round(end, 2) * 1000) + begin])
text = text[1:]
if npeak - 1 == nchar or npeak > nchar:
return time_stamp_list[:-1]
else:
return time_stamp_list
def time_stamp_lfr6_pl(us_alphas, us_cif_peak, char_list, begin_time=0.0, end_time=None):
START_END_THRESHOLD = 5
TIME_RATE = 10.0 * 6 / 1000 / 3 # 3 times upsampled