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modelscope paraformer large long input

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游雁 2023-01-17 17:34:38 +08:00
parent 5014a39078
commit 5ddad6db68
1 changed files with 37 additions and 18 deletions
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55
funasr/bin/asr_inference_paraformer_vad_punc.py
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@ -666,9 +666,10 @@ def inference_modelscope(
vad_infer_config: Optional[str] = None,
vad_model_file: Optional[str] = None,
vad_cmvn_file: Optional[str] = None,
time_stamp_writer: bool = False,
time_stamp_writer: bool = True,
punc_infer_config: Optional[str] = None,
punc_model_file: Optional[str] = None,
outputs_dict: Optional[bool] = True,
**kwargs,
):
assert check_argument_types()
@ -725,6 +726,11 @@ def inference_modelscope(
speech2text = Speech2Text(**speech2text_kwargs)
text2punc = Text2Punc(punc_infer_config, punc_model_file, device=device, dtype=dtype)
if output_dir is not None:
writer = DatadirWriter(output_dir)
ibest_writer = writer[f"1best_recog"]
ibest_writer["token_list"][""] = " ".join(speech2text.asr_train_args.token_list)
def _forward(data_path_and_name_and_type,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
@ -756,6 +762,9 @@ def inference_modelscope(
output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
if output_path is not None:
writer = DatadirWriter(output_path)
ibest_writer = writer[f"1best_recog"]
# ibest_writer["punc_dict"][""] = " ".join(punc_infer_config.punc_list)
# ibest_writer["token_list"][""] = " ".join(asr_train_config.token_list)
else:
writer = None
@ -805,11 +814,10 @@ def inference_modelscope(
# Create a directory: outdir/{n}best_recog
if writer is not None:
ibest_writer = writer[f"1best_recog"]
# Write the result to each file
ibest_writer["token"][key] = " ".join(token)
# ibest_writer["token_int"][key] = " ".join(map(str, token_int))
ibest_writer["token_int"][key] = " ".join(map(str, token_int))
ibest_writer["vad"][key] = "{}".format(vadsegments)
if text is not None:
postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
@ -826,17 +834,22 @@ def inference_modelscope(
word_lists = None
text_postprocessed_punc_time_stamp = None
punc_id_list = None
item = {'key': key, 'value': text_postprocessed_punc_time_stamp, 'text': text_postprocessed,
'time_stamp': time_stamp_postprocessed, 'punc': punc_id_list}
'time_stamp': time_stamp_postprocessed, 'punc': punc_id_list, 'token': token}
if outputs_dict:
item = {'text_punc': text_postprocessed_punc, 'text': text_postprocessed,
'punc_id': punc_id_list, 'token': token, 'time_stamp': time_stamp_postprocessed}
item = {'key': key, 'value': item}
asr_result_list.append(item)
finish_count += 1
# asr_utils.print_progress(finish_count / file_count)
if writer is not None:
ibest_writer["text"][key] = text_postprocessed
if time_stamp_writer and time_stamp_postprocessed is not None:
ibest_writer["time_stamp"][key] = " ".join(
["-".join(map(str, ts)) for ts in time_stamp_postprocessed])
ibest_writer["punc_id"][key] = "{}".format(punc_id_list)
ibest_writer["text_with_punc"][key] = text_postprocessed_punc_time_stamp
if time_stamp_postprocessed is not None:
ibest_writer["time_stamp"][key] = "{}".format(time_stamp_postprocessed)
logging.info("decoding, utt: {}, predictions: {}, time_stamp: {}".format(key, text_postprocessed_punc,
time_stamp_postprocessed))
@ -869,7 +882,6 @@ def Text2Punc(
punc_list[i] = "？"
elif punc_list[i] == "。":
period = i
preprocessor = CommonPreprocessor(
train=False,
token_type="word",
@ -887,7 +899,8 @@ def Text2Punc(
cache_sent = []
mini_sentences = split_to_mini_sentence(words, split_size)
new_mini_sentence = ""
new_mini_sentence_punc = ""
new_mini_sentence_punc = []
cache_pop_trigger_limit = 200
for mini_sentence_i in range(len(mini_sentences)):
mini_sentence = mini_sentences[mini_sentence_i]
mini_sentence = cache_sent + mini_sentence
@ -904,24 +917,31 @@ def Text2Punc(
if indices.size()[0] != 1:
punctuations = torch.squeeze(indices)
assert punctuations.size()[0] == len(mini_sentence)
# Search for the last Period/QuestionMark as cache
if mini_sentence_i < len(mini_sentences) - 1:
sentenceEnd = -1
last_comma_index = -1
for i in range(len(punctuations) - 2, 1, -1):
if punc_list[punctuations[i]] == "。" or punc_list[punctuations[i]] == "？":
sentenceEnd = i
break
if last_comma_index < 0 and punc_list[punctuations[i]] == "，":
last_comma_index = i
if sentenceEnd < 0 and len(mini_sentence) > cache_pop_trigger_limit and last_comma_index >= 0:
# The sentence it too long, cut off at a comma.
sentenceEnd = last_comma_index
punctuations[sentenceEnd] = period
cache_sent = mini_sentence[sentenceEnd + 1:]
mini_sentence = mini_sentence[0:sentenceEnd + 1]
punctuations = punctuations[0:sentenceEnd + 1]
# if len(punctuations) == 0:
# continue
punctuations_np = punctuations.cpu().numpy()
new_mini_sentence_punc += "".join([str(x) for x in punctuations_np])
new_mini_sentence_punc += [int(x) for x in punctuations_np]
words_with_punc = []
for i in range(len(mini_sentence)):
if i > 0:
@ -931,9 +951,8 @@ def Text2Punc(
if punc_list[punctuations[i]] != "_":
words_with_punc.append(punc_list[punctuations[i]])
new_mini_sentence += "".join(words_with_punc)
return new_mini_sentence, new_mini_sentence_punc
return _forward
def get_parser():