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Merge pull request #276 from alibaba-damo-academy/dev_sx

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Dev sx
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zhifu gao 2023-03-21 20:18:21 +08:00 committed by GitHub
commit 1f0f44bd07
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2 changed files with 33 additions and 10 deletions
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39
funasr/bin/asr_inference_paraformer.py
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@ -43,6 +43,7 @@ from funasr.models.frontend.wav_frontend import WavFrontend
from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer
from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export
from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
from funasr.bin.tp_inference import SpeechText2Timestamp
class Speech2Text:
@ -540,7 +541,8 @@ def inference(
ngram_weight: float = 0.9,
nbest: int = 1,
num_workers: int = 1,
timestamp_infer_config: Union[Path, str] = None,
timestamp_model_file: Union[Path, str] = None,
**kwargs,
):
inference_pipeline = inference_modelscope(
@ -604,6 +606,8 @@ def inference_modelscope(
nbest: int = 1,
num_workers: int = 1,
output_dir: Optional[str] = None,
timestamp_infer_config: Union[Path, str] = None,
timestamp_model_file: Union[Path, str] = None,
param_dict: dict = None,
**kwargs,
):
@ -661,6 +665,15 @@ def inference_modelscope(
else:
speech2text = Speech2Text(**speech2text_kwargs)
if timestamp_model_file is not None:
speechtext2timestamp = SpeechText2Timestamp(
timestamp_cmvn_file=cmvn_file,
timestamp_model_file=timestamp_model_file,
timestamp_infer_config=timestamp_infer_config,
)
else:
speechtext2timestamp = None
def _forward(
data_path_and_name_and_type,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
@ -744,7 +757,17 @@ def inference_modelscope(
key = keys[batch_id]
for n, result in zip(range(1, nbest + 1), result):
text, token, token_int, hyp = result[0], result[1], result[2], result[3]
time_stamp = None if len(result) < 5 else result[4]
timestamp = None if len(result) < 5 else result[4]
# conduct timestamp prediction here
# timestamp inference requires token length
# thus following inference cannot be conducted in batch
if timestamp is None and speechtext2timestamp:
ts_batch = {}
ts_batch['speech'] = batch['speech'][batch_id].unsqueeze(0)
ts_batch['speech_lengths'] = torch.tensor([batch['speech_lengths'][batch_id]])
ts_batch['text_lengths'] = torch.tensor([len(token)])
us_alphas, us_peaks = speechtext2timestamp(**ts_batch)
ts_str, timestamp = ts_prediction_lfr6_standard(us_alphas[0], us_peaks[0], token, force_time_shift=-3.0)
# Create a directory: outdir/{n}best_recog
if writer is not None:
ibest_writer = writer[f"{n}best_recog"]
@ -756,20 +779,20 @@ def inference_modelscope(
ibest_writer["rtf"][key] = rtf_cur
if text is not None:
if use_timestamp and time_stamp is not None:
postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
if use_timestamp and timestamp is not None:
postprocessed_result = postprocess_utils.sentence_postprocess(token, timestamp)
else:
postprocessed_result = postprocess_utils.sentence_postprocess(token)
time_stamp_postprocessed = ""
timestamp_postprocessed = ""
if len(postprocessed_result) == 3:
text_postprocessed, time_stamp_postprocessed, word_lists = postprocessed_result[0], \
text_postprocessed, timestamp_postprocessed, word_lists = postprocessed_result[0], \
postprocessed_result[1], \
postprocessed_result[2]
else:
text_postprocessed, word_lists = postprocessed_result[0], postprocessed_result[1]
item = {'key': key, 'value': text_postprocessed}
if time_stamp_postprocessed != "":
item['time_stamp'] = time_stamp_postprocessed
if timestamp_postprocessed != "":
item['timestamp'] = timestamp_postprocessed
asr_result_list.append(item)
finish_count += 1
# asr_utils.print_progress(finish_count / file_count)

4
funasr/bin/tp_inference.py
View File

@ -116,8 +116,8 @@ class SpeechText2Timestamp:
enc = enc[0]
# c. Forward Predictor
_, _, us_alphas, us_cif_peak = self.tp_model.calc_predictor_timestamp(enc, enc_len, text_lengths.to(self.device)+1)
return us_alphas, us_cif_peak
_, _, us_alphas, us_peaks = self.tp_model.calc_predictor_timestamp(enc, enc_len, text_lengths.to(self.device)+1)
return us_alphas, us_peaks
def inference(