speech_recognition/FunASR
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asr_inference pipeline supports combine tp model

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shixian.shi 2023-03-21 19:41:40 +08:00
parent 3258d2be0a
commit 8cd93a1fc7
2 changed files with 15 additions and 13 deletions
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24
funasr/bin/asr_inference_paraformer.py
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@ -756,16 +756,18 @@ def inference_modelscope(
key = keys[batch_id] key = keys[batch_id]
for n, result in zip(range(1, nbest + 1), result): for n, result in zip(range(1, nbest + 1), result):
# import pdb; pdb.set_trace()
text, token, token_int, hyp = result[0], result[1], result[2], result[3] 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 # conduct timestamp prediction here
if time_stamp is None and speechtext2timestamp: # timestamp inference requires token length
# thus following inference cannot be conducted in batch
if timestamp is None and speechtext2timestamp:
ts_batch = {} ts_batch = {}
ts_batch['speech'] = batch['speech'][batch_id].squeeze(0) ts_batch['speech'] = batch['speech'][batch_id].unsqueeze(0)
ts_batch['speech_lengths'] = torch.tensor([batch['speech_lengths'][batch_id]]) ts_batch['speech_lengths'] = torch.tensor([batch['speech_lengths'][batch_id]])
ts_batch['text_lengths'] = torch.tensor([len(token)]) ts_batch['text_lengths'] = torch.tensor([len(token)])
import pdb; pdb.set_trace() 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 # Create a directory: outdir/{n}best_recog
if writer is not None: if writer is not None:
ibest_writer = writer[f"{n}best_recog"] ibest_writer = writer[f"{n}best_recog"]
@ -777,20 +779,20 @@ def inference_modelscope(
ibest_writer["rtf"][key] = rtf_cur ibest_writer["rtf"][key] = rtf_cur
if text is not None: if text is not None:
if use_timestamp and time_stamp is not None: if use_timestamp and timestamp is not None:
postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp) postprocessed_result = postprocess_utils.sentence_postprocess(token, timestamp)
else: else:
postprocessed_result = postprocess_utils.sentence_postprocess(token) postprocessed_result = postprocess_utils.sentence_postprocess(token)
time_stamp_postprocessed = "" timestamp_postprocessed = ""
if len(postprocessed_result) == 3: 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[1], \
postprocessed_result[2] postprocessed_result[2]
else: else:
text_postprocessed, word_lists = postprocessed_result[0], postprocessed_result[1] text_postprocessed, word_lists = postprocessed_result[0], postprocessed_result[1]
item = {'key': key, 'value': text_postprocessed} item = {'key': key, 'value': text_postprocessed}
if time_stamp_postprocessed != "": if timestamp_postprocessed != "":
item['time_stamp'] = time_stamp_postprocessed item['timestamp'] = timestamp_postprocessed
asr_result_list.append(item) asr_result_list.append(item)
finish_count += 1 finish_count += 1
# asr_utils.print_progress(finish_count / file_count) # 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] enc = enc[0]
# c. Forward Predictor # c. Forward Predictor
_, _, us_alphas, us_cif_peak = self.tp_model.calc_predictor_timestamp(enc, enc_len, text_lengths.to(self.device)+1) _, _, us_alphas, us_peaks = self.tp_model.calc_predictor_timestamp(enc, enc_len, text_lengths.to(self.device)+1)
return us_alphas, us_cif_peak return us_alphas, us_peaks
def inference( def inference(