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https://github.com/modelscope/FunASR
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update libtorch inference
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@ -62,26 +62,28 @@ class Paraformer():
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am_scores, valid_token_lens = outputs[0], outputs[1]
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if len(outputs) == 4:
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# for BiCifParaformer Inference
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us_alphas, us_cif_peak = outputs[2], outputs[3]
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us_alphas, us_peaks = outputs[2], outputs[3]
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else:
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us_alphas, us_cif_peak = None, None
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us_alphas, us_peaks = None, None
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except:
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#logging.warning(traceback.format_exc())
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logging.warning("input wav is silence or noise")
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preds = ['']
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else:
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am_scores, valid_token_lens = am_scores.detach().cpu().numpy(), valid_token_lens.detach().cpu().numpy()
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preds = self.decode(am_scores, valid_token_lens)
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if us_cif_peak is None:
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if us_peaks is None:
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for pred in preds:
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pred = sentence_postprocess(pred)
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asr_res.append({'preds': pred})
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else:
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for pred, us_cif_peak_ in zip(preds, us_cif_peak):
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text, tokens = pred
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timestamp, timestamp_total = time_stamp_lfr6_onnx(us_cif_peak_, copy.copy(tokens))
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for pred, us_peaks_ in zip(preds, us_peaks):
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raw_tokens = pred
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timestamp, timestamp_raw = time_stamp_lfr6_onnx(us_peaks_, copy.copy(raw_tokens))
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text_proc, timestamp_proc, _ = sentence_postprocess(raw_tokens, timestamp_raw)
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# logging.warning(timestamp)
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if len(self.plot_timestamp_to):
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self.plot_wave_timestamp(waveform_list[0], timestamp_total, self.plot_timestamp_to)
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asr_res.append({'preds': text, 'timestamp': timestamp})
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self.plot_wave_timestamp(waveform_list[0], timestamp, self.plot_timestamp_to)
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asr_res.append({'preds': text_proc, 'timestamp': timestamp_proc, "raw_tokens": raw_tokens})
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return asr_res
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def plot_wave_timestamp(self, wav, text_timestamp, dest):
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@ -182,6 +184,6 @@ class Paraformer():
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# Change integer-ids to tokens
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token = self.converter.ids2tokens(token_int)
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token = token[:valid_token_num-self.pred_bias]
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texts = sentence_postprocess(token)
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return texts
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# texts = sentence_postprocess(token)
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return token
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