| | |
|---|
| | | am_scores, valid_token_lens = outputs[0], outputs[1] |
|---|
| | | if len(outputs) == 4: |
|---|
| | | # for BiCifParaformer Inference |
|---|
| | | us_alphas, us_cif_peak = outputs[2], outputs[3] |
|---|
| | | us_alphas, us_peaks = outputs[2], outputs[3] |
|---|
| | | else: |
|---|
| | | us_alphas, us_cif_peak = None, None |
|---|
| | | us_alphas, us_peaks = None, None |
|---|
| | | except ONNXRuntimeError: |
|---|
| | | #logging.warning(traceback.format_exc()) |
|---|
| | | logging.warning("input wav is silence or noise") |
|---|
| | | preds = [''] |
|---|
| | | else: |
|---|
| | | preds = self.decode(am_scores, valid_token_lens) |
|---|
| | | if us_cif_peak is None: |
|---|
| | | if us_peaks is None: |
|---|
| | | for pred in preds: |
|---|
| | | pred = sentence_postprocess(pred) |
|---|
| | | asr_res.append({'preds': pred}) |
|---|
| | | else: |
|---|
| | | for pred, us_cif_peak_ in zip(preds, us_cif_peak): |
|---|
| | | for pred, us_peaks_ in zip(preds, us_peaks): |
|---|
| | | raw_tokens = pred |
|---|
| | | timestamp, timestamp_raw = time_stamp_lfr6_onnx(us_cif_peak_, copy.copy(raw_tokens)) |
|---|
| | | timestamp, timestamp_raw = time_stamp_lfr6_onnx(us_peaks_, copy.copy(raw_tokens)) |
|---|
| | | text_proc, timestamp_proc, _ = sentence_postprocess(raw_tokens, timestamp_raw) |
|---|
| | | # logging.warning(timestamp) |
|---|
| | | if len(self.plot_timestamp_to): |
|---|
