| | |
|---|
| | | device_id: Union[str, int] = "-1", |
|---|
| | | plot_timestamp_to: str = "", |
|---|
| | | pred_bias: int = 1, |
|---|
| | | quantize: bool = False, |
|---|
| | | intra_op_num_threads: int = 4, |
|---|
| | | ): |
|---|
| | | |
|---|
| | | if not Path(model_dir).exists(): |
|---|
| | | raise FileNotFoundError(f'{model_dir} does not exist.') |
|---|
| | | |
|---|
| | | model_file = os.path.join(model_dir, 'model.onnx') |
|---|
| | | if quantize: |
|---|
| | | model_file = os.path.join(model_dir, 'model_quant.onnx') |
|---|
| | | config_file = os.path.join(model_dir, 'config.yaml') |
|---|
| | | cmvn_file = os.path.join(model_dir, 'am.mvn') |
|---|
| | | config = read_yaml(config_file) |
|---|
| | |
|---|
| | | cmvn_file=cmvn_file, |
|---|
| | | **config['frontend_conf'] |
|---|
| | | ) |
|---|
| | | self.ort_infer = OrtInferSession(model_file, device_id) |
|---|
| | | self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads) |
|---|
| | | self.batch_size = batch_size |
|---|
| | | self.plot_timestamp_to = plot_timestamp_to |
|---|
| | | self.pred_bias = pred_bias |
|---|
| | |
|---|
| | | 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): |
|---|
| | | text, tokens = pred |
|---|
| | | timestamp, timestamp_total = time_stamp_lfr6_onnx(us_cif_peak_, copy.copy(tokens)) |
|---|
| | | for pred, us_peaks_ in zip(preds, us_peaks): |
|---|
| | | raw_tokens = pred |
|---|
| | | 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): |
|---|
| | | self.plot_wave_timestamp(waveform_list[0], timestamp_total, self.plot_timestamp_to) |
|---|
| | | asr_res.append({'preds': text, 'timestamp': timestamp}) |
|---|
| | | self.plot_wave_timestamp(waveform_list[0], timestamp, self.plot_timestamp_to) |
|---|
| | | asr_res.append({'preds': text_proc, 'timestamp': timestamp_proc, "raw_tokens": raw_tokens}) |
|---|
| | | return asr_res |
|---|
| | | |
|---|
| | | def plot_wave_timestamp(self, wav, text_timestamp, dest): |
|---|
| | |
|---|
| | | # Change integer-ids to tokens |
|---|
| | | token = self.converter.ids2tokens(token_int) |
|---|
| | | token = token[:valid_token_num-self.pred_bias] |
|---|
| | | texts = sentence_postprocess(token) |
|---|
| | | return texts |
|---|
| | | # texts = sentence_postprocess(token) |
|---|
| | | return token |
|---|
| | | |
|---|
