| | |
|---|
| | | if not Path(model_dir).exists(): |
|---|
| | | raise FileNotFoundError(f'{model_dir} does not exist.') |
|---|
| | | |
|---|
| | | model_file = os.path.join(model_dir, 'model.onnx') |
|---|
| | | model_file = os.path.join(model_dir, 'model.torchscripts') |
|---|
| | | config_file = os.path.join(model_dir, 'config.yaml') |
|---|
| | | cmvn_file = os.path.join(model_dir, 'am.mvn') |
|---|
| | | config = read_yaml(config_file) |
|---|
| | |
|---|
| | | feats, feats_len = self.extract_feat(waveform_list[beg_idx:end_idx]) |
|---|
| | | |
|---|
| | | try: |
|---|
| | | outputs = self.infer(feats, feats_len) |
|---|
| | | outs = outputs[0], outputs[1] |
|---|
| | | am_scores, valid_token_lens = outs[0], outs[1] |
|---|
| | | outputs = self.ort_infer(feats, feats_len) |
|---|
| | | 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] |
|---|
| | |
|---|
| | | logging.warning("input wav is silence or noise") |
|---|
| | | preds = [''] |
|---|
| | | else: |
|---|
| | | am_scores, valid_token_lens = am_scores.cpu().numpy(), valid_token_lens.cpu().numpy() |
|---|
| | | am_scores, valid_token_lens = am_scores.detach().cpu().numpy(), valid_token_lens.detach().cpu().numpy() |
|---|
| | | preds, raw_token = self.decode(am_scores, valid_token_lens)[0] |
|---|
| | | res['preds'] = preds |
|---|
| | | if us_cif_peak is not None: |
|---|
| | |
|---|
| | | |
|---|
| | | feats = self.pad_feats(feats, np.max(feats_len)) |
|---|
| | | feats_len = np.array(feats_len).astype(np.int32) |
|---|
| | | feats = torch.from_numpy(feats).type(torch.float32) |
|---|
| | | feats_len = torch.from_numpy(feats_len).type(torch.int32) |
|---|
| | | return feats, feats_len |
|---|
| | | |
|---|
| | | @staticmethod |
|---|
