| | |
|---|
| | | _language_list = _language_list * B |
|---|
| | | if len(_textnorm_list) == 1 and B != 1: |
|---|
| | | _textnorm_list = _textnorm_list * B |
|---|
| | | |
|---|
| | | |
|---|
| | | ctc_logits, encoder_out_lens = self.ort_infer( |
|---|
| | | torch.Tensor(feats).to(self.device), |
|---|
| | | torch.Tensor(feats_len).to(self.device), |
|---|
| | | torch.tensor([_language_list]).to(self.device), |
|---|
| | | torch.tensor([_textnorm_list]).to(self.device), |
|---|
| | | torch.tensor(_language_list).to(self.device), |
|---|
| | | torch.tensor(_textnorm_list).to(self.device), |
|---|
| | | ) |
|---|
| | | # support batch_size=1 only currently |
|---|
| | | x = ctc_logits[0, : encoder_out_lens[0].item(), :] |
|---|
| | | yseq = x.argmax(dim=-1) |
|---|
| | | yseq = torch.unique_consecutive(yseq, dim=-1) |
|---|
| | | for b in range(feats.shape[0]): |
|---|
| | | # back to torch.Tensor |
|---|
| | | if isinstance(ctc_logits, np.ndarray): |
|---|
| | | ctc_logits = torch.from_numpy(ctc_logits).float() |
|---|
| | | # support batch_size=1 only currently |
|---|
| | | x = ctc_logits[b, : encoder_out_lens[b].item(), :] |
|---|
| | | yseq = x.argmax(dim=-1) |
|---|
| | | yseq = torch.unique_consecutive(yseq, dim=-1) |
|---|
| | | |
|---|
| | | mask = yseq != self.blank_id |
|---|
| | | token_int = yseq[mask].tolist() |
|---|
| | | mask = yseq != self.blank_id |
|---|
| | | token_int = yseq[mask].tolist() |
|---|
| | | |
|---|
| | | asr_res.append(self.tokenizer.decode(token_int)) |
|---|
| | | asr_res.append(self.tokenizer.decode(token_int)) |
|---|
| | | |
|---|
| | | return asr_res |
|---|
| | | |
|---|
