| | |
|---|
| | | mini_sentence = cache_sent + mini_sentence |
|---|
| | | mini_sentence_id = np.concatenate((cache_sent_id, mini_sentence_id), axis=0) |
|---|
| | | data = { |
|---|
| | | "text": mini_sentence_id, |
|---|
| | | "text_lengths": len(mini_sentence_id), |
|---|
| | | "text": mini_sentence_id[None,:].astype(np.int64), |
|---|
| | | "text_lengths": np.array([len(mini_sentence_id)], dtype='int32'), |
|---|
| | | } |
|---|
| | | try: |
|---|
| | | outputs = self.infer(data['text'], data['text_lengths']) |
|---|
| | | y = outputs[0] |
|---|
| | | _, indices = y.view(-1, y.shape[-1]).topk(1, dim=1) |
|---|
| | | punctuations = indices |
|---|
| | | assert punctuations.size()[0] == len(mini_sentence) |
|---|
| | | punctuations = np.argmax(y,axis=-1)[0] |
|---|
| | | assert punctuations.size == len(mini_sentence) |
|---|
| | | except ONNXRuntimeError: |
|---|
| | | logging.warning("error") |
|---|
| | | |
|---|
| | |
|---|
| | | new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.period] |
|---|
| | | return new_mini_sentence_out, new_mini_sentence_punc_out |
|---|
| | | |
|---|
| | | def infer(self, feats: List) -> Tuple[np.ndarray, np.ndarray]: |
|---|
| | | |
|---|
| | | outputs = self.ort_infer(feats) |
|---|
| | | def infer(self, feats: np.ndarray, |
|---|
| | | feats_len: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: |
|---|
| | | outputs = self.ort_infer([feats, feats_len]) |
|---|
| | | return outputs |
|---|
| | | |
|---|
