| | |
|---|
| | | mini_sentence = cache_sent + mini_sentence |
|---|
| | | mini_sentence_id = np.concatenate((cache_sent_id, mini_sentence_id), axis=0,dtype='int32') |
|---|
| | | text_length = len(mini_sentence_id) |
|---|
| | | vad_mask = self.vad_mask(text_length, len(cache))[None, None, :, :].astype(np.float32) |
|---|
| | | data = { |
|---|
| | | "input": mini_sentence_id[None,:], |
|---|
| | | "text_lengths": np.array([text_length], dtype='int32'), |
|---|
| | | "vad_mask": self.vad_mask(text_length, len(cache))[None, None, :, :].astype(np.float32), |
|---|
| | | "sub_masks": np.tril(np.ones((text_length, text_length), dtype=np.float32))[None, None, :, :].astype(np.float32) |
|---|
| | | "vad_mask": vad_mask, |
|---|
| | | "sub_masks": vad_mask |
|---|
| | | } |
|---|
| | | try: |
|---|
| | | outputs = self.infer(data['input'], data['text_lengths'], data['vad_mask'], data["sub_masks"]) |
|---|
