| | |
|---|
| | | feats_len = speech_lengths |
|---|
| | | |
|---|
| | | if feats.shape[1] != 0: |
|---|
| | | if cache_en["is_final"]: |
|---|
| | | if feats.shape[1] + cache_en["chunk_size"][2] < cache_en["chunk_size"][1]: |
|---|
| | | cache_en["last_chunk"] = True |
|---|
| | | else: |
|---|
| | | # first chunk |
|---|
| | | feats_chunk1 = feats[:, :cache_en["chunk_size"][1], :] |
|---|
| | | feats_len = torch.tensor([feats_chunk1.shape[1]]) |
|---|
| | | results_chunk1 = self.infer(feats_chunk1, feats_len, cache) |
|---|
| | | |
|---|
| | | # last chunk |
|---|
| | | cache_en["last_chunk"] = True |
|---|
| | | feats_chunk2 = feats[:, -(feats.shape[1] + cache_en["chunk_size"][2] - cache_en["chunk_size"][1]):, :] |
|---|
| | | feats_len = torch.tensor([feats_chunk2.shape[1]]) |
|---|
| | | results_chunk2 = self.infer(feats_chunk2, feats_len, cache) |
|---|
| | | |
|---|
| | | return [" ".join(results_chunk1 + results_chunk2)] |
|---|
| | | |
|---|
| | | results = self.infer(feats, feats_len, cache) |
|---|
| | | |
|---|
| | | return results |
|---|
