| | |
|---|
| | | def _add_overlap_chunk(self, feats: np.ndarray, cache: dict = {}): |
|---|
| | | if len(cache) == 0: |
|---|
| | | return feats |
|---|
| | | # process last chunk |
|---|
| | | cache["feats"] = to_device(cache["feats"], device=feats.device) |
|---|
| | | overlap_feats = torch.cat((cache["feats"], feats), dim=1) |
|---|
| | | if cache["is_final"]: |
|---|
| | | cache["feats"] = overlap_feats[:, -cache["chunk_size"][0]:, :] |
|---|
| | | if not cache["last_chunk"]: |
|---|
| | | padding_length = sum(cache["chunk_size"]) - overlap_feats.shape[1] |
|---|
| | | overlap_feats = overlap_feats.transpose(1, 2) |
|---|
| | | overlap_feats = F.pad(overlap_feats, (0, padding_length)) |
|---|
| | | overlap_feats = overlap_feats.transpose(1, 2) |
|---|
| | | else: |
|---|
| | | cache["feats"] = overlap_feats[:, -(cache["chunk_size"][0] + cache["chunk_size"][2]):, :] |
|---|
| | | cache["feats"] = overlap_feats[:, -(cache["chunk_size"][0] + cache["chunk_size"][2]):, :] |
|---|
| | | return overlap_feats |
|---|
| | | |
|---|
| | | def forward_chunk(self, |
|---|
