| | |
|---|
| | | if spk_mode not in ["default", "vad_segment", "punc_segment"]: |
|---|
| | | logging.error("spk_mode should be one of default, vad_segment and punc_segment.") |
|---|
| | | self.spk_mode = spk_mode |
|---|
| | | self.preset_spk_num = kwargs.get("preset_spk_num", None) |
|---|
| | | if self.preset_spk_num: |
|---|
| | | logging.warning("Using preset speaker number: {}".format(self.preset_spk_num)) |
|---|
| | | |
|---|
| | | self.kwargs = kwargs |
|---|
| | | self.model = model |
|---|
| | |
|---|
| | | data_batch = data_list[beg_idx:end_idx] |
|---|
| | | key_batch = key_list[beg_idx:end_idx] |
|---|
| | | batch = {"data_in": data_batch, "key": key_batch} |
|---|
| | | if (end_idx - beg_idx) == 1 and isinstance(data_batch[0], torch.Tensor): # fbank |
|---|
| | | if (end_idx - beg_idx) == 1 and kwargs.get("data_type", None) == "fbank": # fbank |
|---|
| | | batch["data_in"] = data_batch[0] |
|---|
| | | batch["data_lengths"] = input_len |
|---|
| | | |
|---|
| | |
|---|
| | | if self.spk_model is not None: |
|---|
| | | all_segments = sorted(all_segments, key=lambda x: x[0]) |
|---|
| | | spk_embedding = result['spk_embedding'] |
|---|
| | | labels = self.cb_model(spk_embedding.cpu(), oracle_num=self.preset_spk_num) |
|---|
| | | labels = self.cb_model(spk_embedding.cpu(), oracle_num=kwargs['preset_spk_num']) |
|---|
| | | del result['spk_embedding'] |
|---|
| | | sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu()) |
|---|
| | | if self.spk_mode == 'vad_segment': # recover sentence_list |
|---|
