funasr/models/e2e_diar_eend_ola.py
@@ -157,12 +157,11 @@ def estimate_sequential(self, speech: torch.Tensor, speech_lengths: torch.Tensor, n_speakers: int = None, shuffle: bool = True, threshold: float = 0.5, **kwargs): speech = [s[:s_len] for s, s_len in zip(speech, speech_lengths)] speech_lengths = torch.tensor([len(sph) for sph in speech]).to(torch.int64) emb = self.forward_encoder(speech, speech_lengths) if shuffle: orders = [np.arange(e.shape[0]) for e in emb]
