update

funasr/models/e2e_diar_eend_ola.py

@@ -52,15 +52,15 @@ class DiarEENDOLAModel(AbsESPnetModel):
 
         super().__init__()
         self.frontend = frontend
-        self.encoder = encoder
-        self.encoder_decoder_attractor = encoder_decoder_attractor
+        self.enc = encoder
+        self.eda = encoder_decoder_attractor
         self.attractor_loss_weight = attractor_loss_weight
         self.max_n_speaker = max_n_speaker
         if mapping_dict is None:
             mapping_dict = generate_mapping_dict(max_speaker_num=self.max_n_speaker)
             self.mapping_dict = mapping_dict
         # PostNet
-        self.PostNet = nn.LSTM(self.max_n_speaker, n_units, 1, batch_first=True)
+        self.postnet = nn.LSTM(self.max_n_speaker, n_units, 1, batch_first=True)
         self.output_layer = nn.Linear(n_units, mapping_dict['oov'] + 1)
 
     def forward_encoder(self, xs, ilens):
@@ -68,7 +68,7 @@ class DiarEENDOLAModel(AbsESPnetModel):
         pad_shape = xs.shape
         xs_mask = [torch.ones(ilen).to(xs.device) for ilen in ilens]
         xs_mask = torch.nn.utils.rnn.pad_sequence(xs_mask, batch_first=True, padding_value=0).unsqueeze(-2)
-        emb = self.encoder(xs, xs_mask)
+        emb = self.enc(xs, xs_mask)
         emb = torch.split(emb.view(pad_shape[0], pad_shape[1], -1), 1, dim=0)
         emb = [e[0][:ilen] for e, ilen in zip(emb, ilens)]
         return emb
@@ -77,7 +77,7 @@ class DiarEENDOLAModel(AbsESPnetModel):
         maxlen = torch.max(ilens).to(torch.int).item()
         logits = nn.utils.rnn.pad_sequence(logits, batch_first=True, padding_value=-1)
         logits = nn.utils.rnn.pack_padded_sequence(logits, ilens, batch_first=True, enforce_sorted=False)
-        outputs, (_, _) = self.PostNet(logits)
+        outputs, (_, _) = self.postnet(logits)
         outputs = nn.utils.rnn.pad_packed_sequence(outputs, batch_first=True, padding_value=-1, total_length=maxlen)[0]
         outputs = [output[:ilens[i].to(torch.int).item()] for i, output in enumerate(outputs)]
         outputs = [self.output_layer(output) for output in outputs]
@@ -112,7 +112,7 @@ class DiarEENDOLAModel(AbsESPnetModel):
         text = text[:, : text_lengths.max()]
 
         # 1. Encoder
-        encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+        encoder_out, encoder_out_lens = self.enc(speech, speech_lengths)
         intermediate_outs = None
         if isinstance(encoder_out, tuple):
             intermediate_outs = encoder_out[1]
@@ -198,10 +198,10 @@ class DiarEENDOLAModel(AbsESPnetModel):
             orders = [np.arange(e.shape[0]) for e in emb]
             for order in orders:
                 np.random.shuffle(order)
-            attractors, probs = self.encoder_decoder_attractor.estimate(
+            attractors, probs = self.eda.estimate(
                 [e[torch.from_numpy(order).to(torch.long).to(speech[0].device)] for e, order in zip(emb, orders)])
         else:
-            attractors, probs = self.encoder_decoder_attractor.estimate(emb)
+            attractors, probs = self.eda.estimate(emb)
         attractors_active = []
         for p, att, e in zip(probs, attractors, emb):
             if n_speakers and n_speakers >= 0:

tests/test_asr_inference_pipeline.py

@@ -451,7 +451,7 @@ class TestUniasrInferencePipelines(unittest.TestCase):
 
     def test_uniasr_2pass_zhcn_16k_common_vocab8358_offline(self):
         inference_pipeline = pipeline(
-            task=Tasks.,
+            task=Tasks.auto_speech_recognition,
             model='damo/speech_UniASauto_speech_recognitionR_asr_2pass-zh-cn-16k-common-vocab8358-tensorflow1-offline')
         rec_result = inference_pipeline(
             audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav',