Bug fix for res combine

examples/industrial_data_pretraining/seaco_paraformer/demo.py

@@ -15,6 +15,8 @@ model = AutoModel(model="iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-com
                   # spk_model_revision="v2.0.2",
                   )
 
-res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
-                     hotword='达摩院 魔搭')
+res = model.generate(input="/Users/shixian/Downloads/output_16000.wav",
+                     hotword='达摩院 魔搭',
+                     # sentence_timestamp=True,
+                    )
 print(res)

funasr/auto/auto_model.py

@@ -123,7 +123,6 @@ class AutoModel:
             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))
-            logging.warning("Many to print when using speaker model...")
             
         self.kwargs = kwargs
         self.model = model
@@ -329,8 +328,6 @@ class AutoModel:
                 speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])       
                 results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, disable_pbar=True, **cfg)
                 if self.spk_model is not None:
-
-                  
                     # compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
                     for _b in range(len(speech_j)):
                         vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0,
@@ -345,8 +342,6 @@ class AutoModel:
                 if len(results) < 1:
                     continue
                 results_sorted.extend(results)
-
-
             
             end_asr_total = time.time()
             time_escape_total_per_sample = end_asr_total - beg_asr_total
@@ -355,7 +350,6 @@ class AutoModel:
                                  f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
                                  f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")
             
-
             restored_data = [0] * n
             for j in range(n):
                 index = sorted_data[j][1]
@@ -378,7 +372,7 @@ class AutoModel:
                             result[k] = restored_data[j][k]
                         else:
                             result[k] = torch.cat([result[k], restored_data[j][k]], dim=0)
-                    elif k == 'raw_text':
+                    elif 'text' in k:
                         if k not in result:
                             result[k] = restored_data[j][k]
                         else:
@@ -393,8 +387,9 @@ class AutoModel:
             if self.punc_model is not None:
                 self.punc_kwargs.update(cfg)
                 punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, **cfg)
+                import copy; raw_text = copy.copy(result["text"])
                 result["text"] = punc_res[0]["text"]
-                     
+                
             # speaker embedding cluster after resorted
             if self.spk_model is not None:
                 all_segments = sorted(all_segments, key=lambda x: x[0])
@@ -402,19 +397,24 @@ class AutoModel:
                 labels = self.cb_model(spk_embedding.cpu(), oracle_num=self.preset_spk_num)
                 del result['spk_embedding']
                 sv_output = postprocess(all_segments, None, labels, spk_embedding.cpu())
-                if self.spk_mode == 'vad_segment':
+                if self.spk_mode == 'vad_segment':  # recover sentence_list
                     sentence_list = []
                     for res, vadsegment in zip(restored_data, vadsegments):
                         sentence_list.append({"start": vadsegment[0],\
                                                 "end": vadsegment[1],
                                                 "sentence": res['raw_text'],
                                                 "timestamp": res['timestamp']})
-                else: # punc_segment
+                elif self.spk_mode == 'punc_segment':
                     sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
                                                         result['timestamp'], \
                                                         result['raw_text'])
                 distribute_spk(sentence_list, sv_output)
                 result['sentence_info'] = sentence_list
+            elif kwargs.get("sentence_timestamp", False):
+                sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
+                                                        result['timestamp'], \
+                                                        result['raw_text'])
+                result['sentence_info'] = sentence_list
                     
             result["key"] = key
             results_ret_list.append(result)

funasr/models/contextual_paraformer/model.py

@@ -65,11 +65,9 @@ class ContextualParaformer(Paraformer):
 
 
         if bias_encoder_type == 'lstm':
-            logging.warning("enable bias encoder sampling and contextual training")
             self.bias_encoder = torch.nn.LSTM(inner_dim, inner_dim, 1, batch_first=True, dropout=bias_encoder_dropout_rate)
             self.bias_embed = torch.nn.Embedding(self.vocab_size, inner_dim)
         elif bias_encoder_type == 'mean':
-            logging.warning("enable bias encoder sampling and contextual training")
             self.bias_embed = torch.nn.Embedding(self.vocab_size, inner_dim)
         else:
             logging.error("Unsupport bias encoder type: {}".format(bias_encoder_type))

funasr/models/seaco_paraformer/model.py

@@ -66,7 +66,6 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
   
         # bias encoder
         if self.bias_encoder_type == 'lstm':
-            logging.warning("enable bias encoder sampling and contextual training")
             self.bias_encoder = torch.nn.LSTM(self.inner_dim, 
                                               self.inner_dim, 
                                               2, 
@@ -79,7 +78,6 @@ class SeacoParaformer(BiCifParaformer, Paraformer):
                 self.lstm_proj = None
             self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
         elif self.bias_encoder_type == 'mean':
-            logging.warning("enable bias encoder sampling and contextual training")
             self.bias_embed = torch.nn.Embedding(self.vocab_size, self.inner_dim)
         else:
             logging.error("Unsupport bias encoder type: {}".format(self.bias_encoder_type))