add mossformer code

funasr/bin/ss_inference_launch.py

@@ -104,7 +104,7 @@ def inference_ss(
             ss_results = speech_separator(**batch)
             
             for spk in range(num_spks):
-                sf.write(os.path.join(output_path, keys[0].replace('.wav', '_s'+str(spk+1)+'.wav')), ss_results[spk], sample_rate)
+                sf.write(os.path.join(output_path, keys[0] + '_s' + str(spk+1)+'.wav'), ss_results[spk], sample_rate)
         torch.cuda.empty_cache()
         return ss_results
 

funasr/build_utils/build_model_from_file.py

@@ -11,18 +11,6 @@ from funasr.build_utils.build_model import build_model
 from funasr.models.base_model import FunASRModel
 
 
-def load_checkpoint(checkpoint_path, use_cuda=1):
-    if use_cuda:
-        checkpoint = torch.load(checkpoint_path)
-    else:
-        checkpoint = torch.load(
-            checkpoint_path, map_location=lambda storage, loc: storage)
-    return checkpoint
-
-def reload_ss_for_eval(model, checkpoint_path, use_cuda=False):
-    checkpoint = load_checkpoint(checkpoint_path, use_cuda)
-    model.load_state_dict(checkpoint['model'], strict=False)
-
 def build_model_from_file(
         config_file: Union[Path, str] = None,
         model_file: Union[Path, str] = None,
@@ -82,9 +70,8 @@ def build_model_from_file(
             model.load_state_dict(model_dict)
         else:
             model_dict = torch.load(model_file, map_location=device)
-    if task_name == 'ss':        
-        reload_ss_for_eval(model, model_file, use_cuda=True)
-        logging.info("model is loaded from path: {}".format(model_file))
+    if task_name == "ss":
+        model_dict = model_dict['model']
     if task_name == "diar" and mode == "sond":
         model_dict = fileter_model_dict(model_dict, model.state_dict())
     if task_name == "vad":