add mossformer code

haoneng.lhn

2023-08-10 b1e980d501f0e6132c76e977a459cf07f3794c56

add mossformer code

 funasr/bin/ss_inference_launch.py

@@ -104,7 +104,7 @@
            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.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 @@
            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":