update with main (#1152)

zhifu gao

2023-12-06 81acb17544a05424dff0ef74f3aeb2ce9866ba6a

 funasr/bin/asr_inference_launch.py

@@ -51,10 +51,10 @@
from funasr.utils.speaker_utils import (check_audio_list, 
                                        sv_preprocess, 
                                        sv_chunk, 
                                        CAMPPlus, 
                                        extract_feature, 
                                        postprocess,
                                        distribute_spk)
import funasr.modules.cnn as sv_module
from funasr.build_utils.build_model_from_file import build_model_from_file
from funasr.utils.cluster_backend import ClusterBackend
from funasr.utils.modelscope_utils import get_cache_dir
@@ -818,7 +818,15 @@
        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
    )

    sv_model_file = asr_model_file.replace("model.pb", "campplus_cn_common.bin")
    sv_model_config_path = asr_model_file.replace("model.pb", "sv_model_config.yaml")
    if not os.path.exists(sv_model_config_path):
        sv_model_config = {'sv_model_class': 'CAMPPlus','sv_model_file': 'campplus_cn_common.bin', 'models_config': {}}
    else:
        with open(sv_model_config_path, 'r') as f:
            sv_model_config = yaml.load(f, Loader=yaml.FullLoader)
    if sv_model_config['models_config'] is None:
        sv_model_config['models_config'] = {}
    sv_model_file = asr_model_file.replace("model.pb", sv_model_config['sv_model_file'])

    if param_dict is not None:
        hotword_list_or_file = param_dict.get('hotword')
@@ -944,9 +952,15 @@
            #####  speaker_verification  #####
            ##################################
            # load sv model
            if ngpu > 0:
                sv_model_dict = torch.load(sv_model_file)
                sv_model = getattr(sv_module, sv_model_config['sv_model_class'])(**sv_model_config['models_config'])
                sv_model.cuda()
            else:
            sv_model_dict = torch.load(sv_model_file, map_location=torch.device('cpu'))
            sv_model = CAMPPlus()
                sv_model = getattr(sv_module, sv_model_config['sv_model_class'])(**sv_model_config['models_config'])
            sv_model.load_state_dict(sv_model_dict)
            print(f'load sv model params: {sv_model_file}')
            sv_model.eval()
            cb_model = ClusterBackend()
            vad_segments = []
@@ -969,9 +983,11 @@
                    embs = []
                    for x in wavs:
                        x = extract_feature([x])
                        if ngpu > 0:
                            x = x.cuda()
                        embs.append(sv_model(x))
                    embs = torch.cat(embs)
                    embeddings.append(embs.detach().numpy())
                    embeddings.append(embs.cpu().detach().numpy())
                embeddings = np.concatenate(embeddings)
                labels = cb_model(embeddings)
                sv_output = postprocess(segments, vad_segments, labels, embeddings)