增加分角色语音识别对ERes2Net模型的支持。

夜雨飘零

2023-12-04 73613cefc97bd43699d10b8d162c69b2c4544ad5

 funasr/bin/asr_inference_launch.py

@@ -48,13 +48,13 @@
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none
from funasr.utils.vad_utils import slice_padding_fbank
from funasr.utils.speaker_utils import (check_audio_list, 
                                        sv_preprocess, 
                                        sv_chunk, 
                                        CAMPPlus, 
                                        extract_feature, 
from funasr.utils.speaker_utils import (check_audio_list,
                                        sv_preprocess,
                                        sv_chunk,
                                        CAMPPlus,
                                        extract_feature,
                                        postprocess,
                                        distribute_spk)
                                        distribute_spk, ERes2Net)
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
@@ -819,6 +819,10 @@
    )

    sv_model_file = asr_model_file.replace("model.pb", "campplus_cn_common.bin")
    if not os.path.exists(sv_model_file):
        sv_model_file = asr_model_file.replace("model.pb", "pretrained_eres2net_aug.ckpt")
        if not os.path.exists(sv_model_file):
            raise FileNotFoundError("sv_model_file not found: {}".format(sv_model_file))

    if param_dict is not None:
        hotword_list_or_file = param_dict.get('hotword')
@@ -944,8 +948,14 @@
            #####  speaker_verification  #####
            ##################################
            # load sv model
            sv_model_dict = torch.load(sv_model_file, map_location=torch.device('cpu'))
            sv_model = CAMPPlus()
            sv_model_dict = torch.load(sv_model_file)
            print(f'load sv model params: {sv_model_file}')
            if os.path.basename(sv_model_file) == "campplus_cn_common.bin":
                sv_model = CAMPPlus()
            else:
                sv_model = ERes2Net()
            if ngpu > 0:
                sv_model.cuda()
            sv_model.load_state_dict(sv_model_dict)
            sv_model.eval()
            cb_model = ClusterBackend()
@@ -969,9 +979,11 @@
                    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)