config.py

class Config(object):
    def __init__(self, config_dict: dict):
        for key, val in config_dict.items():
            if val is not None:
                self.__setattr__(key, val)

    def copy(self, new_config_dict={}):
        ret = Config(vars(self))
        for key, val in new_config_dict.items():
            if val is not None:
                ret.__setattr__(key, val)
        return ret

    def replace(self, new_config_dict):
        if isinstance(new_config_dict, Config):
            new_config_dict = vars(new_config_dict)

        for key, val in new_config_dict.items():
            if val is not None:
                self.__setattr__(key, val)

    def print(self):
        for k, v in vars(self).items():
            print(k, '=', v)

    # def parser_val(self, val):
    #     if isinstance(val, dict):
    #         return Config(val)
    #     elif isinstance(val, list):
    #         for i in range(len(val)):
    #             if val is not None:
    #                 val[i] = self.parser_val(val[i])
    #         return val
    #     else:
    #         return val

    def __str__(self):
        return str(vars(self))


base_config = Config({
    "project": "speaker_verification",
    "name": "VGGVox",
    "save_dir": "train_models/",
    "resume": "",

    # Training and test data
    "dataset": Config({
            "name": "voxceleb2_wav",
            "train_list": "data/train_list.txt",
            "test_list": "data/veri_list.txt",
            "train_path": "data/voxceleb2",
            "test_path": "data/voxceleb1",
            "musan_path": "data/musan_split",  # 噪声文件
            "rir_path": "data/RIRS_NOISES/simulated_rirs",  # 混响文件
        }),


    # Data loader
    "max_frames": 300,  # 训练时帧长
    "eval_frames": 300,
    "batch_size": 64,
    "max_seg_per_spk": 500,  # 每个说话人最大的语音段数
    "nDataLoaderThread": 16,  # 多线程加载
    "augment": True,  # 是否数据增强
    "seed": 10,
    "segment": 1,

    # Training details
    "test_interval": 1,  # 测试间隔
    "max_epoch": 500,

    # Model definition
    "n_mels": 40,
    "log_input": False,
    "model": "Vgg",
    "encoder_type": "SAP",
    "nOut": 512,

    # Loss functions
    "loss": "SoftmaxProto",  # lossfunction function
    "hard_prob": 0.5,
    "hard_rank": 10,
    "margin": 0.2,
    "scale": 30,
    "nPerSpeaker": 2,  # 同一段语音取多少组
    "nClasses": 5994,

    # Optimizer
    "optimizer": "adam",
    "scheduler": "steplr",
    "lr": 0.001,
    "lr_decay": 0.95,
    "weight_decay": 0,

    # Evaluation parameters
    "dcf_p_target": 0.05,
    "dcf_c_miss": 1,
    "dcf_c_fa": 1,

    # eval
    "eval": False,
})

cfg = base_config

vgg_cfg = Config({
    "name": "vgg_spectrogram",
    "model": "vgg",
    "batch_size": 64,
    "nPerSpeaker": 2,
})

Unet_cfg = Config({
    "name": "Unet",
    "model": "UNetVgg",
    "batch_size": 48,
    "nPerSpeaker": 2,
    "loss": "Unetloss"
})

UnetMask_cfg = Config({
    "name": "UnetMask",
    "model": "UNetVggMask",
    "batch_size": 16,
    "nPerSpeaker": 2,
    "segment": 3,
    "loss": "UnetMaskloss"
})

ECAPA_TDNN_cfg = Config({
    "name": "ECAPA_TDNNm",
    "model": "ECAPA_TDNN",
    "loss": "AamSoftmaxProto",
    "batch_size": 180,
    "nPerSpeaker": 2,
    "nOut": 192,
})

ECAPA_TDNNm_cfg = Config({
    "name": "ECAPA_TDNNm",
    "model": "ECAPA_TDNN",
    "batch_size": 180,
    "nPerSpeaker": 2,
    "nOut": 192,
})

ECAPA_TDNN1024_cfg = Config({
    "name": "ECAPA_TDNN1024",
    "model": "ECAPA_TDNN",
    "batch_size": 80,
    "nPerSpeaker": 2,
    "channels": 1024,
    "nOut": 192,
})

ECAPA_TDNN_ks5_cfg = Config({
    "name": "ECAPA_TDNN_ks5",
    "model": "ECAPA_TDNN_ks5",
    "batch_size": 180,
    "nPerSpeaker": 2,
    "nOut": 192,
})

ECAPA_TDNN_L2_cfg = Config({
    "name": "ECAPA_TDNN_L2_pre",
    "model": "ECAPA_TDNN_L2",
    "batch_size": 180,
    "nPerSpeaker": 2,
    "nOut": 192,
    "resume": "train_models/speaker_verification_ECAPA_TDNN/20210725/epoch:47,EER:2.5981,MinDCF:0.1912"
})

ECAPA_TDNN_br_cfg = Config({
    "name": "ECAPA_TDNN_br",
    "model": "ECAPA_TDNN_br",
    "batch_size": 180,
    "nPerSpeaker": 2,
    "nOut": 192,
})

ECAPATDNN_cfg = Config({
    "name": "ECAPATDNN",
    "model": "ECAPATDNN",
    "batch_size": 110,
    "nPerSpeaker": 2,
    "nOut": 192,
    "input_size": 80,
})

HRNet_cfg = Config({
    "name": "hrnet",
    "model": "hrnet",
    "max_frames": 224,
    "eval_frames": 224,
    "batch_size": 48,
    "nPerSpeaker": 2,
    "nOut": 1024,
    "input_size": 224*224,

    "model_cfg": Config({
        "hrnet_name": "w48",
        "STAGE1": {
            "NUM_MODULES": 1,
            "NUM_BRANCHES": 1,
            "BLOCK": "BOTTLENECK",
            "NUM_BLOCKS": [4],
            "NUM_CHANNELS": [64],
            "FUSE_METHOD": "SUM"
        },
        "STAGE2": {
            "NUM_MODULES": 1,
            "NUM_BRANCHES": 2,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [4, 4],
            "NUM_CHANNELS": [18, 36],
            "FUSE_METHOD": "SUM"
        },
        "STAGE3": {
            "NUM_MODULES": 4,
            "NUM_BRANCHES": 3,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [4, 4, 4],
            "NUM_CHANNELS": [18, 36, 72],
            "FUSE_METHOD": "SUM"
        },
        "STAGE4": {
            "NUM_MODULES": 3,
            "NUM_BRANCHES": 4,
            "BLOCK": "BASIC",
            "NUM_BLOCKS": [4, 4, 4, 4],
            "NUM_CHANNELS": [18, 36, 72, 144],
            "FUSE_METHOD": "SUM"
        },
    }),

})

VGG_TDNN_cfg = Config({
    "name": "Vggtdnn1",
    "model": "Vggtdnn",
    "batch_size": 256,
    "nOut": 512,
    "nDataLoaderThread": 16,
})

ResNetSE34V2_cfg = Config({
    "name": "ResNetSE34V2",
    "model": "ResNetSE34V2",
    "batch_size": 128,
    "nOut": 512,
    "nDataLoaderThread": 16,
})

HRTDNN_cfg = Config({
    "name": "hrtdnn",
    "model": "hrtdnn",
    "max_frames": 300,
    "eval_frames": 300,
    "batch_size": 96,
    "nPerSpeaker": 2,
    "nOut": 256,

    "model_cfg": Config({
        "hrnet_name": "hrtdnn",
        "STAGE1": {
            "NUM_BRANCHES": 1,
            "BLOCK": 'TDNNBlock',
            "NUM_CHANNELS": [128],
            "FUSE_METHOD": "SUM"
        },
        "STAGE2": {
            "NUM_BRANCHES": 2,
            "BLOCK": 'TDNNBlock',
            "NUM_CHANNELS": [128, 512],
            "FUSE_METHOD": "SUM"
        },
        "STAGE3": {
            "NUM_BRANCHES": 3,
            "BLOCK": 'TDNNBlock',
            "NUM_CHANNELS": [128, 512, 1024],
            "FUSE_METHOD": "SUM"
        },

    }),

})

ResTDNN_cfg = Config({
    "name": "ResTDNN",
    "model": "ResTDNN",
    "batch_size": 110,
    "nOut": 256,
    "nDataLoaderThread": 16,
})

TDNN_VGG_cfg = Config({
    "name": "TDNN_VGG",
    "model": "TDNN_VGG",
    "batch_size": 64,
    "nOut": 256,
    "nDataLoaderThread": 16,
})

ResNet_TDNN_cfg = Config({
    "name": "ResNet_TDNN",
    "model": "ResNet_TDNN",
    "batch_size": 96,
    "nOut": 192,
    "nDataLoaderThread": 16,
})

ResNet_TDNNa_cfg = Config({
    "name": "ResNet_TDNNa",
    "model": "ResNet_TDNN",
    "batch_size": 96,
    "nOut": 192,
    "nDataLoaderThread": 16,
})

ResNet_TDNNaam_cfg = Config({
    "name": "ResNet_TDNNaam",
    "model": "ResNet_TDNN",
    "loss": "AamSoftmaxProto",
    "margin": 0.2,
    "scale": 30,
    "batch_size": 96,
    "nOut": 192,
    "nDataLoaderThread": 16,
    "augment": True,
})

TDNN_ResNet_cfg = Config({
    "name": "TDNN_ResNet",
    "model": "TDNN_ResNet",
    "batch_size": 48,
    "nOut": 256,
    "nDataLoaderThread": 16,
})

hr_tdnn_cfg = Config({
    "name": "hr_tdnn",
    "model": "hr_tdnn",
    "batch_size": 46,
    "nOut": 192,
    "nDataLoaderThread": 16,
})


ECAPA_TDNNma_cfg = Config({
    "name": "ECAPA_TDNNma",
    "model": "ECAPA_TDNN",
    "batch_size": 180,
    "nPerSpeaker": 2,
    "nOut": 192,
    "augment": True,
})

ECAPA_TDNNaam_cfg = Config({
    "name": "ECAPA_TDNNaam",
    "model": "ECAPA_TDNN",
    "loss": "AamSoftmax",
    "batch_size": 360,
    "nPerSpeaker": 1,
    "nOut": 192,
    "augment": True,
})

ECAPA_TDNNaam1_cfg = Config({
    "name": "ECAPA_TDNNaam1",
    "model": "ECAPA_TDNN",
    "loss": "AdditiveAngularMargin",
    "batch_size": 360,
    "nPerSpeaker": 1,
    "nOut": 192,
    "augment": True,
})

ECAPA_TDNNaam2_cfg = Config({
    "name": "ECAPA_TDNNaam2",
    "model": "ECAPA_TDNN",
    "loss": "AamSoftmax",
    "margin": 0.2,
    "scale": 30,
    "batch_size": 360,
    "nPerSpeaker": 1,
    "nOut": 192,
    "augment": True,

})

ECAPA_TDNNaam3_cfg = Config({
    "name": "ECAPA_TDNNaam3",
    "model": "ECAPA_TDNN",
    "loss": "AamSoftmax",
    "margin": 0.1,
    "scale": 30,
    "batch_size": 360,
    "nPerSpeaker": 1,
    "nOut": 192,
    "augment": True,

})

ECAPA_TDNN_aamproto_cfg = Config({
    "name": "ECAPA_TDNN_aamproto",
    "model": "ECAPA_TDNN",
    "loss": "AamSoftmaxProto",
    "batch_size": 180,
    "nPerSpeaker": 2,
    "nOut": 192,
    "augment": True,
})

ECAPA_TDNN_aamproto1_cfg = Config({
    "name": "ECAPA_TDNN_aamproto1",
    "model": "ECAPA_TDNN",
    "loss": "AamSoftmaxProto",
    "margin": 0.2,
    "scale": 30,
    "batch_size": 180,
    "nPerSpeaker": 2,
    "nOut": 192,
    "augment": True,
})

ECAPA_TDNN0_cfg = Config({
    "name": "ECAPA_TDNN-1lr0.001",
    "model": "ECAPA_TDNN",
    "loss": "AamSoftmax",
    "batch_size": 360,
    "nOut": 192,
    "nPerSpeaker": 1,
    "resume": "train_models/speaker_verification_ECAPA_TDNN0/20210928/epoch:25,EER:2.4125,MinDCF:0.1537",
})

SwinTransformer_cfg = Config({
    "name": "SwinTransformer",
    "model": "SwinTransformer",
    "loss": "SoftmaxProto",
    "max_frames": 224,
    "eval_frames": 224,
    "n_mels": 224,
    "batch_size": 90,
    "nPerSpeaker": 2,
    "nOut": 192,
    "augment": True,
    "lr": 5e-5,
})

ECAPA_TDNN_aampre_cfg = Config({
    "name": "ECAPA_TDNN_aampre",
    "model": "ECAPA_TDNN",
    "loss": "AamSoftmaxProto",
    "batch_size": 180,
    "nOut": 192,
    "nPerSpeaker": 2,
    "resume": "train_models/speaker_verification_ECAPA_TDNNma/20210908/epoch:67,EER:2.3224,MinDCF:0.1658",
})

# 更换dataloader
ECAPA_TDNN_data_cfg = Config({
    "name": "ECAPA_TDNN_data",
    "model": "ECAPA_TDNN",
    "loss": "AamSoftmax",
    "batch_size": 360,
    "nPerSpeaker": 1,
    "nOut": 192,
    "augment": True,

})

# 标准的ECAPA_TDNN 学习率CyclicLR
ECAPA_TDNNaam_cyclr_cfg = Config({
    "name": "ECAPA_TDNNaam_cyclr",
    "model": "ECAPA_TDNN",
    "loss": "AamSoftmax",
    "margin": 0.2,
    "scale": 30,
    "batch_size": 360,
    "nPerSpeaker": 1,
    "nOut": 192,
    "augment": True,

})

# 跟换数据加载的ResNet_TDNN只用softmax
ResNet_TDNNaam_data_cfg = Config({
    "name": "ResNet_TDNNaam_data",
    "model": "ResNet_TDNN",
    "loss": "AamSoftmax",
    "margin": 0.2,
    "scale": 30,
    "batch_size": 192,
    "nOut": 192,
    "nDataLoaderThread": 16,
    "nPerSpeaker": 1,
    "augment": True,
})

# 更换dataloader, 和cyclical lr
ECAPA_TDNN_dataClr_cfg = Config({
    "name": "ECAPA_TDNN_dataClr",
    "model": "ECAPA_TDNN",
    "loss": "AamSoftmax",
    "batch_size": 360,
    "nPerSpeaker": 1,
    "nOut": 192,
    "augment": True,
})

def set_cfg(config_name: str):
    """ Sets the active configs. Works even if cfg is already imported! """
    global cfg
    # Note this is not just an eval because I'm lazy, but also because it can
    # be used like ssd300_config.copy({'max_size': 400}) for extreme fine-tuning
    cfg.replace(eval(config_name))