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from transformers.configuration_utils import PretrainedConfig |
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class EcapaConfig(PretrainedConfig): |
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model_type = 'ecapa' |
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def __init__( |
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self, |
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n_mels=80, |
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sample_rate=16000, |
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win_length=25, |
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hop_length=10, |
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mean_norm=True, |
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std_norm=False, |
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norm_type='sentence', |
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hidden_size=192, |
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channels=[512, 512, 512, 512, 1536], |
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kernel_sizes=[5, 3, 3, 3, 1], |
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dilations=[1, 2, 3, 4, 1], |
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attention_channels=128, |
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res2net_scale=8, |
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se_channels=128, |
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global_context=True, |
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groups=[1, 1, 1, 1, 1], |
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num_classes=1251, |
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loss_fn='aam', |
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auto_map={ |
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"AutoConfig": "configuration_ecapa.EcapaConfig", |
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"AutoModel": "modeling_ecapa.EcapaModel", |
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}, |
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initializer_range=0.02, |
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**kwargs |
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): |
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self.n_mels = n_mels |
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self.sample_rate = sample_rate |
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self.win_length = win_length |
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self.hop_length = hop_length |
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self.mean_norm = mean_norm |
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self.std_norm = std_norm |
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self.norm_type = norm_type |
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self.channels = channels |
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self.kernel_sizes = kernel_sizes |
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self.attention_channels = attention_channels |
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self.dilations = dilations |
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self.res2net_scale = res2net_scale |
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self.se_channels = se_channels |
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self.global_context = global_context |
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self.groups = groups |
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self.hidden_size = hidden_size |
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self.num_classes = num_classes |
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self.loss_fn = loss_fn |
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self.auto_map = auto_map |
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self.initializer_range = initializer_range |
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super().__init__(**kwargs) |
