| from transformers import Wav2Vec2Config | |
| class Wav2Vec2DualHypersphereConfig(Wav2Vec2Config): | |
| model_type = "wav2vec2-dual-hypersphere" | |
| def __init__( | |
| self, | |
| classifier_proj_size=256, | |
| final_dropout=0.1, | |
| freeze_feat_extract_train=True, | |
| **kwargs, | |
| ): | |
| # Ensure default classification metadata is set before calling super | |
| if "num_labels" not in kwargs: | |
| kwargs["num_labels"] = 2 | |
| if "id2label" not in kwargs: | |
| kwargs["id2label"] = {0: "AI Voice (Fake)", 1: "Human Voice (Real)"} | |
| if "label2id" not in kwargs: | |
| kwargs["label2id"] = {"AI Voice (Fake)": 0, "Human Voice (Real)": 1} | |
| super().__init__(**kwargs) | |
| self.classifier_proj_size = classifier_proj_size | |
| self.final_dropout = final_dropout | |
| self.freeze_feat_extract_train = freeze_feat_extract_train | |
| __all__ = ["Wav2Vec2DualHypersphereConfig"] |