Audio Cls Hhd

--- library_name: transformers base_model: Kkonjeong/wav2vec2-base-korean tags: - generated_from_trainer metrics: - accuracy model-index: - name: audio_cls_hhd results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # audio_cls_hhd This model is a fine-tuned version of [Kkonjeong/wav2vec2-base-korean](https://huggingface.co/Kkonjeong/wav2vec2-base-korean) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9499 - Accuracy: 0.8739 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 15 | 2.5664 | 0.1597 | | No log | 2.0 | 30 | 2.3700 | 0.2269 | | No log | 3.0 | 45 | 2.2625 | 0.3361 | | No log | 4.0 | 60 | 1.8920 | 0.5966 | | No log | 5.0 | 75 | 1.6111 | 0.6975 | | No log | 6.0 | 90 | 1.3786 | 0.8067 | | No log | 7.0 | 105 | 1.2079 | 0.8319 | | No log | 8.0 | 120 | 1.0853 | 0.8403 | | No log | 9.0 | 135 | 0.9845 | 0.8571 | | No log | 10.0 | 150 | 0.9499 | 0.8739 | ### Framework versions - Transformers 4.57.3 - Pytorch 2.9.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.1

Source context: 3 downloads · 0 likes · Pipeline audio-classification · Library transformers · Repo alwaysnotavailable/audio_cls_hhd