Distilhubert Finetuned Ccm Falsetto

--- library_name: transformers license: apache-2.0 base_model: ntu-spml/distilhubert tags: - generated_from_trainer datasets: - ccmusic-database/chest_falsetto metrics: - accuracy model-index: - name: distilhubert-finetuned-ccm-falsetto results: - task: name: Audio Classification type: audio-classification dataset: name: Chest Voice and Falsetto Dataset type: ccmusic-database/chest_falsetto config: default split: test args: default metrics: - name: Accuracy type: accuracy value: 0.9066147859922179 --- <!-- 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. --> # distilhubert-finetuned-ccm-falsetto This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the Chest Voice and Falsetto Dataset dataset. It achieves the following results on the evaluation set: - Loss: 0.4966 - Accuracy: 0.9066 ## 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: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4816 | 1.0 | 96 | 0.5584 | 0.7160 | | 0.4206 | 2.0 | 192 | 0.5590 | 0.8132 | | 0.0713 | 3.0 | 288 | 0.4010 | 0.8755 | | 0.0116 | 4.0 | 384 | 0.4312 | 0.8949 | | 0.0264 | 5.0 | 480 | 0.4611 | 0.9105 | | 0.0355 | 6.0 | 576 | 0.4798 | 0.9027 | | 0.0013 | 7.0 | 672 | 0.4728 | 0.9222 | | 0.001 | 8.0 | 768 | 0.4898 | 0.9027 | | 0.0008 | 9.0 | 864 | 0.5173 | 0.8949 | | 0.0007 | 10.0 | 960 | 0.4966 | 0.9066 | ### Framework versions - Transformers 4.49.0 - Pytorch 2.8.0 - Datasets 4.4.1 - Tokenizers 0.21.0