--- base_model: shhossain/whisper-tiny-bn-emo tags: - generated_from_trainer datasets: - audiofolder metrics: - accuracy model-index: - name: whisper-tiny-bn-emo2024-05-16 results: - task: name: Audio Classification type: audio-classification dataset: name: audiofolder type: audiofolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.9759879350566723 --- # whisper-tiny-bn-emo2024-05-16 This model is a fine-tuned version of [shhossain/whisper-tiny-bn-emo](https://huggingface.co/shhossain/whisper-tiny-bn-emo) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0753 - Accuracy: 0.9760 ## 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: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.1554 | 1.0 | 331 | 0.1258 | 0.9614 | | 0.096 | 2.0 | 663 | 0.0973 | 0.9693 | | 0.1093 | 3.0 | 995 | 0.0854 | 0.9737 | | 0.0903 | 4.0 | 1327 | 0.0816 | 0.9743 | | 0.0676 | 4.99 | 1655 | 0.0753 | 0.9760 | ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2