metadata
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 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