metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- balbus-classifier
metrics:
- accuracy
model-index:
- name: whisper-tiny-ft-balbus
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Balbus dataset
type: balbus-classifier
metrics:
- name: Accuracy
type: accuracy
value: 0.955
whisper-tiny-ft-balbus
This model is a fine-tuned version of openai/whisper-tiny on the Balbus dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.3438
- Accuracy: 0.955
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: 2
- eval_batch_size: 2
- 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_ratio: 0.1
- num_epochs: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.0028 | 1.0 | 900 | 0.5075 | 0.895 |
| 0.717 | 2.0 | 1800 | 0.5615 | 0.915 |
| 0.0009 | 3.0 | 2700 | 0.5231 | 0.905 |
| 0.0002 | 4.0 | 3600 | 0.2390 | 0.95 |
| 0.0 | 5.0 | 4500 | 0.4682 | 0.945 |
| 0.0 | 6.0 | 5400 | 0.3438 | 0.955 |
Framework versions
- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0