metadata
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: whisper-kaggle-be2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Wer
type: wer
value: 45.622489959839356
whisper-kaggle-be2
This model is a fine-tuned version of openai/whisper-small on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3317
- Wer: 45.6225
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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0777 | 3.33 | 1000 | 0.1892 | 50.8835 |
| 0.0108 | 6.67 | 2000 | 0.2534 | 48.8755 |
| 0.0015 | 10.0 | 3000 | 0.3003 | 45.9839 |
| 0.0002 | 13.33 | 4000 | 0.3317 | 45.6225 |
Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.0
- Datasets 2.14.4.dev0
- Tokenizers 0.13.3