metadata
language:
- ar
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- Arbi-Houssem/Tunisian_dataset_STT-TTS1
metrics:
- wer
model-index:
- name: Whisper Tunisien
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Tunisian_dataset_STT-TTS1
type: Arbi-Houssem/Tunisian_dataset_STT-TTS1
args: 'config: ar, split: test'
metrics:
- name: Wer
type: wer
value: 99.41634241245137
Whisper Tunisien
This model is a fine-tuned version of openai/whisper-small on the Tunisian_dataset_STT-TTS1 dataset. It achieves the following results on the evaluation set:
- Loss: 2.9262
- Wer: 99.4163
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-06
- train_batch_size: 8
- eval_batch_size: 16
- 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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.565 | 10.3093 | 1000 | 2.7181 | 99.5460 |
| 0.2875 | 20.6186 | 2000 | 2.7486 | 106.3554 |
| 0.1701 | 30.9278 | 3000 | 2.8744 | 103.2425 |
| 0.1375 | 41.2371 | 4000 | 2.9262 | 99.4163 |
Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1