metadata
library_name: transformers
language:
- ar
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: 'arabic Whisper Small '
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13.0
type: mozilla-foundation/common_voice_13_0
config: ar
split: test
args: 'config: ar, split: test'
metrics:
- name: Wer
type: wer
value: 44.37620667463455
arabic Whisper Small
This model is a fine-tuned version of openai/whisper-small on the Common Voice 13.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3384
- Wer: 44.3762
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
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.3475 | 0.4148 | 1000 | 0.4130 | 52.2957 |
| 0.2522 | 0.8295 | 2000 | 0.3674 | 49.2011 |
| 0.1608 | 1.2443 | 3000 | 0.3475 | 44.8819 |
| 0.161 | 1.6591 | 4000 | 0.3384 | 44.3762 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.3.0+cu118
- Datasets 3.0.0
- Tokenizers 0.19.1