Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Arabic
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use UAEpro/whisper-small-ar-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UAEpro/whisper-small-ar-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="UAEpro/whisper-small-ar-2")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("UAEpro/whisper-small-ar-2") model = AutoModelForMultimodalLM.from_pretrained("UAEpro/whisper-small-ar-2") - Notebooks
- Google Colab
- Kaggle
Whisper Small ar - majed test
This model is a fine-tuned version of uaepro/whisper-small-ar-2 on the Common Voice 16.0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3392
- Wer: 168.2218
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 |
|---|---|---|---|---|
| 0.1459 | 0.41 | 1000 | 0.3714 | 182.4752 |
| 0.1378 | 0.82 | 2000 | 0.3486 | 177.9993 |
| 0.0738 | 1.24 | 3000 | 0.3513 | 184.2939 |
| 0.0855 | 1.65 | 4000 | 0.3392 | 168.2218 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0
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Evaluation results
- Wer on Common Voice 16.0test set self-reported168.222