transformers tensorboard safetensors whisper automatic-speech-recognition whisper-event generated_from_trainer ar dataset:mozilla-foundation/common_voice_16_0 base_model:openai/whisper-base license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- ar
license: apache-2.0
base_model: openai/whisper-base
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Whisper Base Arabic
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: mozilla-foundation/common_voice_16_0 ar
      type: mozilla-foundation/common_voice_16_0
      config: ar
      split: test
      args: ar
    metrics:
    - name: Wer
      type: wer
      value: 80.47772163527792
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Base Arabic

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the mozilla-foundation/common_voice_16_0 ar dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5856
- Wer: 80.4777

## 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-07
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer      |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7392        | 1.53  | 500   | 0.8623          | 100.8133 |
| 0.5938        | 3.07  | 1000  | 0.7397          | 93.6651  |
| 0.5388        | 4.6   | 1500  | 0.6953          | 92.3005  |
| 0.4982        | 6.13  | 2000  | 0.6682          | 88.9392  |
| 0.4795        | 7.67  | 2500  | 0.6512          | 90.1524  |
| 0.4483        | 9.2   | 3000  | 0.6373          | 87.1234  |
| 0.4374        | 10.74 | 3500  | 0.6261          | 85.3144  |
| 0.4331        | 12.27 | 4000  | 0.6179          | 86.4290  |
| 0.4125        | 13.8  | 4500  | 0.6106          | 83.2865  |
| 0.3984        | 15.34 | 5000  | 0.6059          | 83.0676  |
| 0.4035        | 16.87 | 5500  | 0.6008          | 82.2165  |
| 0.3997        | 18.4  | 6000  | 0.5970          | 81.1195  |
| 0.3878        | 19.94 | 6500  | 0.5941          | 81.7153  |
| 0.3827        | 21.47 | 7000  | 0.5906          | 81.2559  |
| 0.3785        | 23.01 | 7500  | 0.5892          | 81.0506  |
| 0.372         | 24.54 | 8000  | 0.5882          | 81.4248  |
| 0.3655        | 26.07 | 8500  | 0.5865          | 81.0479  |
| 0.3697        | 27.61 | 9000  | 0.5856          | 80.4777  |
| 0.3658        | 29.14 | 9500  | 0.5849          | 80.6128  |
| 0.3539        | 30.67 | 10000 | 0.5848          | 80.6696  |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0
