transformers tensorboard safetensors whisper automatic-speech-recognition ar-asr-leaderboard generated_from_trainer ar dataset:mozilla-foundation/common_voice_16_1 base_model:openai/whisper-medium license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- ar
license: apache-2.0
tags:
- ar-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
base_model: openai/whisper-medium
model-index:
- name: Whisper Medium Ar - AxAI
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Common Voice 16.1
      type: mozilla-foundation/common_voice_16_1
      config: default
      split: None
      args: 'config: ar, split: test[:10%]'
    metrics:
    - type: wer
      value: 100.0
      name: Wer
---

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# Whisper Medium Ar - AxAI

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4298
- Wer: 100.0

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer   |
|:-------------:|:-----:|:----:|:---------------:|:-----:|
| 0.0045        | 35.71 | 500  | 1.3081          | 100.0 |
| 0.0012        | 71.43 | 1000 | 1.4298          | 100.0 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
