transformers tensorboard safetensors whisper automatic-speech-recognition hf-asr-leaderboard generated_from_trainer ar dataset:mozilla-foundation/common_voice_16_0 base_model:uaepro/whisper-small-ar-2 license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- ar
license: apache-2.0
base_model: uaepro/whisper-small-ar-2
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Whisper Small ar - majed test
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 16.0
      type: mozilla-foundation/common_voice_16_0
      config: ar
      split: test
      args: 'config: ar, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 168.22177271055537
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Whisper Small ar - majed test

This model is a fine-tuned version of [uaepro/whisper-small-ar-2](https://huggingface.co/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
