transformers tensorboard safetensors whisper automatic-speech-recognition ar-asr-leaderboard generated_from_trainer hi dataset:mozilla-foundation/common_voice_11_0 base_model:openai/whisper-small license:apache-2.0 model-index endpoints_compatible region:us
---
language:
- hi
license: apache-2.0
tags:
- ar-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
base_model: openai/whisper-small
model-index:
- name: Whisper Small Ar - AxAI
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: ar
      split: None
      args: 'config: ar, split: test[:10%]'
    metrics:
    - type: wer
      value: 116.16948508455253
      name: Wer
---

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# Whisper Small Ar - AxAI

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8021
- Wer: 116.1695

## 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      |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0331        | 4.15  | 1000 | 0.5950          | 130.9519 |
| 0.0031        | 8.3   | 2000 | 0.7200          | 114.9724 |
| 0.0006        | 12.45 | 3000 | 0.7821          | 116.3405 |
| 0.0006        | 16.6  | 4000 | 0.8021          | 116.1695 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
