metadata

license: apache-2.0
base_model: openai/whisper-small
tags:
  - audio
  - automatic-speech-recognition
metrics:
  - wer
widget:
  - example_title: Sample 1
    src: sample_ar_1.mp3
  - example_title: Sample 2
    src: sample_ar_2.mp3
model-index:
  - name: whisper-small-ar-v2
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_16_1
          type: common_voice_16_1
          config: ar
          split: test
          args: ar
        metrics:
          - name: Wer
            type: wer
            value: 47.726437288634024
language:
  - ar
library_name: transformers
pipeline_tag: automatic-speech-recognition
datasets:
  - mozilla-foundation/common_voice_16_1

whisper-small-ar-v2

This model is for Arabic automatic speech recognition (ASR). It is a fine-tuned version of openai/whisper-small on the Arabic portion of the mozilla-foundation/common_voice_16_1 dataset. It achieves the following results on the evaluation set:

Loss: 0.4007
Wer: 47.7264

Model description

Whisper model fine-tuned on Arabic data, following the official tutorial.

Intended uses & limitations

It is recommended to fine-tune and evaluate on your data before using it.

Training and evaluation data

Training Data: CommonVoice (v16.1) Arabic train + validation splits
Validation Data: CommonVoice (v16.1) Arabic test split

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 32
eval_batch_size: 32
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: 8000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.2742	0.82	1000	0.3790	275.2463
0.1625	1.65	2000	0.3353	228.5252
0.1002	2.47	3000	0.3311	238.8858
0.0751	3.3	4000	0.3354	158.1532
0.0601	4.12	5000	0.3576	48.9285
0.0612	4.95	6000	0.3575	47.8937
0.0383	5.77	7000	0.3819	46.9085
0.0234	6.6	8000	0.4007	47.7264

Framework versions

Transformers 4.38.1
Pytorch 2.1.0+cu118
Datasets 2.17.1
Tokenizers 0.15.2