metadata

language:
  - ar
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_1
metrics:
  - wer
model-index:
  - name: Whisper Small Ar - Mhisham
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 11.1 ar
          type: mozilla-foundation/common_voice_11_1
          config: ar
          split: None
          args: 'config: ar, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 47.53466164723166

Whisper Small Ar - Mhisham

This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.1 ar dataset. It achieves the following results on the evaluation set:

Loss: 0.3242
Wer: 47.5347

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
0.2956	0.42	1000	0.3930	50.8659
0.2776	0.83	2000	0.3418	48.6604
0.1831	1.25	3000	0.3358	47.8175
0.1638	1.66	4000	0.3242	47.5347

Framework versions

Transformers 4.39.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2