metadata

language:
  - ar
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - ar-asr-leaderboard
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_16_1
metrics:
  - wer
model-index:
  - name: Whisper Medium Ar - AxAI
    results:
      - task:
          name: Automatic Speech Recognition
          type: 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:
          - name: Wer
            type: wer
            value: 100

Whisper Medium Ar - AxAI

This model is a fine-tuned version of 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