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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: Client
          type: mozilla-foundation/common_voice_16_1
          config: default
          split: None
          args: default
        metrics:
          - name: Wer
            type: wer
            value: 100

Whisper Medium Ar - AxAI

This model is a fine-tuned version of openai/whisper-medium on the Client dataset. It achieves the following results on the evaluation set:

  • Loss: 3.5466
  • 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-05
  • train_batch_size: 1
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 8
  • 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
0.0411 24.39 500 2.8748 100.0
0.0063 48.78 1000 3.3347 100.0
0.0017 73.17 1500 3.4076 100.0
0.0003 97.56 2000 3.4587 100.0
0.0001 121.95 2500 3.5256 100.0
0.0001 146.34 3000 3.5325 100.0
0.0001 170.73 3500 3.5419 100.0
0.0001 195.12 4000 3.5466 100.0

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.2.0+cu121
  • Datasets 2.17.1
  • Tokenizers 0.15.2