whisper-largev2-ar / README.md
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metadata
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
  - hf-asr-leaderboard
datasets:
  - arbml/mgb2
metrics:
  - wer
base_model: openai/whisper-large-v2
model-index:
  - name: Whisper Large V2 - Zaid Alyafeai
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0
          type: mozilla-foundation/common_voice_11_0
          config: ar
          split: test
          args: ar
        metrics:
          - type: wer
            value: 38.23
            name: Wer
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: ar_eg
          split: test
          args: ar
        metrics:
          - type: wer
            value: 11.6
            name: Wer

openai/whisper-large-v2

This model is a fine-tuned version of openai/whisper-large-v2 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.8091
  • Wer: 17.7875

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: 32
  • eval_batch_size: 16
  • seed: 42
  • distributed_type: multi-GPU
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 10000

Training results

Training Loss Epoch Step Validation Loss Wer
0.2528 0.2 2000 0.9370 22.1311
0.2718 0.4 4000 0.8721 24.9294
0.2745 0.6 6000 0.8770 20.5292
0.2157 0.8 8000 0.8774 18.1018
0.1729 1.0 10000 0.8091 17.7875

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.0+cu117
  • Datasets 2.7.1.dev0
  • Tokenizers 0.13.2