whisper-medium-vi / README.md
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metadata
language:
  - vi
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
base_model: openai/whisper-medium
model-index:
  - name: Whisper Medium Vietnamese
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0 vi
          type: mozilla-foundation/common_voice_11_0
          config: vi
          split: test
          args: vi
        metrics:
          - type: wer
            value: 15.492494795661225
            name: Wer
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: vi_vn
          split: test
        metrics:
          - type: wer
            value: 19.55
            name: WER

Whisper Medium Vietnamese

This model is a fine-tuned version of openai/whisper-medium on the mozilla-foundation/common_voice_11_0 vi dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7136
  • Wer: 15.4925

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: 64
  • 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: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.0001 124.0 1000 0.7136 15.4925
0.0001 249.0 2000 0.8532 17.0045
0.0 374.0 3000 0.9251 19.0972
0.0 499.0 4000 0.9787 21.5953
0.0 624.0 5000 0.9921 21.4638

Framework versions

  • Transformers 4.26.0.dev0
  • Pytorch 1.13.0
  • Datasets 2.7.1
  • Tokenizers 0.12.1