whisper_finetune / README.md
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metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - audiofolder
metrics:
  - wer
model-index:
  - name: openai/whisper-small
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: audiofolder
          type: audiofolder
          config: default
          split: validation
          args: default
        metrics:
          - type: wer
            value: 55.43576297850026
            name: Wer
      - 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: en
          split: test
        metrics:
          - type: wer
            value: 85.75
            name: WER

openai/whisper-small

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

  • Loss: 1.1091
  • Wer: 55.4358

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: 8
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.7682 0.5 50 1.8436 32.0993
0.5702 1.01 100 1.1091 55.4358

Framework versions

  • Transformers 4.49.0
  • Pytorch 2.4.0+cu121
  • Datasets 3.3.2
  • Tokenizers 0.21.0