whisper-small-mn / README.md
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metadata
language:
  - mn
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Small Mn - Sanchit Gandhi
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          config: mn
          split: None
          args: 'config: mn, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 46.60332022717344

Whisper Small Mn - Sanchit Gandhi

This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5062
  • Wer: 46.6033

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: 2
  • eval_batch_size: 8
  • 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: 7000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.6115 0.4975 1000 0.7317 69.4572
0.4096 0.9950 2000 0.5577 56.7770
0.2114 1.4925 3000 0.5270 52.8506
0.2126 1.9900 4000 0.4860 50.1365
0.105 2.4876 5000 0.5017 48.1542
0.0678 2.9851 6000 0.4909 47.1876
0.0294 3.4826 7000 0.5062 46.6033

Framework versions

  • Transformers 4.40.1
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.0
  • Tokenizers 0.19.1