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metadata
language:
  - mn
license: apache-2.0
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
base_model: zagibest/whisper-small-custom-data
datasets:
  - common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Small MN with custom data + Common voice - Zagi
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: common_voice_11_0
          type: common_voice_11_0
          config: mn
          split: None
          args: 'config: mn, split: test'
        metrics:
          - type: wer
            value: 43.3431629532547
            name: Wer

Whisper Small MN with custom data + Common voice - Zagi

This model is a fine-tuned version of zagibest/whisper-small-custom-data on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6134
  • Wer: 43.3432

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: 16
  • 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: 4000

Training results

Training Loss Epoch Step Validation Loss Wer
0.2751 1.98 500 0.4451 47.4170
0.0614 3.97 1000 0.4734 45.0579
0.0141 5.95 1500 0.5313 44.3370
0.0033 7.94 2000 0.5615 43.6490
0.0011 9.92 2500 0.5826 43.8565
0.0011 11.9 3000 0.6012 43.3705
0.0004 13.89 3500 0.6094 43.3486
0.0004 15.87 4000 0.6134 43.3432

Framework versions

  • Transformers 4.39.1
  • Pytorch 2.0.1+cu117
  • Datasets 2.18.0
  • Tokenizers 0.15.2