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whisper-large-v3-turbo-nepali

Automatic Speech Recognition
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metadata
library_name: transformers
language:
  - ne
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
  - generated_from_trainer
datasets:
  - kiranpantha/OpenSLR54-Whisper
metrics:
  - wer
model-index:
  - name: Whisper Large v3 Turbo Nepali - Kiran Pantha
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: OpenSLR54
          type: kiranpantha/OpenSLR54-Whisper
          config: default
          split: test
          args: 'config: ne, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 23.63425925925926

Whisper Large v3 Turbo Nepali - Kiran Pantha

This model is a fine-tuned version of openai/whisper-large-v3-turbo on the OpenSLR54 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1707
  • Wer: 23.6343
  • Cer: 5.4903

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: 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: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.3073 0.3597 300 0.2895 53.2870 13.5643
0.2457 0.7194 600 0.2396 45.3704 11.6816
0.166 1.0791 900 0.2062 37.9167 9.6668
0.1477 1.4388 1200 0.1949 37.4306 9.3071
0.1284 1.7986 1500 0.1680 32.6620 8.3235
0.0745 2.1583 1800 0.1706 31.1574 7.5272
0.0701 2.5180 2100 0.1661 32.0370 7.7217
0.0777 2.8777 2400 0.1599 28.6111 7.1308
0.0455 3.2374 2700 0.1723 28.7037 7.0097
0.0375 3.5971 3000 0.1579 26.9444 6.3674
0.0374 3.9568 3300 0.1639 26.8981 6.2794
0.0171 4.3165 3600 0.1711 25.3241 6.2280
0.0219 4.6763 3900 0.1638 25.0 5.9307
0.0089 5.0360 4200 0.1635 24.5139 5.7435
0.0072 5.3957 4500 0.1717 24.1898 5.5711
0.0059 5.7554 4800 0.1707 23.6343 5.4903

Framework versions

  • Transformers 4.46.3
  • Pytorch 2.5.1+cxx11.abi
  • Datasets 3.2.0
  • Tokenizers 0.20.3