whisper-base-ne / README.md
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metadata
library_name: transformers
language:
  - ne
license: apache-2.0
base_model: openai/whisper-base
tags:
  - generated_from_trainer
datasets:
  - openslr/openslr
metrics:
  - wer
model-index:
  - name: Whisper Medium - Kiran Pantha
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: OpenSLR54
          type: openslr/openslr
          config: default
          split: test
          args: 'config: ne, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 43.58105012370567

Whisper Medium - Kiran Pantha

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

  • Loss: 0.2096
  • Wer: 43.5811

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: 8
  • eval_batch_size: 4
  • 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
0.5243 0.1200 500 0.4983 77.1832
0.3685 0.2399 1000 0.3600 64.9684
0.3007 0.3599 1500 0.3094 58.0592
0.2704 0.4798 2000 0.2785 54.5038
0.2529 0.5998 2500 0.2560 50.7560
0.2479 0.7198 3000 0.2407 48.4193
0.2349 0.8397 3500 0.2262 46.3850
0.211 0.9597 4000 0.2170 45.2305
0.1707 1.0797 4500 0.2132 44.6990
0.1506 1.1996 5000 0.2096 43.5811

Framework versions

  • Transformers 4.44.2
  • Pytorch 2.4.0+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1