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metadata
language:
  - sw
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_15_0
metrics:
  - wer
model-index:
  - name: Whisper Small Luganda
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 15.0
          type: mozilla-foundation/common_voice_15_0
          config: lg
          split: validation
          args: 'config: lu, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 42.958416092634074

Whisper Small Luganda

This model is a fine-tuned version of openai/whisper-small on the Common Voice 15.0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4483
  • Wer: 42.9584

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: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • training_steps: 4000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.8089 0.0682 500 0.8624 73.2282
0.6106 0.1364 1000 0.6437 59.8234
0.539 0.2045 1500 0.5589 51.8256
0.462 0.2727 2000 0.5167 48.5304
0.4342 0.3409 2500 0.4888 46.1205
0.4226 0.4091 3000 0.4673 44.8168
0.3951 0.4772 3500 0.4545 43.7128
0.4014 0.5454 4000 0.4483 42.9584

Framework versions

  • Transformers 4.40.0
  • Pytorch 2.2.2+cu118
  • Datasets 2.19.0
  • Tokenizers 0.19.1