README.md

metadata

license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - common_voice_11_0
metrics:
  - wer
model-index:
  - name: whisper-small-swahili
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_11_0
          type: common_voice_11_0
          config: sw
          split: None
          args: sw
        metrics:
          - name: Wer
            type: wer
            value: 34.94378922684323

whisper-small-swahili

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: 0.6821
• Wer: 34.9438

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
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.3797	1.5625	1000	0.6029	40.2361
0.1129	3.125	2000	0.5886	35.5841
0.0507	4.6875	3000	0.6397	35.4404
0.0161	6.25	4000	0.6821	34.9438

Framework versions

• Transformers 4.40.0
• Pytorch 2.1.0+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1