--- library_name: transformers language: - sw license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Swahili - Oxygene results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 config: sw split: None args: 'config: sw, split: test' metrics: - name: Wer type: wer value: 30.023909694921493 --- # Whisper Small Swahili - Oxygene This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4492 - Wer: 30.0239 ## 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: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 0.4789 | 0.4342 | 1000 | 0.5843 | 37.5957 | | 0.3985 | 0.8684 | 2000 | 0.4846 | 31.9413 | | 0.2349 | 1.3026 | 3000 | 0.4658 | 31.1056 | | 0.2273 | 1.7369 | 4000 | 0.4492 | 30.0239 | ### Framework versions - Transformers 4.48.1 - Pytorch 2.5.1+cu124 - Datasets 3.2.0 - Tokenizers 0.21.0