| --- |
| library_name: transformers |
| language: |
| - sw |
| license: apache-2.0 |
| base_model: openai/whisper-small |
| tags: |
| - generated_from_trainer |
| datasets: |
| - google/WaxalNLP |
| metrics: |
| - wer |
| model-index: |
| - name: sauti-whisper-small-swh |
| results: |
| - task: |
| name: Automatic Speech Recognition |
| type: automatic-speech-recognition |
| dataset: |
| name: WAXAL (WaxalNLP) |
| type: google/WaxalNLP |
| args: "config: swa_tts, split: test" |
| metrics: |
| - name: WER |
| type: wer |
| value: 56.12052730696798 |
| --- |
| |
| # sauti-whisper-small-swh |
|
|
| This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the **WAXAL (WaxalNLP)** dataset (config: `swa_tts`). |
|
|
| It achieves the following results on the evaluation set: |
| - Loss: 1.3956 |
| - WER: 56.1205 |
|
|
| ## Model description |
|
|
| `openai/whisper-small` fine-tuned for Swahili ASR as part of the **Sauti** project at MsingiAI. |
|
|
| ## Intended uses & limitations |
|
|
| **Intended use:** Automatic speech recognition for Swahili. |
|
|
| **Limitations:** |
| - The training data comes from a dataset originally curated for TTS; performance may not generalize well to noisy, conversational, or code-switched audio. |
| - Whisper models have a maximum decoder target length (448 tokens). Long transcripts were truncated during preprocessing. |
|
|
| ## Training and evaluation data |
|
|
| - Dataset: `google/WaxalNLP` |
| - Config: `swa_tts` |
| - Splits: train+validation for training, test for evaluation |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| - learning_rate: 1e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: AdamW |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 200 |
| - training_steps: 1500 |
| - mixed_precision_training: fp16 (Native AMP) |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | WER | |
| |:-------------:|:-------:|:----:|:---------------:|:-------:| |
| | 1.4954 | 2.5253 | 250 | 1.4282 | 59.3974 | |
| | 1.1445 | 5.0505 | 500 | 1.2585 | 55.7721 | |
| | 0.8363 | 7.5758 | 750 | 1.2555 | 55.9981 | |
| | 0.6360 | 10.1010 | 1000 | 1.3102 | 55.7815 | |
| | 0.4057 | 12.6263 | 1250 | 1.3652 | 55.9416 | |
| | 0.3618 | 15.1515 | 1500 | 1.3956 | 56.1205 | |
| |
| ### Framework versions |
| |
| - Transformers 5.2.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 4.5.0 |
| - Tokenizers 0.22.2 |
