sauti-whisper-small-swh

sauti-whisper-small-swh

This model is a fine-tuned version of 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

Framework versions

• Transformers 5.2.0
• Pytorch 2.10.0+cu128
• Datasets 4.5.0
• Tokenizers 0.22.2