whisper-swahili-small

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whisper-swahili-small

This model is a fine-tuned version of openai/whisper-small on the Common Voice 11 dataset. It achieves the following results on the evaluation set:

• Loss: 0.3392
• Wer: 24.1789

Model description

• Developed by: iLabAfrica
• Model type: automatic-speech-recognition (ASR)
• License: Mozilla.org
• Finetuned from model: openai/whisper-small

Intended uses & limitations

The model is intended as exploration exercise to develop better ASR model for Swahili English.

The recommended audio usage for testing should be:

1. Involves local Swahili, dialect, names, and terms etc.
2. Involves Swahili accents i.e. Kenyan & Tanzanian accents.

Training and evaluation data

• Training Data: 37,000 audio samples (average 6 seconds each) from the Swahili subset of the Common Voice 11 dataset.
• Test Data: 11,000 audio samples from the same dataset

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

Training results

Limitations & Biases

The model struggles with noisy audio and Kenyan slang.

Framework versions

• Transformers 4.33.0.dev0
• Pytorch 2.0.1+cu117
• Datasets 2.14.3
• Tokenizers 0.13.3

Future Work

The model could be improved further by training on more diverse datasets and refining performance on noisy audio and slang.