whisper-large-v3_finetuned_kinyarwanda_standard_speech_v0.9

Whisper Large V3 - Kinyarwanda (Standard Speech)

Fine-tuned version of openai/whisper-large-v3 for automatic speech recognition on standard Kinyarwanda speech.

Note on language token

Whisper's supported language set does not include Kinyarwanda. This model was trained using the sw (Swahili) language token as a substitute, since it is one of the closer supported languages available. Use language="sw" at inference time — this is expected and required, not an error.

Dataset

Source data is DigitalUmuganda/Afrivoice_Kinyarwanda (CC-BY-4.0), a Kinyarwanda ASR dataset from Digital Umuganda. The training dataset used here, ElizabethMwangi/kinyarwanda_afrivoice_all_domains_v0.1, was created by extracting and concatenating 5 of its domains:

• Agriculture
• Education
• Financial
• Government
• Health

Note: scripted_education subset was excluded due to corrupted files.

Training Configuration

Results

Evaluated on held-out validation data at step 500:

WER and CER improved consistently from step 0 (WER 0.93, CER 0.34) through step 500, with both training and validation loss decreasing steadily and no signs of overfitting at this point in training.

Usage

from transformers import WhisperProcessor, WhisperForConditionalGeneration

processor = WhisperProcessor.from_pretrained("ElizabethMwangi/whisper-large-v3_finetuned_kinyarwanda_standard_speech_v1.0", language="sw", task="transcribe")
model = WhisperForConditionalGeneration.from_pretrained("ElizabethMwangi/whisper-large-v3_finetuned_kinyarwanda_standard_speech_v1.0")

Limitations

This model was trained on standard (non-impaired) Kinyarwanda speech across five domains. Performance on non-standard or impaired speech, or domains outside those listed above, has not been evaluated. Because Whisper does not natively support Kinyarwanda, this model repurposes the sw language token