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--- |
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license: mit |
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datasets: |
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- benax-rw/my_kinyarwanda_dataset |
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language: |
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- rw |
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metrics: |
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- wer |
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base_model: openai/whisper-small |
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pipeline_tag: automatic-speech-recognition |
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library_name: transformers |
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tags: |
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- kinyarwanda |
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- asr |
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- whisper |
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- low-resource |
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- fine-tuning |
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- benax-technologies |
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- transformers |
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- torchaudio |
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- speech-recognition |
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model-index: |
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- name: KinyaWhisper |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: KinyaWhisper Custom Dataset |
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type: custom |
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config: kinyarwanda |
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metrics: |
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- name: WER |
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type: wer |
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value: 51.85 |
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--- |
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## 🗣️ KinyaWhisper |
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KinyaWhisper is a fine-tuned version of OpenAI’s Whisper model for automatic speech recognition (ASR) in Kinyarwanda. It was trained on 102 manually labeled .wav files and serves as a reproducible baseline for speech recognition in low-resource, indigenous languages. |
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## 🔧 Usage |
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To run inference on your own audio files using the fine-tuned KinyaWhisper model: |
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```python |
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from transformers import WhisperProcessor, WhisperForConditionalGeneration |
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import torchaudio |
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# Load fine-tuned KinyaWhisper model and processor from Hugging Face |
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model = WhisperForConditionalGeneration.from_pretrained("benax-rw/KinyaWhisper") |
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processor = WhisperProcessor.from_pretrained("benax-rw/KinyaWhisper") |
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# Load and preprocess audio |
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waveform, sample_rate = torchaudio.load("your_audio.wav") |
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inputs = processor(waveform.squeeze(), sampling_rate=sample_rate, return_tensors="pt") |
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# Generate prediction |
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predicted_ids = model.generate(inputs["input_features"]) |
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transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] |
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print("🗣️ Transcription:", transcription) |
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``` |
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## 🏋️ Taining Details |
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• Model: openai/whisper-small |
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• Epochs: 80 |
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• Batch size: 4 |
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• Learning rate: 1e-5 |
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• Optimizer: Adam |
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• Final loss: 0.00024 |
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• WER: 51.85% |
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## ⚠️Limitations |
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The model was trained on a small dataset (102 samples). It performs best on short, clear Kinyarwanda utterances and may struggle with longer or noisy audio. This is an early-stage educational model, not yet suitable for production use. |
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## 📚 Citation |
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If you use this model, please cite: |
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```bibtex |
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@misc{baziramwabo2025kinyawhisper, |
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author = {Gabriel Baziramwabo}, |
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title = {KinyaWhisper: Fine-Tuning Whisper for Kinyarwanda ASR}, |
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year = {2025}, |
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publisher = {Hugging Face}, |
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howpublished = {\url{https://huggingface.co/benax-rw/KinyaWhisper}}, |
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note = {Version 1.0} |
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} |
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``` |
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## 📬 Contact |
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Maintained by Gabriel Baziramwabo. |
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✉️ [email protected] |
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🔗 https://benax.rw |
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