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birajsubedi
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Model Details
Model Provider
birajsubedi
Model Tree
openai/whisper-large-v2
Input Modalities
Output Modalities
Supported Functionality
Evaluation on the held-out test set of NepFinSpeech-403:
| Model | WER% (↓) | CER% (↓) | NumAcc% (↑) |
|---|---|---|---|
| Whisper large-v2 (zero-shot) | 129.95 | 92.32 | 0.0 |
| Whisper small (general Nepali FT) | 106.32 | 63.48 | 0.0 |
| Whisper large-v2 + LoRA (ours) | 42.58 | 16.95 | 73.9 |
Relative WER reduction of 67.2% over the zero-shot baseline, with improvements on 59/60 test utterances (sign test, p = 3.5×10⁻¹⁸). Transaction Success Rate improves from 1.67% to 33.33%.
This is a PEFT adapter and must be loaded together with the base model openai/whisper-large-v2. For usage examples and full training/inference pipeline, please refer to the SpeakPay GitHub repository.
openai/whisper-large-v2The model was evaluated on the held‑out test set of NepFinSpeech, containing 60 utterances spanning all command types and numeral ranges. Metrics reported are word error rate (WER), character error rate (CER), and Devanagari numeral accuracy (NumAcc). Full analysis (including confusion patterns and data‑efficiency sweeps) is available in the paper and in the analysis/ directory of the repository.
The LoRA adapter was fine-tuned using the training/ pipeline from the SpeakPay repository. Hyperparameters and details can be found in the repository documentation.
bibtex
@misc{subedi2026speakpay, title = {SpeakPay: Domain-Adaptive LoRA Fine-Tuning of Whisper for Low-Resource Nepali Financial Speech Recognition}, author = {Subedi, Biraj}, year = {2026}, url = {https://github.com/subedibiraj/speakpay}}@misc{subedi2026speakpay, title = {SpeakPay: Domain-Adaptive LoRA Fine-Tuning of Whisper for Low-Resource Nepali Financial Speech Recognition}, author = {Subedi, Biraj}, year = {2026}, url = {https://github.com/subedibiraj/speakpay}}