BuzzASR — Nepali
A monolingual automatic speech recognition model for Nepali, fine-tuned from openai/whisper-large-v3. Part of BuzzASR, a suite of 102 language-specialized ASR models (paper: arXiv:2609.09554, Findings of EMNLP 2026).
This model uses full fine-tuning (native per-language tokenizer replacement + text multitask fine-tuning).
Results (normalized CER / WER, %)
| Test set | CER | WER | Whisper-large-v3 (zero-shot) CER |
|---|---|---|---|
| FLEURS | 13.35 | 40.06 | 25.3 |
| Common Voice 25 | 1.6 | 6.95 | 27.25 |
| Combined | 12.23 | 36.25 | 20.92 |
~1.7x CER reduction over Whisper zero-shot on the combined test set.
Usage
import torch, torchaudio
from transformers import WhisperForConditionalGeneration, WhisperProcessor
model = WhisperForConditionalGeneration.from_pretrained("BuzzASR/nepali", torch_dtype=torch.float16).to("cuda").eval()
proc = WhisperProcessor.from_pretrained("BuzzASR/nepali")
wav, sr = torchaudio.load("audio.wav") # 16 kHz mono
feats = proc(wav[0], sampling_rate=16000, return_tensors="pt").input_features.to("cuda").half()
ids = model.generate(feats, num_beams=1, no_repeat_ngram_size=3, repetition_penalty=1.2)
print(proc.batch_decode(ids, skip_special_tokens=True)[0])
The language/task prompt is baked into the generation config, so no language= argument is needed.
Training data
FLEURS + Common Voice Corpus 25.0 (Mozilla, March 2025; https://commonvoice.mozilla.org/en/datasets), capped per the paper. Text-only data from the Goldfish corpus (Chang et al., 2026).
Limitations
Monolingual (Nepali only). Evaluated on FLEURS / Common Voice test splits; other domains or dialects may differ.
Links & citation
- Paper: https://arxiv.org/abs/2609.09554 (Findings of EMNLP 2026)
- Project page: https://lemn-lab.github.io/buzz-asr/
- All models: https://huggingface.co/BuzzASR
@misc{buzzasr2026,
title = {BuzzASR: A Swarm of 100+ Monolingual Speech Recognition Models},
author = {Shivam Singh and Aditya Yadavalli and Catherine Arnett and Alex Warstadt},
year = {2026},
eprint = {2609.09554},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
note = {Findings of the Association for Computational Linguistics: EMNLP 2026},
url = {https://arxiv.org/abs/2609.09554}
}