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language: mn
license: mit
library_name: transformers
pipeline_tag: automatic-speech-recognition
base_model: openai/whisper-large-v3
tags: [automatic-speech-recognition, whisper, mongolian, buzzasr]
datasets: [google/fleurs]
metrics: [cer, wer]
---
# BuzzASR — Mongolian
A monolingual automatic speech recognition model for **Mongolian**, fine-tuned from
[openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3). Part of **BuzzASR**,
a suite of 102 language-specialized ASR models (Findings of EMNLP 2026).
This model uses **full fine-tuning** (native per-language tokenizer replacement + text multitask fine-tuning).
> 🏆 **State-of-the-art (open-source).** On the combined FLEURS + Common Voice test set, this model
> achieves the lowest CER of every open system we compare against: Whisper-large-v3, Omnilingual 1B/7B,
> MMS, Qwen3-ASR, and Cohere Transcribe.
## Results (normalized CER / WER, %)
| Test set | CER | WER | Whisper-large-v3 (zero-shot) CER |
|---|---|---|---|
| FLEURS | 9.15 | 24.69 | 34.42 |
| Common Voice 25 | 1.00 | 2.14 | 40.14 |
| **Combined** | **5.21** | **13.34** | **38.23** |
~7.3x CER reduction over Whisper zero-shot on the combined test set.
## Usage
```python
import torch, torchaudio
from transformers import WhisperForConditionalGeneration, WhisperProcessor
model = WhisperForConditionalGeneration.from_pretrained("BuzzASR/mongolian", torch_dtype=torch.float16).to("cuda").eval()
proc = WhisperProcessor.from_pretrained("BuzzASR/mongolian")
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](https://huggingface.co/datasets/google/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 (Mongolian only). Evaluated on FLEURS / Common Voice test splits; other domains or dialects may differ.
## Citation
Project page: https://lemn-lab.github.io/buzzasr-docs/
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