🐝 BuzzASR — A Swarm of 100+ Monolingual Speech Recognition Models

One specialist model per language. BuzzASR is a suite of 102 monolingual ASR models, each a fine-tune of Whisper-large-v3 specialized to a single language.

The work has been accepted at EMNLP 2026 · built by Lemn Lab in collaboration with EleutherAI.

The idea

Big multilingual models spread themselves thin across hundreds of languages. We asked whether a single specialist, the same size as Whisper, could beat the giant generalists. It can to a considerable extent :)

Highlights

• ✅ Beats Whisper-large-v3 on 89 of 102 languages (FLEURS)
• 🏆 Open-source state-of-the-art (beats Whisper, Omnilingual 1B/7B, MMS, Qwen3-ASR, Cohere) on 27 languages
• 📉 Cuts character error rate ~3x on average, and 3.45x across Whisper's 51 worst languages
• 🧩 Each model is the same 1.55B parameters as Whisper — about 4.5x smaller than the strongest baseline (Omni-7B)

A few standouts (combined FLEURS + Common Voice, normalized CER %)

Find your language

Browse all 102 models in the Models tab above, or go to huggingface.co/BuzzASR/<language>.

Usage

from transformers import WhisperForConditionalGeneration, WhisperProcessor
model = WhisperForConditionalGeneration.from_pretrained("BuzzASR/mongolian")
proc  = WhisperProcessor.from_pretrained("BuzzASR/mongolian")
# the language/task prompt is baked in — just call model.generate(input_features)

Links

• 📄 Paper — Findings of EMNLP 2026 (arXiv link soon)
• 🌐 Project & docs — https://lemn-lab.github.io/buzzasr-docs/