BuzzASR/swedish

BuzzASR — Swedish

A monolingual automatic speech recognition model for Swedish, 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).

🏆 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, %)

~1.3x 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/swedish", torch_dtype=torch.float16).to("cuda").eval()
proc  = WhisperProcessor.from_pretrained("BuzzASR/swedish")

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 (Swedish 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}
}