OLMoASR

OLMoASR#

OLMoASR is a series of English automatic speech recognition (ASR) models proposed in the OLMoASR: Open Models and Data for Training Robust Speech Recognition Models paper by Huong Ngo et al. from Ai2. Trained on 440K hours of weakly-supervised audio-text pairs collected from the public internet, OLMoASR demonstrates strong robustness and zero-shot capabilities. Visit the OLMoASR repository for access to data processing, training and evaluation code.

Model Details#

OLMoASR uses a Transformer-based encoder-decoder architecture and is an audio language model (LM), where there is an audio encoder and language decoder. OLMoASR has 5 different model sizes and all checkpoints are trained with English-only data. Below is a table enumerating the different model sizes and associated parameter count.

Training Data#

OLMoASR is trained on 440K hours of weakly-supervised data subsampled from OLMoASR-Mix, a filtered version of OLMoASR-Pool. OLMoASR-Mix is a collection 1M hours of audio-text pairs, curated from the 3M hours of OLMoASR-Pool.

Usage#

To perform transcription, you can run

import olmoasr

model = olmoasr.load_model("medium", inference=True)
result = model.transcribe("audio.mp3")
print(result)

Evaluation#

To perform evaluation, you can visit the OLMoASR repository for more details.

License#

This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.

BibTeX entry and citation info#

@misc{ngo2025olmoasropenmodelsdata,
      title={OLMoASR: Open Models and Data for Training Robust Speech Recognition Models}, 
      author={Huong Ngo and Matt Deitke and Martijn Bartelds and Sarah Pratt and Josh Gardner and Matt Jordan and Ludwig Schmidt},
      year={2025},
      eprint={2508.20869},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2508.20869}, 
}