allenai/OLMoASR-Mix

OLMoASR-Mix is the curated version of OLMoASR-Pool, a web-scale audio-text dataset collected from the public internet. The dataset consists of approximately 1M hours of audio.

With OLMoASR-Mix from OLMoASR-Pool, we trained OLMoASR 💬🎙️, a series of English speech recognition models and observed strong generalization and robust capabilities!

Content

The dataset spans approximately 1M hours of audio. It also spans across a variety speaking styles, accents and audio setups such as news segments 📰, podcasts 🎙️, outdoors 🌳🏙️, crowds 🧑‍🤝‍🧑, speeches 🎤, commentary 🗣️, interviews 🤳 and more! OLMoASR-Mix is English-only as it has been curated for training English speech recognition models.

Usage

Download from HuggingFace Retrieve HF access token from here to gain access to the dataset. Run pip install huggingface_hub[cli] Run huggingface-cli login in your CLI and paste the HF access token to login Use the code below to access the IDs

from datasets import load_dataset
dataset = load_dataset("allenai/OLMoASR-Mix", streaming=True)
print(dataset) # features: ['id']
print(next(iter(dataset['train'])))

If you're downloading all the IDs, you can run the code below

from datasets import load_dataset
dataset = load_dataset("allenai/OLMoASR-Mix", streaming=False, cache_dir=<where you want to download the IDs to>)

Download the audio and transcript files from ID information. Preprocess the audio and transcript files. Follow the instructions at the OLMoASR repo.

Uses

The collection was used to train a speech recognition model, but it can also be used in research areas such as conversational data, audio understanding, speaker diarization, voice detection and more.

License

This dataset is licensed under ODC-BY. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.

Reference

@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}, 
}

Contact

If you have any questions regarding the dataset, please contact Huong Ngo at zoengo2002@gmail.com.