README.md

---
license: odc-by
---
**OLMoASR-Pool** is a web-scale audio-text dataset collected from the public internet, consisting of approximately **3M hours of audio** and **17M transcripts**. 

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

# Content
- The dataset contains 18,761,823 unique IDs spanning approximately 3.4M 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-Pool** is multilingual as it can contain non-English audio/transcripts. To retrieve an English-only dataset, it is critical to perform audio-text language alignment.
- After downloading the collection for training, only 3M hours of audio and 17M transcripts remains.

# Usage
1. Download from HuggingFace
    - Retrieve HF access token from [here](https://huggingface.co/settings/tokens) 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-Pool", 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-Pool", streaming=False, cache_dir=<where you want to download the IDs to>)
     ```
2. Download the audio and transcript files from ID information.
4. Preprocess the audio and transcript files. Follow the instructions at the [OLMoASR repo](https://github.com/allenai/OLMoASR_newest)


# 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](https://allenai.org/responsible-use).


# 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}, 
}
```