README.md

---
license: odc-by
---
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.