Update README.md

README.md

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