Establishing Data Card

README.md

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-license: mit
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+---
+license: mit
+---
+**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.
+