File size: 2,351 Bytes
04687c9 2f657b7 04687c9 2e9a81a 04687c9 2e9a81a 04687c9 2e9a81a 04687c9 2e9a81a 2f657b7 2e9a81a 2f657b7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 |
---
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 |