Instructions to use espnet/OpenBEATS-Large-i3-as2m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ESPnet
How to use espnet/OpenBEATS-Large-i3-as2m with ESPnet:
unknown model type (must be text-to-speech or automatic-speech-recognition)
- Notebooks
- Google Colab
- Kaggle
Add pipeline tag, library name, paper link, and usage instructions
Browse filesThis PR improves the model card for `OpenBEATS-Large-i3-as2m` by:
- Adding `pipeline_tag: audio-classification` and `library_name: espnet` to the metadata.
- Linking the model to its publication: [OpenBEATs: A Fully Open-Source General-Purpose Audio Encoder](https://huggingface.co/papers/2507.14129).
- Adding a link to the official GitHub repository.
- Adding Python and CLI usage instructions from the GitHub README.
README.md
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---
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tags:
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- espnet
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- audio
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- classification
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datasets:
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- as2m
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license: cc-by-4.0
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---
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## ESPnet2 CLS model
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### `espnet/OpenBEATS-Large-i3-as2m`
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This model was trained by Shikhar Bharadwaj using as2m recipe in [espnet](https://github.com/espnet/espnet/).
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## CLS config
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doi={10.21437/Interspeech.2018-1456},
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url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
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}
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```
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---
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datasets:
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- as2m
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license: cc-by-4.0
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library_name: espnet
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pipeline_tag: audio-classification
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tags:
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- espnet
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- audio
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- classification
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---
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## ESPnet2 CLS model
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### `espnet/OpenBEATS-Large-i3-as2m`
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This model was trained by Shikhar Bharadwaj using as2m recipe in [espnet](https://github.com/espnet/espnet/). It is presented in the paper [OpenBEATs: A Fully Open-Source General-Purpose Audio Encoder](https://huggingface.co/papers/2507.14129).
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* **Repository:** [GitHub - Shikhar-S/OpenBEATs](https://github.com/Shikhar-S/OpenBEATs)
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## Installation
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```bash
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pip install openbeats
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```
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## Usage
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### From Python
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```python
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from openbeats.model import OpenBeats
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from openbeats.utils import load_audio
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# load model
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model = OpenBeats.from_pretrained("espnet/OpenBEATS-Large-i3-as2m", device="cuda")
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# from a file with any sample rate
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out = model.encode_file("audio.wav") # pass chunk_seconds=10 for long audio
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# or load the waveform in 16khz monoaural array with values in [-1,1]
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wav, sr = load_audio("audio.wav")
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# and pass it
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out = model.encode(wav, sr)
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print(out["patch_embeddings"].shape) # (num_patches, 1024)
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```
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### From the command line
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```bash
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openbeats-infer --checkpoint espnet/OpenBEATS-Large-i3-as2m \
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--audio audio.wav --out embeddings.npz
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```
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## CLS config
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doi={10.21437/Interspeech.2018-1456},
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url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
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}
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```
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