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Add pipeline tag, library name, paper link, and usage instructions

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This 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.

Files changed (1) hide show
  1. README.md +44 -11
README.md CHANGED
@@ -1,18 +1,57 @@
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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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@@ -774,10 +813,4 @@ task: cls
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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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  ---
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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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+
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+ * **Repository:** [GitHub - Shikhar-S/OpenBEATs](https://github.com/Shikhar-S/OpenBEATs)
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+
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+ ## Installation
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+
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+ ```bash
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+ pip install openbeats
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+ ```
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+
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+ ## Usage
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+
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+ ### From Python
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+
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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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+
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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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+
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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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+
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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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+
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+ print(out["patch_embeddings"].shape) # (num_patches, 1024)
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+ ```
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+
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+ ### From the command line
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+
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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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+ ```