Add pipeline tag, library name, and usage instructions

README.md

@@ -1,18 +1,49 @@
 ---
+datasets:
+- as20k
+license: cc-by-4.0
+library_name: espnet
+pipeline_tag: audio-classification
 tags:
 - espnet
 - audio
 - classification
-datasets:
-- as20k
-license: cc-by-4.0
 ---
 
 ## ESPnet2 CLS model
 
 ### `espnet/OpenBEATS-Large-i2-as20k`
 
-This model was trained by Shikhar Bharadwaj using as20k recipe in [espnet](https://github.com/espnet/espnet/).
+This model was trained by Shikhar Bharadwaj using as20k 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).
+
+- **GitHub Repository:** [Shikhar-S/OpenBEATs](https://github.com/Shikhar-S/OpenBEATs)
+- **Paper:** [OpenBEATs: A Fully Open-Source General-Purpose Audio Encoder](https://huggingface.co/papers/2507.14129)
+
+## Python Usage
+
+You can use this model via the `openbeats` library:
+
+```bash
+pip install openbeats
+```
+
+```python
+from openbeats.model import OpenBeats
+from openbeats.utils import load_audio
+
+# load model
+model = OpenBeats.from_pretrained("espnet/OpenBEATS-Large-i2-as20k", device="cuda")
+
+# from a file with any sample rate
+out = model.encode_file("audio.wav")              # pass chunk_seconds=10 for long audio
+
+# or load the waveform in 16khz monoaural array with values in [-1,1]
+wav, sr = load_audio("audio.wav")
+# and pass it
+out = model.encode(wav, sr)
+
+print(out["patch_embeddings"].shape)               # (num_patches, 1024)
+```
 
 ## CLS config
 
@@ -774,10 +805,4 @@ task: cls
   doi={10.21437/Interspeech.2018-1456},
   url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
 }
-
-
-
-
-
-
-```
+```