Create README.md

README.md

@@ -0,0 +1,58 @@
+---
+tags:
+- espnet
+- audio
+- automatic-speech-recognition
+- speech-translation
+language: multilingual
+datasets:
+- owsm_v3.1
+license: cc-by-4.0
+---
+
+## OWLS: Open Whisper-style Large-scale neural model Suite 
+
+OWLS is a suite of Whisper-style models, designed to help researchers understand the scaling properties of speech models. 
+OWLS models range from 0.25B to 18B parameters, and are trained on up to 360K hours of data.
+
+OWLS models are developed using [ESPnet](https://github.com/espnet/espnet), and support multilingual Speech Recognition and Translation.
+
+It is part of the [OWSM](https://www.wavlab.org/activities/2024/owsm/) project, which aims to develop fully open speech foundation models using publicly available data and open-source toolkits.
+
+The model in this repo has 17.64B parameters in total and is trained on 180k hours of public speech data.
+Specifically, it supports the following speech-to-text tasks:
+- Speech recognition
+- Any-to-any-language speech translation
+- Utterance-level alignment
+- Long-form transcription
+- Language identification
+
+## Use this model
+
+You can use this model in your projects with the following code:
+
+```python
+# make sure espnet is installed: pip install espnet
+from espnet2.bin.s2t_inference import Speech2Text
+
+model = Speech2Text.from_pretrained(
+  "espnet/owls_18B_180K"
+)
+
+speech, rate = soundfile.read("speech.wav")
+text, *_ = model(speech)[0]
+```
+
+
+## Citations
+
+```
+@article{chen2025owls,
+  title={OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models},
+  author={Chen, William and Tian, Jinchuan and Peng, Yifan and Yan, Brian and Yang, Chao-Han Huck and Watanabe, Shinji},
+  journal={arXiv preprint arXiv:2502.10373},
+  year={2025}
+}
+```
+
+