Update README.md

README.md

@@ -1,63 +1,63 @@
----
-license: apple-amlr
-language:
-- en
-metrics:
-- wer
-pipeline_tag: automatic-speech-recognition
-tags:
-- asr
-- mixture-of-experts
-- speech
-- streaming
----
-# Model Card for Model ID
-
-Omni-router Transformer is a new Mixture-of-Experts (MoE) architecture that explicitly couples routing across layers using a shared router to learn strong and specialized experts. Omni-router's routing decisions appear to form consistent temporal segments and strutured usage across model depth, suggesting meaningful coordination between layers.
-
-
-## Model Details
-
-### Model Description
-
-This ASR model is a 4-expert MoE model (total 613M with 200M activate parameters). The model is streaming which transcribes speech conditioned only on the previous speech.
-
-- **Developed by:** Apple Machine Learning Research
-- **Model type:** ASR
-- **Language(s):** English
-- **License:** apple-amlr
-
-## Uses
-
-This model is a speech recognition model.
-
-## How to Get Started with the Model
-
-Please refer to the github pages for detailed usage.
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-It is trained on the 1M English subset of Apple's internal conversational dataset named SpeechCrawl.
-
-
-## Citation
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-If you find this work useful, please cite our paper:
-```
-@article{gu2025omnirouter,
-  title={Omni-router: Sharing Routing Decisions in Sparse Mixture-of-Experts for Speech Recognition},
-  author={Gu, Zijin and Likhomanenko, Tatiana and Jaitly, Navdeep},
-  journal={arXiv preprint arXiv:2507.05724},
-  year={2025}
-}
-```
-
-## Model Card Contact
-
+---
+license: apple-amlr
+language:
+- en
+metrics:
+- wer
+pipeline_tag: automatic-speech-recognition
+tags:
+- asr
+- mixture-of-experts
+- speech
+- streaming
+---
+# Model Card for Model ID
+
+Omni-router Transformer is a new Mixture-of-Experts (MoE) architecture that explicitly couples routing across layers using a shared router to learn strong and specialized experts. Omni-router's routing decisions appear to form consistent temporal segments and strutured usage across model depth, suggesting meaningful coordination between layers.
+Please refer to the [paper](https://arxiv.org/abs/2507.05724) for details. 
+
+## Model Details
+
+### Model Description
+
+This ASR model is a 4-expert MoE model (total 613M with 200M activate parameters). The model is streaming which transcribes speech conditioned only on past and current speech.
+
+- **Developed by:** Apple Machine Learning Research
+- **Model type:** ASR
+- **Language(s):** English
+- **License:** apple-amlr
+
+## Uses
+
+This model is a speech recognition model.
+
+## How to Get Started with the Model
+
+Please refer to the [github](https://github.com/apple/ml-omni-router-moe-asr) page for detailed usage.
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+The training data is a large-scale conversational audio dataset collected from publicly accessible sources, named SpeechCrawl. Please refer to the [paper](https://arxiv.org/abs/2507.05724) for details. 
+
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+If you find this work useful, please cite our paper:
+```
+@article{gu2025omnirouter,
+  title={Omni-router: Sharing Routing Decisions in Sparse Mixture-of-Experts for Speech Recognition},
+  author={Gu, Zijin and Likhomanenko, Tatiana and Jaitly, Navdeep},
+  journal={arXiv preprint arXiv:2507.05724},
+  year={2025}
+}
+```
+
+## Model Card Contact
+
 Contact zijin@apple.com for any issues.