Update README.md

README.md

@@ -147,19 +147,51 @@ print(transcribe(AUDIO_PATH))
 ```
 
 ## Use Directly from Hugging Face Hub
-
+ 
 ```python
 from huggingface_hub import hf_hub_download
-
+ 
 repo_id = "gam30/nepali-automatic-speech-recognition"
 onnx_path = hf_hub_download(repo_id=repo_id, filename="model_ctc_quantized.onnx")
 config_path = hf_hub_download(repo_id=repo_id, filename="model_config.yaml")
 ```
-
+ 
 Then run inference with the same code above, replacing `ONNX_PATH` and `CONFIG_PATH`.
-
+ 
+---
+ 
 ## Notes
-
+ 
 - The notebook `local_onnx_asr_inference.ipynb` is the reference test workflow.
 - For better quality, use clean 16 kHz mono audio where possible.
 - Quantized model is faster/smaller; full model provides better accuracy with small margin only.
+ 
+---
+ 
+## Citation
+ 
+If you use this model in your research, project, or application, please cite it as follows:
+ 
+**APA:**
+```
+gam30. (2025). Nepali Automatic Speech Recognition (ONNX CTC) [Model]. Hugging Face. https://huggingface.co/gam30/nepali-automatic-speech-recognition
+```
+ 
+**BibTeX:**
+```bibtex
+@misc{gam30_nepali_asr,
+  author       = {gam30},
+  title        = {Nepali Automatic Speech Recognition (ONNX CTC)},
+  year         = {2025},
+  publisher    = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/gam30/nepali-automatic-speech-recognition}}
+}
+```
+ 
+> **Please note:** This model is based on the architecture from [ai4bharat/indicconformer_stt_ne_hybrid_ctc_rnnt_large](https://huggingface.co/ai4bharat/indicconformer_stt_ne_hybrid_ctc_rnnt_large). When citing, please also acknowledge the original base model authors.
+ 
+---
+ 
+## License
+ 
+If you use or redistribute this model, you must credit **[gam30/nepali-automatic-speech-recognition](https://huggingface.co/gam30/nepali-automatic-speech-recognition)** as the source as well as AI4Bharat.