Update README.md

README.md

@@ -24,7 +24,7 @@ widget:
 
 [Our models](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h) are pre-trained on 13k hours of Vietnamese youtube audio (un-label data) and fine-tuned on 250 hours labeled of [VLSP ASR dataset](https://vlsp.org.vn/vlsp2020/eval/asr) on 16kHz sampled speech audio. 
 
-We use wav2vec2 architecture for the pre-trained model. Follow wav2vec2 paper:
+We use [wav2vec2 architecture](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) for the pre-trained model. Follow wav2vec2 paper:
 
 >For the first time that learning powerful representations from speech audio alone followed by fine-tuning on transcribed speech can outperform the best semi-supervised methods while being conceptually simpler.
 
@@ -36,6 +36,7 @@ For fine-tuning phase, wav2vec2 is fine-tuned using Connectionist Temporal Class
 
 In a formal ASR system, two components are required: acoustic model and language model. Here ctc-wav2vec fine-tuned model works as an acoustic model. For the language model, we provide a [4-grams model](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/blob/main/vi_lm_4grams.bin.zip) trained on 2GB of spoken text. 
 
+Detail of training and fine-tuning process, the audience can follow [fairseq github](https://github.com/pytorch/fairseq/tree/master/examples/wav2vec) and [huggingface blog](https://huggingface.co/blog/fine-tune-wav2vec2-english).
 
 ### Benchmark WER result:
 
@@ -84,11 +85,28 @@ predicted_ids = torch.argmax(logits, dim=-1)
 transcription = processor.batch_decode(predicted_ids)
  ```
 
-# License
+### Model Parameters License
 
-This model follows [CC-BY-NC-4.0](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h/raw/main/CC-BY-NC-SA-4.0.txt) license. Therefore, those compounds are freely available for academic purposes or individual research but restricted for commercial use.
+The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode
+
+### Citation
+
+[![CITE](https://zenodo.org/badge/DOI/10.5281/zenodo.5356039.svg)](https://github.com/vietai/ASR)
+
+```text
+@misc{Thai_Binh_Nguyen_wav2vec2_vi_2021,
+  author = {Thai Binh Nguyen},
+  doi = {10.5281/zenodo.5356039},
+  month = {09},
+  title = {{Vietnamese end-to-end speech recognition using wav2vec 2.0}},
+  url = {https://github.com/vietai/ASR},
+  year = {2021}
+}
+```
+**Please CITE** our repo when it is used to help produce published results or is incorporated into other software.
 
 # Contact 
 
-nguyenvulebinh@gmail.com
+nguyenvulebinh@gmail.com / binh@vietai.org
+
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