Update README.md

README.md

@@ -20,12 +20,36 @@ widget:
 
 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/)
 
-The base model pretrained and fine-tuned on 250 hours of [VLSP ASR dataset](https://vlsp.org.vn/vlsp2020/eval/asr) on 16kHz sampled speech audio. When using the model
-make sure that your speech input is also sampled at 16Khz.
+### Model description
 
-# Usage
+[Our model](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h) was pre-trained on 13k hours of youtube (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:
+
+>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.
+
+For fine-tuning phase, wav2Vec2 is fine-tuned using Connectionist Temporal Classification (CTC), which is an algorithm that is used to train neural networks for sequence-to-sequence problems and mainly in Automatic Speech Recognition and handwriting recognition.
+
+| Model | #params | Pre-training data | Fine-tune data |
+|---|---|---|---|
+| [base]((https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h)) | 95M | 13k hours | 250 hours |
+
+In a formal ASR system, two components are required: acoustic model and language model. Here ctc-wav2vec fine-tuned model working 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. 
+
+
+### Benchmark WER result (with 4-grams LM):
+
+| [VIVOS](https://ailab.hcmus.edu.vn/vivos) | [VLSP-T1](https://vlsp.org.vn/vlsp2020/eval/asr) | [VLSP-T2](https://vlsp.org.vn/vlsp2020/eval/asr) |
+|---|---|---|
+| 6.1 | 9.1 | 40.8 |
+
+
+### Example usage
+
+When using the model make sure that your speech input is also sampled at 16Khz. Following Colab link below to use a combination of CTC-wav2vec and 4-grams LM.
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1pVBY46gSoWer2vDf0XmZ6uNV3d8lrMxx?usp=sharing)
 
-To transcribe audio files the model can be used as a standalone acoustic model as follows:
 
 ```python
 from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
@@ -58,13 +82,6 @@ logits = model(input_values).logits
 predicted_ids = torch.argmax(logits, dim=-1)
 transcription = processor.batch_decode(predicted_ids)
  ```
- 
-*Result WER (with 4-grams LM)*:
-
-| VIVOS | VLSP-T1 | VLSP-T2 |
-|---|---|---|
-| 6.1 | 9.1 | 40.8 |
-
 # 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.