Update README.md

README.md

@@ -13,7 +13,7 @@ license: cc-by-nc-4.0
 
 We use wav2vec2 architecture for doing Self-Supervised learning
 
-<img src="https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/wav2vec2.png" width=50% height=50%>
+<img src="https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/wav2vec2.png" width=75% height=75%>
 
 ## Data
 
@@ -22,10 +22,12 @@ Our self-supervised model is pre-trained on a massive audio set of 13k hours of
   - Noise audio
   - Conversation
   - Multi-gender and dialects
+  
 
 ## Download
 
-We have already upload our pre-trained model to the Huggingface. 
+We have already upload our pre-trained model to the Huggingface. The base model trained 35 epochs and the large model trained 20 epochs in about 30 days using TPU V3-8.
+
  - [Based version](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vi) ~ 95M params
  - [Large version](https://huggingface.co/nguyenvulebinh/wav2vec2-large-vi) ~ 317M params
 
@@ -44,6 +46,56 @@ processor = Wav2Vec2Processor.from_pretrained(model_name)
 
 Since our model has the same architecture as the English wav2vec2 version, you can use [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how to fine-tune the model.
 
+## Finetuned version
+
+### VLSP 2020 ASR dataset
+
+Benchmark WER result on VLSP T1 testset:
+
+| | [base model](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vi-vlsp2020) | [large model](https://huggingface.co/nguyenvulebinh/wav2vec2-large-vi-vlsp2020) |
+|---|---|---|
+|without LM| 8.66  | 6.90 |
+|with 5-grams LM| 6.53 | 5.32 |
+
+Usage
+
+```python
+#pytorch
+#!pip install transformers==4.20.0
+#!pip install https://github.com/kpu/kenlm/archive/master.zip
+#!pip install pyctcdecode==0.4.0
+from transformers.file_utils import cached_path, hf_bucket_url
+from importlib.machinery import SourceFileLoader
+from transformers import Wav2Vec2ProcessorWithLM
+from IPython.lib.display import Audio
+import torchaudio
+import torch
+
+# Load model & processor
+model_name = "nguyenvulebinh/wav2vec2-base-vi-vlsp2020"
+# model_name = "nguyenvulebinh/wav2vec2-large-vi-vlsp2020"
+model = SourceFileLoader("model", cached_path(hf_bucket_url(model_name,filename="model_handling.py"))).load_module().Wav2Vec2ForCTC.from_pretrained(model_name)
+processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name)
+
+# Load an example audio (16k)
+audio, sample_rate = torchaudio.load(cached_path(hf_bucket_url(model_name, filename="t2_0000006682.wav")))
+input_data = processor.feature_extractor(audio[0], sampling_rate=16000, return_tensors='pt')
+
+# Infer
+output = model(**input_data)
+
+# Output transcript without LM
+print(processor.tokenizer.decode(output.logits.argmax(dim=-1)[0].detach().cpu().numpy()))
+
+# Output transcript with LM
+print(processor.decode(output.logits.cpu().detach().numpy()[0], beam_width=100).text)
+```
+
+## Acknowledgment
+
+- We would like to thank the Google TPU Research Cloud (TRC) program and Soonson Kwon (Google ML Ecosystem programs Lead) for their support.
+- Special thanks to my colleagues at [VietAI](https://vietai.org/) and [VAIS](https://vais.vn/) for their advice.
+
 ## Contact 
 
 [email protected] / [email protected]
@@ -51,3 +103,6 @@ [email protected] / [email protected]
 [![Follow](https://img.shields.io/twitter/follow/nguyenvulebinh?style=social)](https://twitter.com/intent/follow?screen_name=nguyenvulebinh)
 
 
+
+
+