Update README.md

README.md

@@ -22,17 +22,19 @@ The training recipe was based on wsj recipe in [espnet](https://github.com/espne
 
 <!-- Provide a longer summary of what this model is. -->
 
-This model is Hybrid CTC/Attention model with pre-trained HuBERT encoder.
+This model is a Hybrid CTC/Attention model with pre-trained HuBERT encoder.
 
-The model pre-trained on Thai-central and fine-tuned on Khummuang, Korat, and Pattani.
+The model was pre-trained on Thai-central and fine-tuned on Khummuang, Korat, and Pattani. (Experiment 3 in the paper)
+
+We provide some demo code to do inference with this model architecture on colab [here](https://colab.research.google.com/drive/1stltGdpG9OV-sCl9QgkvEXZV7fGB2Ixe?usp=sharing). (Please note that you cannot inference >4 seconds of audio with free Google colab)
+(Code is for Thai-Central. Please select the correct model accordingly.)
 
-you can demo on colab with [this link](https://colab.research.google.com/drive/1stltGdpG9OV-sCl9QgkvEXZV7fGB2Ixe?usp=sharing). (Please note that you cannot inference >4 seconds of audio with free Google colab)
 
 ## Evaluation
 
 <!-- This section describes the evaluation protocols and provides the results. -->
 
-For evaluation, the metrics are CER and WER. before WER evaluation, transcriptions were re-tokenized using newmm tokenizer in [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp)
+For evaluation, the metrics are CER and WER. Before WER evaluation, transcriptions were re-tokenized using newmm tokenizer in [PyThaiNLP](https://github.com/PyThaiNLP/pythainlp)
 
 In this reposirity, we also provide the vocabulary for building the newmm tokenizer using this script:
 
@@ -55,7 +57,7 @@ custom_tokenizer = get_tokenizer(vocab)
 tokenized_sentence_list = custom_tokenizer.word_tokenize(<your_sentence>)
 ```
 
-The CER and WER results on test set are:
+The CER and WER results on the test set are:
 
 |Micro CER|Macro CER|Survival CER|E-commerce WER|Micro WER|Macro WER|Survival WER|E-commerce WER|
 |---|---|---|---|---|---|---|---|