Update README.md

README.md

@@ -52,7 +52,9 @@ print(inference(path=path))
 This model has been trained and evaluated on three datasets:
 
 - Common Voice 13
+  - The Common Voice dataset has been cleaned and divided into training, testing, and development sets. Care has been taken to ensure that the sentences in each set are unique and do not have any duplicates.
 - [Gowajee Corpus](https://github.com/ekapolc/gowajee_corpus)
+  - The Gowajee dataset has already been pre-split into training, development, and testing sets, allowing for direct utilization.
 ```
 @techreport{gowajee,
      title = {{Gowajee Corpus}},
@@ -67,10 +69,9 @@ and Penpicha Sangsa-nga and Thunyathon Anutarases and Nitchakran Chaipojjana},
 }
 ```
 - [Thai Elderly Speech](https://github.com/VISAI-DATAWOW/Thai-Elderly-Speech-dataset/releases/tag/v1.0.0)
+  - As for the Thai Elderly Speech dataset, I performed a random split.
+
 
-The Common Voice dataset has been cleaned and divided into training, testing, and development sets. Care has been taken to ensure that the sentences in each set are unique and do not have any duplicates.
-The Gowajee dataset has already been pre-split into training, development, and testing sets, allowing for direct utilization.
-As for the Thai Elderly Speech dataset, I performed a random split.
 The Character Error Rate (CER) is calculated by removing spaces in both the labels and predicted text, and then computing the CER.
 The Word Error Rate (WER) is calculated using the PythaiNLP newmm tokenizer to tokenize both the labels and predicted text, and then computing the WER.