Update README.md

README.md

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-# Dataset Card for Voxpopuli
+# Dataset Card for TIE_Shorts
 
 ## Table of Contents
 - [Table of Contents](#table-of-contents)
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 ### Dataset Summary
 
-TIE_shorts is a derived version of the Technical Indian English (TIE) dataset, a large-scale speech dataset originally consisting of approximately 750 GB of content sourced from the NPTEL platform. The original TIE dataset contains around 9.8K technical lectures in English delivered by instructors from various regions across India, with each lecture averaging about 50 minutes. These lectures cover a wide range of technical subjects and capture diverse linguistic features characteristic of Indian English.
+TIE_shorts is a derived version of the Technical Indian English (TIE) dataset, a large-scale speech dataset originally consisting of approximately 750 GB of content 
+sourced from the NPTEL platform. The original TIE dataset contains around 9.8K technical lectures in English delivered by instructors from various regions across India, 
+with each lecture averaging about 50 minutes. These lectures cover a wide range of technical subjects and capture diverse linguistic features characteristic of Indian 
+English.
 
-The TIE_shorts version was created to facilitate efficient training and usage in speech processing tasks by providing shorter audio samples. In TIE_shorts, consecutive audio snippets from the original dataset were merged based on timestamps, with a condition that the final merged audio should not exceed 30 seconds in duration. This process results in 25–30 second audio clips, each accompanied by a corresponding ground-truth transcript. This approach retains the linguistic diversity of the original dataset while significantly reducing the size and complexity, making TIE_shorts ideal for Automatic Speech Recognition (ASR) and other speech-to-text applications.
+The TIE_shorts version was created to facilitate efficient training and usage in speech processing tasks by providing shorter audio samples. In TIE_shorts, 
+consecutive audio snippets from the original dataset were merged based on timestamps, with a condition that the final merged audio should not exceed 30 seconds in duration.
+This process results in 25–30 second audio clips, each accompanied by a corresponding ground-truth transcript. This approach retains the linguistic diversity of the original 
+dataset while significantly reducing the size and complexity, making TIE_shorts ideal for Automatic Speech Recognition (ASR) and other speech-to-text applications.
 
 ### Example usage