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README.md

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 ### Dataset Summary
 
-TIE_shorts is a derived version of the [Technical Indian English (TIE)](https://github.com/raianand1991/TIE) dataset, a large-scale speech dataset (of around 8K hours) originally consisting of approximately 750 GB of content 
+TIE_shorts is a derived version of the [Technical Indian English (TIE)](https://github.com/raianand1991/TIE) dataset, a large-scale speech dataset (~ 8K hours) originally consisting of approximately 750 GB of content 
 sourced from the [NPTEL](https://nptel.ac.in/) 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 (around 70 hours) was created to facilitate efficient training and usage in speech processing tasks by providing shorter audio samples. In TIE_shorts, 
+The TIE_shorts version (~ 70 hours) 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.