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README.md

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 Frame-VAD Multilingual MarbleNet v2.0 is a convolutional neural network for voice activity detection (VAD) that serves as the first step for Speech Recognition and Speaker Diarization. It is a frame-based model that outputs a speech probability for each 20 millisecond frame of the input audio. The model has 91.5K parameters, making it lightweight and efficient for real-time applications. <br>
 To reduce false positive errors — cases where the model incorrectly detects speech when none is present — the model was trained with white noise and real-word noise perturbations. During training, the volume of audios was also varied. Additionally, the training data includes non-speech audio samples to help the model distinguish between speech and non-speech sounds (such as coughing, laughter, and breathing, etc.) <br>
-The model supports multiple languages, including Chinese, German, Russian, English, Spanish, and French.
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+**Key Features**
+- Lightweight model with only 91.5K parameters
+- Robust against false positive errors
+- Outputs speech probability for each 20 ms audio frame
+- Multilingual support: Chinese, German, Russian, English, Spanish, and French 
 
 This model is ready for commercial use. <br>