Update README.md

README.md

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 license: apache-2.0
 ---
 # **TEN VAD**
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 ***A Low-Latency, Lightweight and High-Performance Streaming VAD***
 
 
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 The precision-recall curves comparing the performance of WebRTC VAD (pitch-based), Silero VAD, and TEN VAD are shown below. The evaluation is conducted on the precisely manually annotated TEN-VAD-TestSet. The audio files are from librispeech, gigaspeech, DNS Challenge etc. As demonstrated, TEN VAD achieves the best performance. Additionally, cross-validation experiments conducted on large internal real-world datasets demonstrate the reproducibility of these findings. The **TEN-VAD-TestSet with annotated labels** is released in directory "TEN-VAD-TestSet" of this repository.
 
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 <div style="text-align:">
   <img src="./images/PR_Curves_TEN-VAD-TestSet.png" width="800">
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 cd ./examples
 python plot_pr_curves.py
 ```
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 ### **2. Agent-Friendly:** 
 As illustrated in the figure below, TEN VAD rapidly detects speech-to-non-speech transitions, whereas Silero VAD suffers from a delay of several hundred milliseconds, resulting in increased end-to-end latency in human-agent interaction systems. In addition, as demonstrated in the 6.5s-7.0s audio segment, Silero VAD fails to identify short silent durations between adjacent speech segments.
 <div style="text-align:">
   <img src="./images/Agent-Friendly-image.png" width="800">
 </div>
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 ### **3. Lightweight:**
 We evaluated the RTF (Real-Time Factor) across five distinct platforms, each equipped with varying CPUs. TEN VAD demonstrates much lower computational complexity and smaller library size than Silero VAD.
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 ### **4. Multiple programming languages and platforms:**
 TEN VAD provides cross-platform C compatibility across five operating systems (Linux x64, Windows, macOS, Android, iOS), with Python bindings optimized for Linux x64.
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 ### **5. Supproted sampling rate and hop size:**
 TEN VAD operates on 16kHz audio input with configurable hop sizes (optimized frame configurations: 160/256 samples=10/16ms). Other sampling rates must be resampled to 16kHz.
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 ## **Installation**
 ```
 git clone https://huggingface.co/TEN-framework/ten-vad
 ```
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 ## **Quick Start**
 The project supports five major platforms with dynamic library linking.
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     <td> 1. not simulator <br> 2. not iPad </td>
   </tr>
 </table>
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 ### **Python Usage**
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 ```
 pip install -r requirements.txt
 ```
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 #### **Usage**
 Note: For usage in python, you can either use it by **git clone** or **pip**.
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 ```
 python test.py s0724-s0730.wav out.txt
 ```
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 ##### **By using pip:**
 
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 ```
 from ten_vad import TenVad
 ```
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 ### **C Usage**
 #### **Build Scripts**
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 - Run demo with sample audio s0724-s0730.wav
 - Processed results saved to out.txt
 
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 The detailed usage methods of each platform are as follows <br> 
 
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 1) cd ./examples
 2) ./build-and-deploy-linux.sh
 ```
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 ####  **2. Windows**
 ##### **Requirements**
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     - Visual Studio version (default: 2019)
 3) ./build-and-deploy-windows.bat
 ```
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 ####  **3. macOS**
 ##### **Requirements**
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   - Alternative: x86_64 (Intel)
 3) ./build-and-deploy-mac.sh
 ```
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 ####  **4. Android**
 ##### **Requirements**
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   - Toolchain: aarch64-linux-android-clang (default) or custom NDK toolchain
 4) ./build-and-deploy-android.sh
 ```
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 ####  **5. iOS**
 ##### **Requirements**
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       - Specify your Certification
 
     3.5. Build in Xcode and run demo on your device.
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 ## **Citations**
 ```
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   email = {developer@ten.ai}
 }
 ```
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 ## **License**
 This project is Apache 2.0 licensed.