Are you ready to learn how to use Python and Web Real-Time Communication (WebRTC) to perform real-time voice activity detection?

Voice Activity Detection, or VAD, is a technique used to classify audio as either voiced or unvoiced based on changes in speech audio patterns. In this article, we will show you how to implement VAD using Python and WebRTC.

Before we begin, let’s make sure we have all the necessary libraries and dependencies installed for this project. The first step is to install the Python library for WebRTC. Once we have that set up, we can move on to setting up the WebRTC connection and processing the audio data in real-time.

Getting Started with WebRTC Voice Activity Detection in Python

In order to use Python and Web Real-Time Communication (WebRTC) to perform voice activity detection, you will need to install the py-webrtcvad library.

The py-webrtcvad package/library is a Python interface to the WebRTC Voice Activity Detector from Google and is compatible with Python 2 and Python 3. This library can be used for telephony and speech recognition free of charge.

To install the py-webrtcvad library, open up your command line/terminal and run the following command:

pip install webrtcvad

This will install the latest version of the py-webrtcvad library on your machine. You can then import it in Python using a Python IDE or Python Shell by writing the following line of code:

import webrtcvad

If running this line of code doesn’t give an error, then, you’ve successfully installed and imported py-webrtcvad in Python. Note that the py-webrtcvad library is written as webrtcvad in Python.

WebRTC Voice Activity Detection in Python Example

Here’s an example of how Voice Activity Detection can be performed in Python:

# Import the py-webrtcvad library
import webrtcvad

# Initialize a vad object
vad = webrtcvad.Vad()
# Run the VAD on 10 ms of silence and 16000 sampling rate 
sample_rate = 16000
frame_duration = 10  # in ms

# Creating an audio frame of silence
frame = b'\x00\x00' * int(sample_rate * frame_duration / 1000)

# Detecting speech
print(f'Contains speech: {vad.is_speech(frame, sample_rate)}')
Contains speech: False

As you can see, the is_speech() method from webrtcvad library/package can be used to detect voice in Python. You can use this method in any of your projects to start detecting voice in recorded audio frames.

See example.py from the library’s GitHub repository for a more detailed example that will process a .wav file, find the voiced segments, and write each one as a separate .wav.

In Conclusion

Congratulations! You have learned how to use Python and Web Real-Time Communication (WebRTC) to perform voice activity detection. If you have any questions or need further assistance, please leave a comment below and we will do our best to help you out. Thank you for following along with this tutorial, and we hope you found it helpful.

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