Using WebRTC Voice Activity Detector (VAD) in Python for Telephony and Speech Recognition

Using WebRTC Voice Activity Detector (VAD) in Python for Telephony and Speech Recognition

Voice Activity Detection (VAD) is an essential feature in telephony and speech recognition systems, allowing the detection of voice in an audio stream. In this article, we will explore how to utilize the WebRTC VAD in Python using the py-webrtcvad library.

The WebRTC VAD, developed by Google, is known for its speed, modernity, and efficiency. It is one of the best available VAD implementations, and it’s also free to use.

To get started, follow these steps:

Step 1: Install the py-webrtcvad Library

The first step is to install the py-webrtcvad library. You can easily install it using the following command:

pip install webrtcvad

Step 2: Create a VAD Object

After installing the library, you need to create a VAD object in your Python code. Here’s an example:

python
import webrtcvad
vad = webrtcvad.Vad()

Step 3: Set Aggressiveness Mode (Optional)

The py-webrtcvad library allows you to set the aggressiveness mode for VAD. The aggressiveness mode determines how aggressively the VAD filters out non-speech segments. You can set the mode when creating the VAD object or later using the set_mode() method. Here’s an example:

python
vad.set_mode(1)

Step 4: Process Audio Segments

The WebRTC VAD accepts 16-bit mono PCM audio samples sampled at 8000, 16000, 32000, or 48000 Hz. To classify an audio segment as voiced or unvoiced, pass the segment to the is_speech() method of the VAD object. The segment must be 10, 20, or 30 ms in duration. Here’s an example:

python
sample_rate = 16000
frame_duration = 10 # ms
frame = b'\x00\x00' * int(sample_rate * frame_duration / 1000)
print('Contains speech:', vad.is_speech(frame, sample_rate))

Step 5: Advanced Usage

For more advanced usage of the py-webrtcvad library, refer to the example.py file provided in the official repository. This example demonstrates how to process a WAV file, identify voiced segments, and save them as separate WAV files.

Unit Testing

If you would like to run unit tests for the py-webrtcvad library, follow these steps:

pip install -e ".[dev]"
python setup.py test

Conclusion

In this article, we explored how to use the WebRTC Voice Activity Detector (VAD) in Python for telephony and speech recognition applications. We discussed the steps to install and set up the py-webrtcvad library and provided a code example for processing audio segments. With this knowledge, you can enhance the functionality of your telephony and speech recognition systems by incorporating VAD capabilities.

If you have any further questions or need assistance, feel free to reach out. Happy coding!

References:
– py-webrtcvad Repository by wiseman: https://github.com/wiseman/py-webrtcvad
– WebRTC Project: https://webrtc.org/