WebRTC : How to apply webRTC's VAD on audio through samples obtained from WAV file

Step 1: Prepare Development Environment

First, verify that WebRTC is installed in your development environment. Since the WebRTC VAD module is implemented in C, ensure your environment supports C compilation. Python developers can utilize the webrtcvad library, which provides a Python interface to WebRTC's VAD.

Step 2: Read WAV File

Use an appropriate library to read the WAV file. For Python, leverage the wave module or the more advanced librosa library to load audio files.

For example, using the wave module:

python
import wave

# Open WAV file
with wave.open('path_to_file.wav', 'rb') as wav_file:
    sample_rate = wav_file.getframerate()
    frames = wav_file.readframes(wav_file.getnframes())

Step 3: Configure VAD

Configure the VAD. For WebRTC VAD, set the mode to a value between 0 and 3, where 0 is the least strict and 3 is the most strict.

python
import webrtcvad

vad = webrtcvad.Vad()

# Set mode to 3
vad.set_mode(3)

Step 4: Process Audio Frames

Divide the audio data into frames of 10 ms or 30 ms duration. WebRTC VAD requires strict adherence to these frame lengths. For 16 kHz sampled audio, a 10 ms frame corresponds to 160 samples.

python
frame_duration = 10  # in milliseconds
frame_length = int(sample_rate * frame_duration / 1000)  # frame length in samples

frames = [frames[i:i+frame_length] for i in range(0, len(frames), frame_length)]

# Check frame lengths
frames = [f for f in frames if len(f) == frame_length]

Step 5: Use VAD to Detect Speech

Process the audio frames. Iterate through each frame and use VAD to detect speech activity.

python
speech_frames = []
for frame in frames:
    # WebRTC VAD only accepts byte data; ensure frame data is in bytes
    is_speech = vad.is_speech(frame, sample_rate)

    if is_speech:
        speech_frames.append(frame)

Step 6: Process Detection Results

Process the detection results. Based on the speech_frames data, you can further process or analyze the detected speech segments. For instance, save these frames as a new WAV file or analyze speech features.

Application Example

Suppose a project requires automatically detecting and extracting speech segments from a collection of recordings. By leveraging the WebRTC VAD module, you can efficiently identify and isolate human voice segments within the audio, which can then be used for speech recognition or archiving purposes.

This is a basic example; specific implementations may require adjustments and optimizations, such as handling different sample rates and enhancing algorithm robustness.