Voice Activity Detection

This guide demonstrates how to perform Voice Activity Detection with Phonexia Speech Platform 4. You can find a high-level description in the Voice Activity Detection article.

For testing, we'll be using two media files. You can download them together in the audio_files.zip archive.

At the end of this guide, you'll find the full Python code example that combines all the steps that will first be discussed separately. This guide should give you a comprehensive understanding on how to integrate Voice Activity Detection in your own projects.

Prerequisites

Follow the prerequisites for setup of Virtual Appliance and Python environment as described in the Task lifecycle code examples.

Run Voice Activity Detection

To run Voice Activity Detection for a single media file, you should start by sending a POST request to the /api/technology/voice-activity-detection endpoint. file is the only mandatory parameter. In Python, you can do this as follows:

import requests

VIRTUAL_APPLIANCE_ADDRESS = "http://<virtual-appliance-address>:8000"  # Replace with your address
MEDIA_FILE_BASED_ENDPOINT_URL = f"{VIRTUAL_APPLIANCE_ADDRESS}/api/technology/voice-activity-detection"

media_file = "Kathryn_Paula.wav"

with open(media_file, mode="rb") as file:
    files = {"file": file}
    start_task_response = requests.post(
        url=MEDIA_FILE_BASED_ENDPOINT_URL,
        files=files,
    )
print(start_task_response.status_code)  # Should print '202'

If the task has been successfully accepted, the 202 code will be returned together with a unique task ID in the response body. The task isn't processed immediately, but only scheduled for processing. You can check the current task status by polling for the result.

Polling

To obtain the final result, periodically query the task status until the task state changes to done, failed or rejected. The general polling procedure is described in detail in the Task lifecycle code examples.

Result for Voice Activity Detection

The result field of the task contains a list of independent results for each channel, identified by their channel_number. Each channel result contains the total speech_length, and a list of segments with timestamps in which voice activity was detected.

For our sample audio, the task result should look as follows (segments list was shortened for readability):

{
  "task": {
    "task_id": "5d31c8b4-f4d3-41c2-a5a5-6af6aee4812a",
    "state": "done"
  },
  "result": {
    "channels": [
      {
        "channel_number": 0,
        "speech_length": 114.5,
        "segments": [
          {
            "segment_type": "voice",
            "start_time": 1.53,
            "end_time": 14.1
          },
          {
            "segment_type": "voice",
            "start_time": 14.64,
            "end_time": 25.49
          },
          {
            "segment_type": "voice",
            "start_time": 25.71,
            "end_time": 38.47
          },
          {
            "segment_type": "voice",
            "start_time": 38.52,
            "end_time": 41.64
          },
          ...
        ]
      }
    ]
  }
}

Full Python code

Here is the full example on how to run the Voice Activity Detection technology. The code is slightly adjusted and wrapped into functions for better readability. Refer to the Task lifecycle code examples for a generic code template, applicable to all technologies.

import json
import requests
import time

VIRTUAL_APPLIANCE_ADDRESS = "http://<virtual-appliance-address>:8000"  # Replace with your address

MEDIA_FILE_BASED_ENDPOINT_URL = f"{VIRTUAL_APPLIANCE_ADDRESS}/api/technology/voice-activity-detection"


def poll_result(polling_url, polling_interval=5):
    """Poll the task endpoint until processing completes."""
    while True:
        polling_task_response = requests.get(polling_url)
        polling_task_response.raise_for_status()
        polling_task_response_json = polling_task_response.json()
        task_state = polling_task_response_json["task"]["state"]
        if task_state in {"done", "failed", "rejected"}:
            break
        time.sleep(polling_interval)
    return polling_task_response


def run_media_based_task(media_file, params=None, config=None):
    """Create a media-based task and wait for results."""
    if params is None:
        params = {}
    if config is None:
        config = {}

    with open(media_file, mode="rb") as file:
        files = {"file": file}
        start_task_response = requests.post(
            url=MEDIA_FILE_BASED_ENDPOINT_URL,
            files=files,
            params=params,
            data={"config": json.dumps(config)},
        )
        start_task_response.raise_for_status()
    polling_url = start_task_response.headers["Location"]
    task_result = poll_result(polling_url)
    return task_result.json()


# Run Voice Activity Detection
media_files = ["Laura_Harry_Veronika.wav", "Kathryn_Paula.wav"]

for media_file in media_files:
    print(f"Running Voice Activity Detection for file {media_file}.")
    media_file_based_task = run_media_based_task(media_file)
    media_file_based_task_result = media_file_based_task["result"]
    print(json.dumps(media_file_based_task_result, indent=2))