Test Your AI Voice Assistant with Realistic TTS Using Piper

Introduction

In today's landscape, where AI particularly voice integration is essential for delivering exceptional customer experiences, testing voice interfaces introduces unique challenges for QA teams.

Unlike traditional UI or API testing, voice-based applications require validation across various tones, languages, and speaking styles, making it difficult to scale test coverage efficiently.

This is where Piper, a lightweight and open-source text-to-speech (TTS) model, proves invaluable. It empowers testers to generate realistic voice inputs in multiple languages, accents, and frequencies enabling comprehensive, automated testing of voice-enabled systems.

What is Piper?

• Piper is a high-quality, lightweight TTS engine. It runs locally, is fast, and supports multiple voices and languages. Best of all, it’s open-source (MIT licensed), making it suitable for development, testing, and deployment in privacy-sensitive environments.
• Piper supports over 35 languages and various voice variants—ideal for testing diverse speech scenarios (accents, languages, frequencies). Default audio output format for piper is .wav (Waveform Audio File).  

Key benefits:

• Fully local, no internet needed
• Fast inference and output
• High-quality speech output
• MIT licensed (safe for commercial use)  

Installing Piper:

• Navigate to https://github.com/rhasspy/piper/releases
• Download piper_windows_amd64.zip (You can download as per your system's configurations) 

Generating Audio for Voice Assistant Testing:

• Go to bash
• Change directory to Piper Folder
• echo "Hello, this is a test using Piper TTS." | .\piper.exe -m en_US-kathleen-low.onnx -c en_en_US_kathleen_low_en_US-kathleen-low.onnx.json -f test1.wav

Feeding Audio into the Assistant (Example with WebSocket)

with wave.open("product_A_final.wav", "rb") as wf:

    while chunk := wf.readframes(3200):

        await ws.send(chunk)

await ws.send("EOS")

Validating the Assistant’s Response:

• Was the transcript accurate?
• Did it invoke the correct tool/function?
• Was the backend action completed?

Use a test case definition and then validate programmatically via API assertions or database checks.

{

  "audio_file": "product_A_final.wav",

  "expected_transcript": "Add product to cart",

  "expected_function": "add_product",

  "expected_arguments": {"product": "product A"}

}

Real-World Use Cases where Piper can help:

• Automated regression testing for voice assistants
• Offline voice testing for edge devices
• Multilingual testing using various Piper voices
• CI/CD pipeline integration (e.g., GitHub Actions) 

Conclusion:

Piper offers a powerful and lightweight solution to simulate voice input in automated test scenarios. Combined with tools like pytest, FastAPI, and WebSocket clients, you can create robust test suites for your AI assistant workflows without relying on cloud-based TTS providers.

If you’re building or testing a voice assistant with Azure Voice Live, Dialogflow, or your own NLP stack, Piper is a tool worth integrating into your QA strategy.

If you have any questions you can reach out our SharePoint Consulting team here.