****Explore the power of local AI with Windows’ Audio Editor app sample. Discover how on-device AI models, like Silero and Whisper, enable smart trimming of audio based on theme keywords, showcasing the potential for app development with WinUI3 and WinAppSDK.-

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Unlocking Local AI Capabilities in Windows with the Audio Editor App

Building Windows applications that harness the power of on-device AI models is a complex journey. Yet, the rewards of integrating AI into your apps are immense, offering enhanced functionality and user experiences. Today, we delve into a fascinating example: the AI-empowered Audio Editor app.

Introducing Smart Trimming

The Audio Editor app showcases a remarkable feature known as “smart trimming.” This functionality allows users to upload an audio file, specify a theme keyword or phrase along with a trim duration, and receive a trimmed audio clip that highlights the most relevant segment.

The Process Simplified

Users upload an audio file, input a theme, and define a trim duration. The app then generates a new, theme-focused audio clip.

Behind the Scenes: The AI Models

Three distinct ONNX models, Silero, Whisper, and MiniLML6v2, work in tandem to enable the smart trimming feature. Each plays a critical role in processing the audio data from input to output.

Step 1: Silero Voice Activity Detection (VAD)

“We use Silero VAD to detect voice activity and cut on breaks in speaking, resulting in properly sectioned portions of speech.”

Silero VAD is the first step, preparing the audio for transcription by segmenting it into manageable chunks without disrupting the natural flow of speech.

Step 2: Whisper Tiny

“We use the Tiny version of Whisper to optimize on performance.”

Following Silero’s segmentation, Whisper Tiny takes over to transcribe the speech to text. It’s optimized for performance, albeit with some limitations, but perfectly suits the app’s needs.

Step 3: MiniLM

The final step involves using the MiniLM model, though the specifics of its role are left to the reader’s imagination, it likely serves to refine or contextualize the transcription output.

What’s Important to Know

This example not only illustrates the potential of local AI in enhancing app functionalities but also demonstrates the practical application of combining different AI models to achieve a complex task. The journey from audio upload to smart-trimmed output encapsulates the essence of modern AI capabilities in application development.

For developers intrigued by the possibilities of on-device AI, the Audio Editor app serves as a compelling case study. It underscores the importance of selecting the right models and fine-tuning them to your specific use case. Moreover, it highlights the evolving landscape of AI in application development, where the integration of intelligent features is becoming increasingly accessible.

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Utilizes Silero Voice Activity Detection for efficient audio chunking, optimizing for transcription accuracy.

Employs Whisper Tiny model for converting speech to text, specifically designed for English language processing.

Features smart trimming technology to generate audio clips focused on user-specified themes.

Built using WinUI3 and WinAppSDK, highlighting the integration of AI in Windows applications.

Provides a practical framework for developers to leverage on-device AI models in app creation.

From the Windows Blog

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