I’m diving into the capabilities of the Meta Quest 3 and exploring the speech-to-text features it offers. I’ve come across two main options: hosted inference and local inference. I’m curious about the differences between the two and which one would be more effective for my use case.
Can anyone share their experiences or insights on using speech-to-text with the Meta Quest 3? What are the pros and cons of each method? Is there a specific situation where one would be better than the other?
Hosted is more accurate but needs internet; local is faster and works offline. Depends on your needs.
When I explored speech-to-text on the Meta Quest 3, I found that the choice between hosted and local inference significantly impacted the experience. Hosted inference, relying on cloud-based processing, offers higher accuracy and more robust language models but depends on a stable internet connection and can introduce latency. On the other hand, local inference processes speech directly on the device, providing faster responses and working offline, but with potentially less accuracy and limited language support. For my use, if I needed real-time and reliable transcription without internet constraints, local inference was preferable. However, for tasks requiring high accuracy and complex language understanding, I leaned towards hosted inference despite the need for a good connection. Each method has its strengths depending on whether you prioritize speed and offline functionality or accuracy and language depth.