Below is a response that might help you understand the current landscape and what can be done:

Hi DJ_Sures,

You're absolutely right about Vosk-it’s a popular library for local speech-to-text and can run entirely on the device. However, as you noted, local models (especially one that handles free-form dictation) tend to require significant hardware resources (e.g., 16 GB RAM or more) and large model files. This is why many localized STT implementations are used only for simple commands or require optimized models for embedded scenarios.

Windows Speech Recognition itself is a hybrid system. While it might be efficient for command-and-control scenarios (like what Cortana uses), full dictation is generally offloaded to cloud processing because the computational overhead is high. In other words, the Windows built-in STT-which works reliably for known phrases-is not ideally suited for continuous, open dictation.

For conversational dialog (like integrating with ChatGPT) in a robot project, there are a few considerations:

1. Localized STT Tradeoffs:
   Using libraries like Vosk locally can give you faster, low-latency responses without relying on the cloud. However, you’ll have to consider model size and resource demands. In many robotic platforms, a carefully chosen model (or perhaps one that’s tuned for your expected vocabulary) might work, but if your conversation spans a wide range of topics, you might be forced to use a heavier solution.

2. Dictation vs. Command Recognition:
   Dictation systems (as you mentioned) are designed to stream continuous text, which makes it tricky to define conversational turns. Without a clear start and stop, the system may struggle to segment the text into actionable commands or conversational turns. Using silence detection (or defining a pause threshold) can help, but it isn’t trivial to implement reliably for dynamic conversations.

3. Hybrid Approaches in ARC:
   Since ARC is your main control hub, one promising route is to create a custom integration within ARC using Python calls. For instance, you might:

   • Leverage a local STT library such as Vosk (with an appropriately tuned model) for quick, local command recognition.
   • Use silence or pause detection (e.g., monitoring audio signal levels) to decide when a dictation session ends.
   • Then feed the transcribed text into your ChatGPT skill (which could be directed to a local LLM if available) to maintain fluid dialogue.

4. Real-World Implementation Thoughts:
   As you’ve experienced with Piper/Whisper on your Jetson SBC, achieving a fluid, local conversation usually involves balancing between speed and the resources available. The ideal solution in ARC would be to integrate the local STT solution (perhaps with a trimmed or optimized model) into a custom robot skill. This integration could wrap Vosk (or a similar library) and include logic to detect pauses to help delineate queries from ambient dictation.

In summary:

• Local STT (using something like Vosk) is promising but resource intensive.
• Dictation systems (like Windows’ hybrid approach) are optimized for continuous speech, but they’re not fully exposed for application-by-application use in ARC.
• A custom integration using Python within ARC might let you bridge this gap-leveraging local STT for fast, low-latency control while defining clear conversation boundaries for ChatGPT interactions.

I’d be happy to help you draft out a Python script or guide you in creating a custom robot skill in ARC that implements these ideas. Would you like further details on how to begin integrating Vosk (or another local STT solution) into an ARC Python module, or do you have any other questions on this topic?