Designing a 24/7 Voice Assistant: Platform Comparison and Recommendations

Introduction

Creating a personal voice assistant that runs continuously and responds to a wake word involves combining always-on microphone listening with speech recognition and AI responses. The goal is to design an assistant for Windows 11 that can run in the background 24/7, activate on a specific hotword, and use OpenAI’s API (e.g. GPT-4 or ChatGPT) to handle voice-based interaction. This report compares three implementation approaches – a browser extension, a native Windows application, and a web app – to determine which provides the most reliable continuous microphone access, wake-word detection, and integration with the OpenAI API. We also consider the practical limitations of using Replit for real-time audio processing. Finally, we recommend specific tools and frameworks (Porcupine, Web Speech API, Whisper, etc.) to simplify development.

Key Requirements for a Personal Voice Assistant

Any solution needs to fulfill several key requirements based on the scenario:

• Always-Running Service: The assistant should run continuously (or at least be readily available) without user intervention, effectively acting as an always-on background service.
• Wake Word Activation: It must remain “listening” passively and only fully activate when a designated wake word (hotword) is detected (e.g. “Hey Jarvis”). This avoids constantly processing or responding to every sound.
• OpenAI API Integration: Once activated, the assistant should capture the user’s speech, convert it to text, and send it to an OpenAI model (like GPT-3.5/4) for processing. The answer can then be delivered back to the user (preferably as speech).
• Modes of Operation: It should support at least three modes – (1) an always-on mode where it responds without a wake word (for hands-free continuous interaction), (2) a wake-word-only mode where it listens but only responds when triggered by the hotword, and (3) an off/muted mode where the microphone is not actively processed at all.
• Personal Use Optimization: Since this is for personal use (not a commercial product), ease of development and reliability is prioritized over multi-user deployment. The solution can be custom-tailored to a single Windows 11 machine.
• Development/Hosting Environment: The user is considering coding or hosting on Replit, so the solution should either run locally on Windows or be able to be prototyped on Replit. We must account for Replit’s constraints, especially for an application that needs real-time audio and 24/7 uptime.

With these requirements in mind, we explore three potential platforms and assess how well they meet the needs of a 24/7 voice assistant.

Platform Options for Implementation

1. Browser Extension (e.g. Chrome/Edge Extension)

A browser extension could embed the voice assistant inside a web browser environment. It would run as an extension background script or content script, possibly with an icon or minimal UI. The extension could leverage web APIs for speech. Modern browsers provide the Web Speech API for speech recognition (STT) and speech synthesis (TTS), which can be used in an extension context. In principle, an extension can listen for a wake word by continuously running speech recognition or a custom wake word engine compiled to WebAssembly.

Pros: A browser extension is relatively easy to distribute (though in this case it’s just for one user). It can use the browser’s built-in speech recognition engines and microphones. An extension can run in the background as long as the browser is open. With technologies like Picovoice’s WebAssembly SDK, it’s possible to do on-device wake word detection in the browser (A Voice AI Browser Extension for Hands-free Web - Picovoice) (A Voice AI Browser Extension for Hands-free Web - Picovoice). In fact, Picovoice demonstrated a Chrome extension that runs a wake word detector (Porcupine) in-browser to trigger voice actions (A Voice AI Browser Extension for Hands-free Web - Picovoice). This shows that a dedicated wake word engine can be embedded in an extension, making an always-listening web solution feasible without sending all audio to the cloud. As their demo notes, “a dedicated wake word engine is the only feasible way to achieve always-listening behaviour since it’s impractical — and a privacy nightmare — to continuously have a hot mic open to a cloud API” (A Voice AI Browser Extension for Hands-free Web - Picovoice). Using an on-device hotword like this, the extension could remain idle until the user says the trigger phrase, then activate and use browser STT or forward audio to OpenAI. Browser extensions can also make network requests (e.g. fetch calls to the OpenAI API) if permissions are configured, enabling integration with OpenAI’s services.

Cons: The biggest limitation is that the browser must be running for the assistant to work – if the user closes the browser, the voice assistant stops. There may also be browser security limitations around continuous microphone access. Browsers typically require user permission to use the microphone, and continuous listening might be subject to time limits or the need to restart the recognition periodically. For example, Chrome’s built-in SpeechRecognition (Web Speech API) tends to automatically stop after a short period (around 60 seconds) for security, requiring it to be restarted for truly continuous use (javascript - Continuous Speech Recognition on browser like "ok google" or "hey siri" - Stack Overflow). Developers have found it necessary to hack around this by restarting the recognition in the onspeechend or onend event, but that’s not ideal (javascript - Continuous Speech Recognition on browser like "ok google" or "hey siri" - Stack Overflow). In practice, using a proper wake word engine (like Porcupine in WASM) is preferred over relying on continuous cloud speech recognition in the browser. Another challenge is integration with OpenAI’s API from a client-side extension: calling the OpenAI API directly from JavaScript might run into CORS restrictions (OpenAI’s API is not fully open to arbitrary browser origins by default) and would expose the API key in the extension code (Error when fetching an OpenAI location URL - Stack Overflow). This means extra care is needed (the extension could use a background script to hide the key or call a proxy). Additionally, an extension running heavy audio processing might consume CPU or battery (if on a laptop), though efficient engines like Porcupine are designed for low footprint.

Use Case: A browser extension could work if you plan to always have a browser open on that PC and want a quick way to integrate voice commands into web activities. It’s a plausible approach, but not the most robust for a dedicated 24/7 assistant on Windows because of the dependency on the browser’s uptime and the quirks of continuous listening in a browser.

2. Native Windows Application (Desktop App or Background Script)

A native application refers to writing a program that runs directly on Windows 11, outside of a browser. This could be a Python script, a .NET application (C#), or any executable that can capture microphone input, perform wake word detection, and call the OpenAI API. For example, many hobbyists build voice assistants in Python on their PC using libraries for speech and hotword detection. This app could be configured to launch on startup and run minimized (possibly in the system tray) to provide a truly always-on assistant at the OS level.

Pros: A native app offers the most control over hardware and background operation. It can use Windows APIs or cross-platform libraries to continuously capture audio from the microphone with no arbitrary time limits. There are several libraries available that make this easier. For wake word detection, a popular choice is Picovoice Porcupine, which provides offline hotword detection with high accuracy and minimal resource use. Porcupine is explicitly designed for “always-listening voice-enabled applications” and is cross-platform (supports Windows x86_64 among many other platforms) (GitHub - Picovoice/porcupine: On-device wake word detection powered by deep learning). Using such an engine, the native app can listen for the wake word locally (no internet needed until activation). Porcupine (and similar engines like Snowboy or Mycroft Precise) can run 24/7 and have very low false-alarm rates (Porcupine claims over 97% detection rate with <1 false trigger per 10 hours in real-world noise (FAQ | Porcupine Wake Word Detection Engine - Picovoice Docs)). Once the wake word is detected, the app can switch to full speech recognition mode. Here, the developer has flexibility: they could use an offline speech-to-text like OpenAI’s Whisper model or an online API. Whisper is an advanced ASR model introduced by OpenAI that achieves highly accurate transcriptions even with noise and accents (A Complete Guide to Using Whisper ASR: From Installation to Implementation) (A Complete Guide to Using Whisper ASR: From Installation to Implementation). The open-source Whisper model can be run locally (if the PC has enough performance) or one could use OpenAI’s Whisper API to transcribe the audio. Alternatively, Microsoft’s Azure Speech SDK or Google’s STT could be used – but for personal use, Whisper’s accuracy and OpenAI integration might make it a top choice. The native app can then take the transcribed text and send it to the OpenAI completion/chat API (GPT-3.5/4) using a simple HTTP request (e.g. Python requests or the OpenAI Python SDK). Since this is server-to-server communication, there are no CORS issues or exposure of API keys – the key stays on your machine in the app’s config. The response from OpenAI can be turned into voice output using text-to-speech. On Windows, one could use the built-in TTS engines or a library like pyttsx3 to speak with a system voice. The entire pipeline runs locally except for the OpenAI API call, which suits personal use. This approach has been proven viable by community projects – for instance, one developer created a Raspberry Pi voice assistant called “DaVinci” that uses Picovoice Porcupine for wake word and speech-to-text, and GPT-3.5 for the AI responses (Meet DaVinci – The ChatGPT Virtual Assistant #piday #raspberrypi #ChatGPT « Adafruit Industries – Makers, hackers, artists, designers and engineers!). The result was an offline-capable, always-listening assistant in ~300 lines of Python (Meet DaVinci – The ChatGPT Virtual Assistant #piday #raspberrypi #ChatGPT « Adafruit Industries – Makers, hackers, artists, designers and engineers!). A Windows PC is far more powerful than a Pi, so a similar or even more advanced setup is very achievable.

Cons: Developing a native app might require more familiarity with system programming or managing audio streams. If using Python, capturing audio might involve using libraries like pyaudio or sounddevice, and ensuring your microphone drivers are accessible. Packaging the app (if you want it to run at startup or as a service) adds complexity – though for personal use you might simply run a script manually or via a scheduled task. Another consideration is resource usage: running an always-on listener will use some CPU constantly. Wake word engines are typically optimized for this (Porcupine is lightweight and can run even on microcontrollers (GitHub - Picovoice/porcupine: On-device wake word detection powered by deep learning)). But if you choose a heavy approach (like continuously running Whisper on a live audio stream to detect a hotword in text), that would be very CPU intensive – which is why a small wake-word engine is recommended as a front-end. In terms of integration, a native app has virtually no limitations aside from what you code – it can call any API, use any library. For Windows specifically, one could even integrate with system features (for example, use Windows Voice Activation if available, though Cortana’s SDK is deprecated on Windows 11). Overall, the native route gives the best performance and true background capability, at the expense of a bit more initial setup effort.

3. Web Application (Browser-Based App or PWA)

Another option is to create a web application (served from a local or remote server) that the user would open in a browser to use the voice assistant. This could be a simple webpage with a JavaScript that accesses the microphone and processes audio. If hosted on Replit (or any cloud), the web app would involve a client (in the user’s browser) and a backend (on Replit or another server) to interface with OpenAI. The web app could potentially be made into a Progressive Web App (PWA) on Windows 11, which the user can “install” and run more like a desktop app (even getting an icon and maybe running in background with some service worker capabilities).

Pros: A web app is cross-platform by nature and easy to code with standard web technologies. It could leverage the Web Speech API for speech recognition in the browser, which uses cloud-powered recognition (e.g. Chrome uses Google’s STT under the hood) with minimal coding. Using Web Speech API is as simple as a few JavaScript commands and doesn’t require you to manage audio buffers or models yourself (A Voice AI Browser Extension for Hands-free Web - Picovoice). The Web Speech API also provides continuous listening and interim results, which can be useful for real-time feedback. For wake word detection, you could incorporate a WebAssembly version of a wake-word engine (Picovoice provides a Porcupine Web SDK that works in browsers including Chrome, Firefox, Edge, etc. (A Voice AI Browser Extension for Hands-free Web - Picovoice)). In fact, Picovoice’s example extension is essentially a specialized web app running in the extension context. That same technology can be used in a normal web page as long as you have microphone access. If one wanted to avoid WebAssembly, an alternative is to use the Web Speech API in continuous mode and just check the transcribed text for the wake word – but as mentioned, continuous mode tends to stop periodically and would need restarting, which can cause unreliable gaps (javascript - Continuous Speech Recognition on browser like "ok google" or "hey siri" - Stack Overflow). Thus, using a dedicated wake word WASM (to listen for “Hey X”) and only then engaging full speech recognition is the preferred architecture even in a web app. Once audio is captured and transcribed, the web app can send the text to OpenAI’s API. Usually, this would be done via an AJAX/fetch call to a backend service because of security: it’s not recommended to expose your API key in client-side code or to rely on the browser for the API call (Error when fetching an OpenAI location URL - Stack Overflow). One could write a lightweight backend (in Node, Python, etc., potentially hosted on Replit) that receives the text and your stored API key, calls OpenAI, and returns the result. The web frontend can then speak the response using the browser’s text-to-speech (Speech Synthesis API) or by playing an audio stream from the backend if using a custom voice. Windows 11’s Edge browser (or Chrome) will indicate the microphone is in use (an icon in the system tray) when the web app is listening, which is expected.

Cons: Similar to the extension, a pure web app requires an open browser/page to function. If it’s just a normal web page, you need to keep that tab running. Some browsers might suspend background tabs after a while to save resources, which could interfere with continuous listening. A PWA might alleviate this by running more like a foreground app, but it will still be constrained by browser rules for media. Continuous microphone access in a browser has inherent limitations: the Web Speech API in Chrome is known to stop listening after ~60 seconds of silence or continuous speech, requiring reactivation (javascript - Continuous Speech Recognition on browser like "ok google" or "hey siri" - Stack Overflow). While one can automatically restart it, there might be brief moments where the wake word could be missed during the restart. There’s also the matter of latency – using a web app introduces a bit more latency in audio processing (the Picovoice team noted that browsers impose a 100-300ms buffering on audio streams (Replit — Introducing system audio)). This latency is small (a few tenths of a second) but could be noticeable in fast interactions. Another concern is the reliability of 24/7 operation: if hosted on Replit, the server might not be truly always-on unless you use their paid hosting or keep it active. Replit’s free instances often “sleep” after a period of no web requests, which is problematic for an assistant meant to be always listening. By design, “Always On” functionality for Replit was deprecated in 2024 in favor of their new deployment system (Replit — Changes to Hosting on Replit). So a Replit-hosted app would require either a paid plan or an external service to ping it regularly (a hacky solution) to keep it alive. Finally, implementing the three modes (always-on vs wake-word vs muted) in a web UI might be a bit clunky – you’d need some toggle in the interface that the user clicks to switch modes, whereas a native app or extension could tie that to a keyboard shortcut or physical button.

Use Case: A web app could be useful if you want the convenience of writing code on Replit or similar and accessing it from anywhere. For instance, you could run the assistant on a server and theoretically talk to it from any device’s browser. However, for a single PC personal assistant, a web app adds unnecessary complexity (client-server overhead) compared to a local program. It’s an option if one is uncomfortable installing software on the PC and prefers everything in-browser, but not the optimal route for performance or always-on reliability.

Continuous Microphone Access Considerations

Continuous microphone access is critical for an always-listening assistant. Here’s how the platforms compare on this aspect:

• Browser Extension/Web App: Both of these run in a browser and use similar mechanics for mic access. Browsers require a user gesture or permission to start using the mic. Once granted, the Web Speech API can provide continuous results, but as noted, it often terminates after a fixed duration (around 60 seconds) due to browser policies (javascript - Continuous Speech Recognition on browser like "ok google" or "hey siri" - Stack Overflow). The developer can immediately restart it in a loop, but it’s not truly gapless. Alternatively, using a lower-level approach: one can capture raw audio via the Web Audio API and process it in real-time (e.g., feed to a WebAssembly wake word model). This is how Porcupine’s web SDK operates – it listens to raw audio frames in the browser for the wake word continuously (A Voice AI Browser Extension for Hands-free Web - Picovoice). This method can run indefinitely in an active tab or extension. The key is that the heavy lifting is done locally in the browser, and no continuous stream is sent to a server until a trigger occurs (which is good for privacy and bandwidth). A drawback is that the browser will show a persistent “microphone in use” indicator, and the page or extension must remain running. If the user navigates away or the extension’s background gets suspended (in rare cases of low memory), the listening stops. In summary, browsers can handle continuous mic access with the right tricks, but it’s not as straightforward as a native app, and slight interruptions or permissions re-prompts may occur.

• Native Windows App: This approach typically uses OS APIs or libraries to open the microphone and capture audio in a loop. There is no inherent time limit – the program can keep reading audio buffers forever. It’s essentially how applications like Discord or Zoom keep an open mic. The only limitation is system resources and making sure to handle errors (like the mic being disabled or used by another app). Windows 11 allows multiple apps to use the microphone, though if two apps actively listen, they both get the same feed. A well-designed native assistant can run at startup and keep a low-profile (perhaps just an icon indicating it’s listening). Thus, for uninterrupted 24/7 listening, native apps have an advantage as they are not subject to browser sandbox restrictions. They also can utilize audio processing libraries for noise suppression or voice activity detection to improve reliability. For instance, one could use a Voice Activity Detection (VAD) algorithm to only process audio when speech (voice) is present, reducing CPU usage. Tools like Picovoice’s Cobra VAD or WebRTC’s VAD can be integrated. The DaVinci assistant example on Raspberry Pi uses Picovoice’s VAD in combination with the wake word, ensuring that the system is truly idle (not sending anything to AI) until someone speaks (Meet DaVinci – The ChatGPT Virtual Assistant #piday #raspberrypi #ChatGPT « Adafruit Industries – Makers, hackers, artists, designers and engineers!).

In terms of which offers the best support for continuous mic access, the native application is the clear winner for reliability and true always-on capability. The browser-based options can work, but require navigating around intentional browser limits. A dedicated app has no such limits and can be more robust for continuous use.

Wake Word Detection and Reliability

Wake word detection is a cornerstone of any always-listening assistant – it lets the system remain mostly passive until needed. A good wake word system needs to be accurate (high detection rate) and avoid false positives (not triggering on similar-sounding phrases or background tv chatter). It should also run with low latency and low resource usage since it’s always running. Let’s compare approaches and tools:

• In Browser (Extension/Web): You have two main choices for wake words here: use the Web Speech recognition to spot a keyword in the transcribed text, or use an on-device model in WASM. Relying on text recognition alone (“spotting”) is not very reliable – the STT might mis-transcribe or there is lag, and it means you’re actually doing full speech recognition on everything (which is not efficient). The recommended method is to use a dedicated wake word engine that runs locally (A Voice AI Browser Extension for Hands-free Web - Picovoice). Picovoice’s Porcupine is one such engine that has a WebAssembly build (A Voice AI Browser Extension for Hands-free Web - Picovoice). The blog post from Picovoice explicitly states that a wake word engine is the feasible way to do this in browser because continuously streaming to cloud STT is impractical (A Voice AI Browser Extension for Hands-free Web - Picovoice). Porcupine can be configured with built-in keywords (they provide common ones like “Hey Pico” or “Alexa” etc., or even custom ones via their console). Using Porcupine Web SDK in an extension or web app, you can reliably detect a wake phrase offline in ~20ms segments of audio with <1 false alarm in 10 hours (FAQ | Porcupine Wake Word Detection Engine - Picovoice Docs). Open-source alternative projects for wake words in browser are not as established, but one could compile other engines to WASM if needed. Once the wake word is detected, the web app/extension would then invoke the full speech recognition to hear the user’s command after the hotword. This approach mimics how Alexa works (a small on-device model detects “Alexa”, then the following speech is sent to the cloud). In summary, reliable wake word detection is achievable in browser using libraries like Porcupine. The only caution is that running a WASM model continuously will use some CPU in that tab or extension – but Porcupine is designed to be efficient (made for even microcontrollers), so it should be fine on a modern PC (GitHub - Picovoice/porcupine: On-device wake word detection powered by deep learning).

• Native App: On Windows (or any OS), developers have even more options. Porcupine is available as a native SDK (Python, C#, C++, etc.), and similarly Snowboy (an older yet popular hotword engine, now discontinued but still usable offline) or Mycroft Precise (an open-source wake word model from the Mycroft assistant project) could be used. Porcupine tends to be the go-to for ease of use and accuracy; it supports custom wake word training and works on Windows as well (GitHub - Picovoice/porcupine: On-device wake word detection powered by deep learning). According to its documentation, Porcupine can detect multiple wake words simultaneously with negligible added overhead (GitHub - Picovoice/porcupine: On-device wake word detection powered by deep learning) (for example, you could have it respond to either “Hey Computer” or “Hey Assistant” if you wanted). In a native app, implementing Porcupine is straightforward – e.g., in Python via the pvporcupine library, you load the model and keywords and then feed it audio frames from the microphone in a loop. The detection is instant when the phrase is spoken. There are also platform-specific possibilities: Windows 11’s built-in Voice Access feature (for accessibility) uses its own always-listening system to respond to “voice access wake up” – but that’s not open for dev integration; mentioning it mainly shows the OS is capable. For our purposes, Porcupine on Windows would likely offer the most reliable wake word detection, as it does on other platforms. Other libraries like Vosk (an offline STT toolkit) can be tricked into hotword spotting by listening for a particular word, but that’s a heavier solution and less accurate than a purpose-built wake word model (How to set a wake up word for an virtual assistant using Vosk offline ...).

In summary, all platforms can utilize robust wake word detection by leveraging existing engines. The combination of Porcupine (wake word) and possibly a voice activity detector (to ensure the wake word isn’t triggered by a recording or noise) yields high reliability. This setup was used in the DaVinci assistant project: “Davinci uses Picovoice solutions for wake word and voice activity detection” to ensure it only wakes when someone is actually speaking the trigger (Meet DaVinci – The ChatGPT Virtual Assistant #piday #raspberrypi #ChatGPT « Adafruit Industries – Makers, hackers, artists, designers and engineers!). Between the platforms, there isn’t a stark difference in wake word accuracy since the same underlying algorithm can be used in both. However, implementing it natively might be easier (no need for WASM, just use a native lib). In a browser, it’s doable but you rely on the library’s WASM working smoothly in all browsers.

Integration with OpenAI API (Speech & Language)

Integrating voice with the OpenAI API involves two aspects: converting spoken words to text (Speech-to-Text) and handling the conversation with the AI (sending queries and getting responses), plus possibly converting the response to speech (Text-to-Speech). Let’s break down how each approach handles this and any limitations:

• Browser Extension: Once the wake word is detected, the extension can use either the Web Speech API to get the user’s speech as text or even stream audio to a backend for transcription. The Web Speech API’s accuracy is decent (it uses Google’s engines, typically), but if high accuracy is needed or if offline use is desired, the extension could instead record audio and send it to an API like OpenAI’s Whisper or to a local WASM model. However, recording arbitrary audio in an extension and sending it out might raise permission issues and complexity. It’s simpler to use the built-in STT. After obtaining the text of the user’s request, the extension would call the OpenAI completion/chat API with the query. The integration itself is straightforward HTTP, but as noted, doing this from a browser environment can trigger CORS restrictions. Officially, OpenAI’s API does not allow requests from any origin without proper configuration (Error when fetching an OpenAI location URL - Stack Overflow), and moreover, embedding secret API keys in front-end code is a security risk (Error when fetching an OpenAI location URL - Stack Overflow). In a controlled personal scenario, one might ignore the risk, but it’s generally recommended to route such calls through a backend. A Chrome extension does have one advantage: because it’s not a typical web page, you might declare the OpenAI API domain as allowable in the extension manifest and perform the request in a background script. This could bypass some CORS issues since extensions often have more leeway in accessing external URLs (with permission). Assuming that hurdle is overcome, the extension gets the AI’s text response. To present it to the user, it could either display a notification or speak it out using the Web Speech SpeechSynthesis API, which can use the system or browser voices to read text. That would give a fully voice-based interaction (the assistant talks back). Modern browsers support a variety of voices for TTS through this API. In summary, integration in an extension is doable but requires careful handling of the API calls (potentially needing a companion service or some manifest tweaks to allow direct calls).

• Web App: The web app’s client side would likely function similarly to the extension – use Web Speech or a WASM STT for input, then send the query to OpenAI. Since a pure web page can’t securely call OpenAI directly (CORS and key exposure issues), the typical solution is to have a backend component. If using Replit, you could implement a small Flask (Python) or Node server that the web app contacts via AJAX. The flow: the browser sends the transcribed text to your server (over HTTPS), the server (with the stored API key) calls OpenAI and returns the result. This is a common pattern to avoid CORS issues (Error when fetching an OpenAI location URL - Stack Overflow). The latency added is minimal (a few tens of milliseconds extra). The backend could also handle speech transcription if you prefer using OpenAI’s Whisper API – e.g., send the raw audio file to /v1/audio/transcriptions and get text, but that would be slower and not real-time (whisper API expects a file or chunk, not a streaming endpoint). Most likely, one would do real-time STT in the browser (since Web Speech is streaming and interim results capable) for a snappier feel. For output, the web app can directly use the browser’s SpeechSynthesis to speak the response or even play an audio file if the backend generates one (for example, one could use a service like ElevenLabs or AWS Polly on the backend to generate a more natural voice clip, but that’s optional). Overall, the integration of OpenAI in a web app is not technically hard, but it introduces the requirement of maintaining a backend service. This means your personal assistant is now a distributed system (client + server), which can be overkill for personal use.

• Native App: A native application can handle everything in one place. Using a programming language of choice, you can call the OpenAI API directly after getting the speech input. For instance, in Python you would use the openai library or just requests to POST to https://api.openai.com/v1/chat/completions with your prompt. There are no CORS issues because this is not a browser environment; it’s a direct server call. You also keep your API key local and safe. For speech recognition, a native app could either use an online API (like call OpenAI’s Whisper API with an audio snippet – but that’s slower and would require recording the full utterance first) or use an offline STT engine. Using Whisper locally is a strong choice for privacy and possibly speed. Whisper’s small models can run in real-time or faster on a decent CPU, and larger models can run with a GPU. If real-time transcription is too slow, one compromise is to use a cloud STT (like Google’s or Windows’ built-in speech engine) just for getting the text, then feed to GPT. However, given OpenAI now offers a Whisper API (with cost around $0.006/minute of audio) and given the scenario is personal use (so likely not huge volumes of speech), one could also utilize that for high-accuracy transcription. The native app has full freedom to choose which STT path to take. After getting the AI’s text response, the native app can use any TTS engine to vocalize it. Windows 11 comes with built-in voices accessible via the Windows Speech API or via PowerShell, and Python’s pyttsx3 can tap into those. Alternatively, just as the Raspberry Pi “DaVinci” project did, one could call an online TTS (DaVinci used Amazon Polly to get a natural voice audio for responses (Meet DaVinci – The ChatGPT Virtual Assistant #piday #raspberrypi #ChatGPT « Adafruit Industries – Makers, hackers, artists, designers and engineers!)). Since we’re already calling OpenAI, adding one more API call to a TTS service is feasible if high-quality voice output is desired beyond the default system voice. Summarizing, integration in a native app is straightforward and flexible – it can directly handle everything from listening to speaking. There are fewer points of failure (no need to maintain a separate web service or deal with browser policies).

Comparing the three, native apps offer the smoothest integration with OpenAI’s API, because they operate in an environment where communicating with external APIs is native. Browser-based solutions need additional layers (like backend proxies) to adhere to web security models. That said, all three approaches can ultimately achieve the final goal: taking user voice, getting an OpenAI-powered answer, and outputting voice. It’s more a question of how many workarounds are needed. The fewer the workarounds (as in a native app), the less likely something breaks.

Replit Hosting Considerations

The user mentioned possibly hosting or coding the app on Replit. Replit is a cloud development platform where you can run code in an online container. While it’s a great tool for coding and even hosting simple apps, there are some significant limitations for a real-time, always-on voice assistant:

• No Direct Microphone Access: If you run your voice assistant code on Replit’s server, that server has no access to your PC’s microphone. It will not magically hear your voice. As one Stack Overflow discussion pointed out, when code runs on a server, it only has access to devices on that server (which in Replit’s case, there is no microphone) (python - I deployed this code, but its taking input only from my device - Stack Overflow) (python - I deployed this code, but its taking input only from my device - Stack Overflow). To get audio from your PC to the Replit-hosted program, you would need to stream it from your browser to the server. This essentially means building a web interface anyway (the web app approach described above). In other words, Replit can host the brain of the assistant, but the “ears” (microphone) and “mouth” (speaker) must be on the client side, communicating over the internet. This adds complexity and potential latency.

• Always-On Limitations: Replit’s free tier does not run your code 24/7 by default. Containers will shut down when idle (typically after a few minutes of no web traffic). They introduced an “Always On” feature that kept a Repl running constantly, but as of Jan 2024 Replit removed Always On from free plans and moved to a Deployments model for hosting (Replit — Changes to Hosting on Replit). You would likely need a paid plan or use their deployment system (which might incur cost or require using their cycle credits) to keep your assistant backend up continuously. Even then, you’d want monitoring to restart it if it crashes. This is an extra hurdle if the intention was a simple plug-and-play solution.

• Latency and Performance: Streaming audio to a cloud service and getting responses will inherently be slower than a local solution. Replit containers also have limited CPU/RAM unless you pay for more. Running Whisper or even handling many audio streams might be too heavy for a small Replit instance. The Replit team noted that even playing audio through the browser has a ~100-300ms latency due to buffering (Replit — Introducing system audio). For capturing audio, you might encounter similar buffering. If your network connection is spotty, the performance of a cloud-hosted assistant will degrade. A local native app would be more immediate and reliable on a day-to-day basis (no dependency on internet for the hotword detection or STT if you use offline models).

• Use Replit for Prototyping: Replit could still be useful to develop the code (because you can quickly write and test portions of it, especially the OpenAI API integration or even the wake word logic using test audio files). It’s convenient to share code or collaborate. But when it comes to deployment for personal continuous use, it’s generally better to run the final product on your own machine or a dedicated device (like a Raspberry Pi or a local server) rather than an online host that wasn’t designed for real-time media from an external user.

In summary, Replit is not ideal as the live environment for a 24/7 voice assistant, except perhaps as the backend of a web app with the knowledge of the above constraints. For personal use on Windows 11, you’ll likely get the best experience running the assistant directly on the Windows machine itself.

Tools, Libraries, and Frameworks for Voice Assistant Development

To build a voice assistant meeting the above requirements, here are some recommended tools and components that align with the three modes and features:

• Wake Word Detection: Porcupine by Picovoice is highly recommended. It’s “highly-accurate and lightweight” and supports all major platforms including Web (WASM), Windows, Linux, etc. (GitHub - Picovoice/porcupine: On-device wake word detection powered by deep learning). Porcupine provides pre-trained wake words (“Alexa”, “Hey Google”, etc.) and allows custom wake words via their console. Alternative wake-word engines include Snowboy (though no longer actively maintained) and Mycroft Precise (open source, used in the Mycroft assistant project) – these can work but may require training a model with your chosen keyword. If you prefer a fully open solution, the OpenWakeWord project (recently leveraged by Home Assistant) is exploring open-source wake word models, though it’s relatively new (Using a desktop as a always listening assistant - Voice Assistant - Home Assistant Community) (Using a desktop as a always listening assistant - Voice Assistant - Home Assistant Community). For simplicity and reliability, Porcupine is the go-to solution (free for personal/educational use with provided models). It also has a Python SDK for native apps and a JavaScript SDK for web. Using a wake word engine means your assistant will only start full processing when necessary, saving resources and preserving privacy (A Voice AI Browser Extension for Hands-free Web - Picovoice).

• Speech Recognition (STT): Once awakened, converting speech to text is the next step. For browser-based implementations, the Web Speech API (specifically the SpeechRecognition interface) can be used to get live transcriptions. It’s easy and requires no additional libraries (A Voice AI Browser Extension for Hands-free Web - Picovoice). The downside is you rely on Google/Apple’s servers (depending on browser) and may face the 60-second limit if used continuously (javascript - Continuous Speech Recognition on browser like "ok google" or "hey siri" - Stack Overflow). For a native app, OpenAI’s Whisper model is an excellent choice for STT. Whisper is open-source and known for its high accuracy and ability to handle noise and accents (A Complete Guide to Using Whisper ASR: From Installation to Implementation) (A Complete Guide to Using Whisper ASR: From Installation to Implementation). You can run Whisper locally via the whisper Python package or more efficiently via Whisper.cpp which is a C++ port optimized for CPUs (even capable of real-time on some hardware). If real-time local processing is challenging, you can use the Whisper API provided by OpenAI – just send it the recorded audio after the user finishes speaking. Other options include Microsoft Azure Speech Service (very accurate, but a paid service) or Vosk (open source offline STT, which is lightweight but not as accurate as Whisper for general conversation). For a compromise, some hobby projects use Google’s free speech API indirectly via the SpeechRecognition Python library (which can use Google’s web speech behind the scenes) – but that requires internet and has usage limits. Overall, Whisper stands out as a modern solution if you want to keep as much as possible local.

• Voice Activity Detection (VAD): To support an “always-on” mode (no wake word), incorporating VAD is useful. VAD will detect the presence of human speech in the audio. For example, if you want the assistant to respond whenever you talk to it (without a hotword), you can run a VAD to know when to start recording and stop when silence returns. Picovoice’s Cobra is a VAD that can run in real-time and signal voice vs silence (Meet DaVinci – The ChatGPT Virtual Assistant #piday #raspberrypi #ChatGPT « Adafruit Industries – Makers, hackers, artists, designers and engineers!). WebRTC (the technology behind web video calls) has a built-in VAD that can be accessed via some libraries (for instance, the webrtcvad Python package). Using VAD, you could implement a mode where the assistant is effectively in a constant dialogue – but caution: without a wake word, the system might try to respond to any speech it hears (including other people or device sounds). It’s often best to still have a keyword or push-to-talk for clarity. However, VAD can at least ensure the microphone’s input is only processed when someone is actually speaking, reducing unnecessary load.

• OpenAI API (GPT integration): You’ll use OpenAI’s API for the conversational intelligence. The specific endpoint would likely be the Chat Completions API (for ChatGPT-style responses). Ensure you have your API key and are aware of the usage costs. The OpenAI Python SDK makes it easy in a native app (just install openai and call openai.ChatCompletion.create(...)). In a JavaScript context, you might call fetch on the API or use a proxy as discussed. One important consideration is handling the conversation context – you might want your assistant to remember the last few exchanges (to be conversational). This means maintaining a history of the dialogue and sending some recent messages in the prompt each time. It’s straightforward to implement (just store them in a list). Also, make sure to sanitize or limit what is sent to the API to avoid accidentally sending huge audio transcripts or unnecessary data, which could increase cost and latency.

• Text-to-Speech (TTS): For voice output, the easiest method in a web app or extension is the built-in Speech Synthesis API – you can choose a voice and speak a string of text. In a native Python app, pyttsx3 allows offline TTS using the voices installed on the system (for Windows, it uses SAPI5 voices – these are the standard voices that come with Windows). Windows 11 has some improved voices if online (as part of Cortana/Edge), but you can stick to the default or use an online TTS API. For example, Amazon Polly, Google Cloud TTS, or ElevenLabs can produce very natural sounding speech. The DaVinci project used Amazon Polly for realistic voice output (Meet DaVinci – The ChatGPT Virtual Assistant #piday #raspberrypi #ChatGPT « Adafruit Industries – Makers, hackers, artists, designers and engineers!). For personal use, you might call these services if you have API access, or just use whatever voice is available locally for simplicity. The TTS component is not explicitly demanded by the user question, but it’s strongly implied in “voice-based interaction” that the assistant will speak the answer, not just print it. Ensuring the voice output is clear and at a good volume on Windows is important for a good experience. If using the system’s TTS, you also get the benefit that it will automatically use your default output device (speakers) and you can control it via system settings.

• UI/Control Frameworks: If building a native app, you might consider whether it needs any GUI at all. A simple Python script can be headless. But you could also create a small system tray application (for example, using PyQt or Tkinter for a minimal interface) that allows you to toggle modes or see status. There are libraries that help create tray icons and menus in Python (like pystray). For a more polished solution, a C# WPF app could give a nice interface to control modes and show a transcript, but that’s only if you want a GUI. Many voice assistants run headless and perhaps use an audio cue to indicate wake (like a “beep” when activated, and maybe a different “ding” when deactivated). Those audio cues help the user know which mode it’s in (e.g., Alexa plays a short tone when it hears “Alexa”).

• Power Management: Because this will run 24/7, consider the impact on your system. On a desktop PC, it’s fine. On a laptop, you’d want to ensure that the continuous listening doesn’t prevent the system from sleeping or doesn’t drain battery heavily. Efficient wake word detection helps here. Also, Windows might put certain processes to sleep after long idle periods; if it’s a critical assistant, you might have to adjust power settings to allow it to run in background. For example, if it’s a UWP app or some modern app, it might need permission to run in background.

Below is a comparison table summarizing how the three platform options stack up against key criteria:

Recommendation and Conclusion

After examining the options, the native Windows application emerges as the most optimal solution for a personal 24/7 voice assistant given the requirements. The native approach offers superior support for continuous microphone access (it can truly always listen in the background), reliable wake word detection using offline models, and straightforward integration with OpenAI’s API without browser security roadblocks. In practice, many DIY voice assistant projects gravitate toward native apps for exactly these reasons – you have full control and are not constrained by a web browser’s environment.

Why Native? On a Windows 11 PC dedicated to one user, a native solution can be tightly integrated and even optimized for that specific hardware. You can fine-tune the wake word engine (e.g., adjust sensitivity of Porcupine), run local noise suppression if needed, and ensure that the assistant runs at startup so it’s always ready. With a browser-based solution, you’re adding layers of complexity (browser, potentially a web server, etc.) and potential points of failure (browser update or Replit downtime could break the chain). An extension or web app is better suited if you needed cross-device support or were building something to share, but for a single personal assistant on your machine, simplicity and reliability are key.

That said, if someone is far more comfortable with web technologies and doesn’t want to deal with audio drivers or native code at all, a browser extension can be a viable second choice. Especially with modern libraries, you can achieve an always-listening extension with wake-word (as Picovoice’s demo shows) (A Voice AI Browser Extension for Hands-free Web - Picovoice). Just keep in mind the need to keep the browser running and handle the 60-second restart issue for recognition (javascript - Continuous Speech Recognition on browser like "ok google" or "hey siri" - Stack Overflow). A web app hosted on Replit or elsewhere would be the third choice – it’s the least direct and would only be justified if, for example, you wanted to be able to access your assistant from multiple devices through a browser or needed to offload processing to the cloud for some reason.

Replit’s Role: Use Replit for what it’s great at – quick prototyping and iteration – but plan to run the final assistant on your Windows 11 system for the best experience. Replit could host a prototype web app to showcase the idea, but for daily use, running it locally avoids the pitfalls of an online host for a real-time application (python - I deployed this code, but its taking input only from my device - Stack Overflow) (Replit — Changes to Hosting on Replit).

Next Steps: To implement the recommended native solution, one could follow a path such as:

1. Set up Porcupine (via the Picovoice console or their GitHub SDK) in a Python script to detect your chosen wake word. Test it with your microphone to ensure it triggers appropriately.
2. Integrate a speech-to-text solution. For example, use the Whisper API or the speech_recognition library (with Google’s engine) to transcribe the user’s speech after the hotword. Ensure the assistant listens only after the wake word and maybe plays a brief acknowledgement tone to signal it’s recording the query.
3. Call the OpenAI API with the transcribed text. Use your API key securely and perhaps start with GPT-3.5 (which is faster and cheaper) and move to GPT-4 if needed for more complex interactions. Maintain a short conversation history to make the interaction more natural.
4. Take the AI’s response and output it via text-to-speech. Start with a simple approach (e.g., Windows default voice via pyttsx3 or winspeech). Later, you can experiment with more advanced voices or even have multiple voice personalities.
5. Implement the mode switching: a simple way is to have a command or keyboard trigger for switching modes. For example, you could make the assistant listen for a phrase like "assistant standby" to turn itself off (stop listening) and "assistant resume" to resume wake-word listening. Alternatively, if it has a small GUI or tray icon, clicking it could toggle between Active (wake-word mode) and Muted. Always-on mode (no wake word) can be an optional toggle as well, perhaps used in a scenario where you want continuous conversation (though always-on should use VAD and perhaps a timeout to avoid endless open mic to OpenAI).

Using the above tools and considerations, a personal voice assistant can be built that effectively says “Goodbye, Alexa” in favor of a fully customizable, private, and powerful AI assistant. The combination of on-device wake word detection and OpenAI’s language understanding is very powerful – as demonstrated by projects like the DaVinci RPi assistant which “uses Picovoice for wake word and voice activity detection and OpenAI’s GPT-3.5 to do the heavy lifting” (Meet DaVinci – The ChatGPT Virtual Assistant #piday #raspberrypi #ChatGPT « Adafruit Industries – Makers, hackers, artists, designers and engineers!). By choosing the right platform and libraries, you ensure that your assistant will be always at your service, when and where you need it, with minimal hassle.

Sources

• Picovoice Blog – Voice AI Browser Extension (Wake Word in WebAssembly) (A Voice AI Browser Extension for Hands-free Web - Picovoice) (A Voice AI Browser Extension for Hands-free Web - Picovoice)
• Picovoice GitHub – Porcupine Wake Word Engine Documentation (GitHub - Picovoice/porcupine: On-device wake word detection powered by deep learning) (GitHub - Picovoice/porcupine: On-device wake word detection powered by deep learning)
• Stack Overflow – Continuous Speech Recognition limitations in Chrome (javascript - Continuous Speech Recognition on browser like "ok google" or "hey siri" - Stack Overflow)
• Stack Overflow – Server vs Client microphone input (Replit context) (python - I deployed this code, but its taking input only from my device - Stack Overflow) (python - I deployed this code, but its taking input only from my device - Stack Overflow)
• Replit Blog – Changes to Always On hosting (2024) (Replit — Changes to Hosting on Replit)
• Adafruit Blog – DaVinci ChatGPT Voice Assistant example (Meet DaVinci – The ChatGPT Virtual Assistant #piday #raspberrypi #ChatGPT « Adafruit Industries – Makers, hackers, artists, designers and engineers!)
• F22 Labs Blog – Whisper ASR capabilities (2025 overview) (A Complete Guide to Using Whisper ASR: From Installation to Implementation) (A Complete Guide to Using Whisper ASR: From Installation to Implementation)
• Picovoice Documentation – Porcupine accuracy and performance FAQ