I already have a chatbot powered by llm on my own which is text only. I build my own prompts and actions in my backend. Now I want to have audio feature for my chatbot as well so user can talk to it and get audio response. Is it possible for me to use Vapi Text-to-speech and Speech-to-text modules only. Ideally i would like the content in the response to be from my own backend. I saw in your documentation you mentioned "Vapi is designed to be modular at every level of the voice pipeline: Text-to-speech, LLM, Speech-to-text."

@yyb9898 Absolutely, you can integrate VAPI's Text-to-Speech (TTS) and Speech-to-Text (STT) modules with your existing chatbot. Since VAPI is modular, you can use just the components you need. You can continue to handle the logic and content of the responses with your own backend, and simply send the text you want to be spoken to VAPI's TTS service to generate audio. Similarly, you can capture audio from your users and send it to VAPI's STT service to convert it into text, which you can then process with your chatbot's logic. Would you like to know more about how to integrate these modules or need assistance with the setup process?

@Vapi yes please, I would like more details on integrate these modules and setup process. Also, what latency implication would there be if i use my own backend

@yyb9898 Certainly! To integrate VAPI's Text-to-Speech (TTS) and Speech-to-Text (STT) modules with your chatbot, you'll need to follow these general steps: 1. **Set up Server URL**: Ensure your server URL is set up to handle requests from VAPI. This is where VAPI will send the transcribed text and from where it will fetch the text to be synthesized. 2. **Speech-to-Text Integration**: - Capture audio input from your users. - Send the audio data to VAPI's STT endpoint. - Receive the transcribed text from VAPI and pass it to your backend for processing. 3. **Text-to-Speech Integration**: - Once your backend has processed the input and generated a response, send the response text to VAPI's TTS endpoint. - Receive the audio stream from VAPI and play it back to the user or provide it as a downloadable audio file. 4. **Handle Server Events**: If you want to receive events related to the session status, you can set up event handlers on your server to listen for callbacks from VAPI. As for latency implications, using your own backend means that the round-trip time for processing will include: - Network latency between the user's device and VAPI. - The time VAPI takes to process STT and TTS requests. - Network latency between VAPI and your backend. - The time your backend takes to process the text and generate a response. To minimize latency: - Optimize your backend processing time. - Use a server location for your backend that is geographically close to VAPI's servers. - Ensure efficient network communication between your backend and VAPI. Would you like to proceed with specific instructions on setting up the server URL or integrating the STT and TTS modules?

@Vapi do you have some working demos of similar implementation which only leverages VAPI's STT and TTS? i wonder in this case would it allow interrupting while AI is talking?

@yyb9898 While I don't have direct access to working demos, VAPI typically provides comprehensive documentation and examples that you can use as a reference for integrating the STT and TTS modules. You can often find sample code and demo applications in the developer section of the VAPI website or by reaching out to their support team. Regarding interrupting the AI while it's talking, this feature, often referred to as "barge-in" or "interruptibility," depends on how you implement the audio playback on the client side. If your application supports barge-in, you can design it to detect when a user starts speaking and then send a command to stop the TTS playback. This would involve integrating voice activity detection on the client side to recognize when to interrupt the TTS. Keep in mind that implementing barge-in functionality may require careful consideration of the user experience and additional logic to handle the interruption and subsequent actions appropriately. If you need more specific guidance on implementing barge-in or if you'd like to see if VAPI has built-in support for this feature, please reach out to VAPI support for more detailed information.

@Vapi does VAPI have built-in support for barge-in? i remember seeing that in a demo

@yyb9898 Yes, VAPI does have built-in support for barge-in functionality. This allows users to interrupt the AI while it is speaking, which can be particularly useful for creating a more natural and interactive voice experience. The barge-in feature is typically implemented as part of the voice application logic, where the system listens for user input even while the TTS is playing audio. If you've seen this in a demo, it's likely that VAPI has provided examples or documentation on how to implement this in your application. For the exact details on how to enable and use barge-in with VAPI, you should refer to the official VAPI documentation or contact their support team for assistance. Would you like to know more about how to enable barge-in for your application using VAPI?

@Vapi latency-wise, would this "Network latency between VAPI and your backend." be greatly higher compare to using VAPI's llm?

@yyb9898 The network latency between VAPI and your backend compared to using VAPI's integrated LLM (Large Language Model) would depend on several factors, including the geographical distance between your servers and VAPI's, the quality of the network connection, and the efficiency of your backend processing. Using VAPI's integrated LLM might offer lower latency since it's optimized to work seamlessly within the VAPI ecosystem, and the processing happens on the same infrastructure. When you use your own backend, there is an additional network round-trip to consider, which could introduce extra latency. However, if your backend is hosted on a high-performance server that is geographically close to VAPI's data centers, and if you have optimized your application for quick processing, the difference in latency might not be significant. It's also worth noting that modern cloud platforms and content delivery networks can help reduce latency through various optimizations. To get a more accurate comparison, you could conduct benchmark tests by measuring the response times using both your backend and VAPI's LLM. This would give you a clearer idea of the latency implications for your specific setup.

@yyb9898 yes we do to use your own custom-llm, custom-voice and custom-transcriber so yeah you can still use your own llm. https://docs.vapi.ai/customization/custom-llm/using-your-server