**Low-Latency Conversational AI: Building a Real-Time Voice Agent from Scratch**
Conversational AI has come a long way in recent years, but one of the biggest challenges we still face is latency. If your virtual assistant takes too long to respond, users will get frustrated and lose interest. In this tutorial, we’re going to build a real-time voice agent that mirrors the low-latency conversational techniques used by trendy AI systems today. We’ll walk you through the entire pipeline, from chunked audio input and streaming speech recognition to incremental language model reasoning and streamed text-to-speech output, while keeping a close eye on latency at every stage.
**The Code**
You can find the full code for this tutorial on GitHub:
Let’s dive in and explore each component:
### Simulating Real-Time Audio Input
To model real-time audio input, we’ll break speech into fixed-duration chunks that arrive asynchronously. This simulates how audio input would look if it were coming from a microphone in real-time. We’ll also introduce talking rates and streaming behavior to make it more realistic.
### Streaming ASR: Partial Transcriptions and Silence-Based Finalization
Our streaming ASR module will produce partial transcriptions before emitting a final result. This is similar to how modern ASR techniques work in real-time. We’ll also use silence-based finalization to approximate end-of-utterance detection, which helps the system know when to stop processing audio.
### Streaming LLM: Generating Responses Token by Token
Next, we’ll model a streaming language model that generates responses token by token. This captures the time-to-first-token behavior that’s crucial for low-latency conversational AI. We’ll then convert incremental text into audio chunks to simulate early and continuous speech synthesis.
### Streaming TTS: Orchestrating the Total System
Finally, we’ll wire all these components together to create a single asynchronous pipeline with clear stage boundaries. This allows us to measure performance guarantees and ensure responsiveness.
**Latency Budgets: Keeping Things Fast**
To ensure our system is responsive, we’ll apply aggressive latency budgets to key components:
* ASR processing: 0.1 seconds
* LLM first token: 0.3 seconds
* LLM token generation: 0.02 seconds
* TTS first chunk: 0.15 seconds
* Time to first audio: 0.8 seconds
**Running the Demo**
Let’s run our system across multiple conversational turns to evaluate latency consistency and variance. We’ll apply these runs to validate whether the system meets our responsiveness targets throughout interactions.
**Conclusion**
In this tutorial, we’ve demonstrated how to build a fully streaming voice agent that combines partial ASR, token-level LLM streaming, and early-start TTS. By keeping a close eye on latency at every stage, we’ve shown that it’s possible to reduce latency while maintaining overall system performance. Try out the full code on GitHub and experiment with different latency budgets to see how the system responds.
