How to Create a Low-Latency Real-Time AI Speech-to-Speech: A Step-by-Step Guide

Delving into the realm of speech-to-speech technology, I’ve embarked on an exciting journey to create a low-latency, real-time system. And guess what? It’s entirely open-source and operable offline. 

In this blog post, I’m excited to guide you through this journey, showcasing the steps and insights of developing such a groundbreaking system.

Read more or watch the YouTube video(Recommended)


The Workflow

Step 1: System Overview

This speech-to-speech system is a marvel of modern technology, combining various elements like LM Studio running Dolphin Mistral 7B, Open Voice for text-to-speech, and Whisper for voice-to-text translations. What sets this system apart is its low latency, primarily because it operates offline and relies on open-source components, thus eliminating the need for API requests.

Step 2: Python Code and Setup

The heart of this system lies in its Python code. This code includes GPU offloading for enhanced speed and a context length of 4K, ensuring efficient performance without the need for extensive optimization. We also utilize a local inference server, which behaves similarly to the OpenAI API, allowing for straightforward client code integration.

Step 3: Implementing OpenVoice and Whisper

OpenVoice, renowned for its instant voice cloning capabilities, plays a crucial role in this setup. With over 11.6K stars on GitHub, it’s a testament to its effectiveness and popularity. Whisper is used for transcribing voice into text, keeping the process simple yet efficient.

local real time speech to speech ai python

Step 4: The Conversation Loop

The system includes a conversation history list to maintain context and utilizes PIDE audio for recording and playback. The chatbot is programmed to maintain a persona, making the interaction more engaging and realistic. This loop facilitates a smooth and dynamic conversation flow.

Step 5: Real-time Testing and Simulation

After setting up the system, it’s crucial to test it in real-time. This involves simulating conversations between two chatbots, adjusting settings for optimal performance, and fine-tuning the system based on these interactions.

The Results

Through these steps, the system demonstrates impressive low latency and high-quality speech-to-speech translation. The offline functionality offers a significant advantage, particularly in scenarios where internet connectivity is a constraint. The use of uncensored models in the system also allows for more natural and unrestricted conversations.


Creating a low-latency, real-time speech-to-speech system has been a fascinating endeavor, offering insights into the integration of various AI components. The open-source nature and offline capabilities make it a versatile and accessible tool. While there’s always room for optimization, the current setup proves to be a robust foundation for future developments in speech-to-speech technology.

In the world of AI, where evolution is constant, projects like these open doors to endless possibilities. The future of speech-to-speech technology looks promising, and it’s exhilarating to be part of this journey.

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