Local Low Latency Speech to Speech - Mistral 7B + OpenVoice / Whisper | Open Source AI
Science & Technology
Introduction
In this article, we present a detailed breakdown of a locally operated, low latency speech-to-speech system featuring the Mistral 7B model, OpenVoice for text-to-speech, and Whisper for speech recognition. This open-source project is designed to function entirely offline, ensuring minimal latency and maximum privacy by eliminating dependencies on external API requests.
Overview of the System Setup
The architecture of this speech-to-speech system revolves around several core components:
Mistral 7B Model in LM Studio: We are utilizing the uncensored version of the Mistral 7B model, which is integrated into LM Studio to enhance conversational depth and injecting a bit of excitement into dialogues.
OpenVoice: This tool is utilized to convert text responses into speech, allowing for realistic audio output that maintains the context and tone of the conversation.
Whisper: Whisper acts as the speech recognition engine that converts spoken words into text and sends it back to the Python Hub for processing, completing the feedback loop for continuous interaction.
The absence of API dependencies allows us to achieve low latency during conversations. Users are encouraged to provide suggestions for improvements in the comments section of the corresponding YouTube video.
Code Walkthrough
The current implementation is based on Python, where users run a local server to interact with the components listed above. Here are some highlights of the code structure:
- Logging and Audio Playback: Basic functionalities are created to log conversations and playback audio outputs.
- Model Loading: The audio is generated through the OpenVoice model using input from the LM Studio.
- Transcription: The voice is transcribed via Whisper, iterating through voice commands continuously, creating a responsive user experience.
- Conversation History: The system maintains a chat history to provide context and continuity in conversations.
Through the use of a defined system prompt, users can modify the personalities and styles of their chatbot interactants to customize dialogues.
Interactive Simulations
During testing, a conversational simulation between two AIs—"Julie," a dark web hacker, and "Johnny," a radical AI researcher—was showcased. The conversation included humorous and outrageous exchanges regarding cybersecurity and dark web activities. Insights from the tests revealed the capability of generating witty or intense dialogues based on varying profiles for the chatbots.
In a separate testing instance, the two chatbots simulated a conversation devoid of human input, revealing their programmed personalities. This type of simulation highlights the potential for dynamic interaction without reliance on human moderators.
Closing Thoughts
This project represents an exciting development in speech-to-speech technology, allowing users to explore AI capabilities entirely offline. As the open-source nature of the project continues to evolve, suggestions for code optimization and feature enhancements are greatly appreciated.
Links to access the project and further documentation are provided in the descriptions. Stay tuned for more updates as the project continues to grow!
Keywords
- Low latency
- Speech to speech
- Mistral 7B
- OpenVoice
- Whisper
- Open source
- Offline AI
FAQ
What is the main function of the presented system?
The system enables low latency speech-to-speech conversations using an offline setup with Mistral 7B, OpenVoice, and Whisper.
Is this project available for public use?
Yes, this project is open source and can be accessed and run locally without an internet connection.
How can I contribute to the project?
Users are encouraged to leave suggestions for optimizations and features in the comments of the related YouTube video.
What chatbot personas are available?
Users can define various personality traits and conversational styles through system prompts in the code, allowing for customization.
