100% Local AI Speech to Speech with RAG - Low Latency | Mistral 7B, Faster Whisper ++
Science & Technology
Introduction
In this article, we explore an innovative local speech-to-speech AI system designed to operate efficiently and effectively. The system features integrated Retrieval-Augmented Generation (RAG) capabilities, ensuring that it provides relevant information based on user input seamlessly and promptly. Here, we will unpack the technological components such as the Mistral 7B model, Faster Whisper for transcription, and voice command functionality.
System Overview
The core of this project revolves around using a fully local speech system with capabilities to transcribe audio to text, generate relevant contextual outputs, and seamlessly operate various voice commands. This system employs a local large language model (LLM) that can be customized for better performance based on user needs. Key functionalities include:
- Speech Recognition: Utilizing Faster Whisper for rapid audio transcription.
- Text-to-Speech (TTS): An advanced TTS engine (Open Voice) optimized for low latency responses.
- RAG Integration: Allowing the model to retrieve the top K most relevant contexts from a local text file converted into embeddings.
Key Technologies
The project incorporates several notable open-source technologies:
- Mini LLM L6 V2 for creating embeddings.
- XTTs V2 for quality voice generation.
- Faster Whisper for quick and reliable transcription.
- Open Voice for low-latency TTS.
These integrations enrich the assistant's functionality and ensure that it remains responsive to user input.
Code Implementation
One of the notable elements of this system is the get_relevant_context function, which retrieves the most pertinent textual chunks based on user input. The default is set to retrieve three relevant chunks, and the user can adjust this parameter as required.
The voice command setup primarily leverages conditional commands. For example, if the user begins a command with "insert info," the system appends new information into a text file that serves as a vault for embeddings. Additionally, another command fetches and confirms deletions within the text storage.
GPU Utilization
To optimize performance, the system heavily leverages GPU capabilities. Both the Faster Whisper model and TTS engine utilize CUDA to enhance processing speed and decrease latency. While a CPU-only setup may experience slower performance, access to a GPU significantly improves the system's overall inference speed.
Demo and Testing
After implementing the system, a demonstration illustrates how users interact with the assistant named Emma. The user can not only store meeting information but can also query the assistant for upcoming tasks. Emma's responses maintain a lighthearted tone, reflecting her programmed personality.
For instance, upon receiving new meeting information, Emma humorously laments her growing list of tasks. Additionally, functionality includes seamless deletion of entries within the vault upon user confirmation.
PDF Upload Feature
An added layer of the system is its capability to upload PDFs and convert the text into embeddings. This feature exemplifies the system's versatility, allowing for deeper queries from documents and resources. A demonstration using an academic paper showed successful extraction of contextual data, highlighting Emma's ability to provide accurate responses based on the uploaded content.
Conclusion
The local AI speech-to-speech system constructed using Mistral 7B and Faster Whisper showcases a robust foundation for those interested in developing AI projects with speech processing capabilities. Its vibrant personality and responsiveness make it an engaging tool for users seeking to manage their schedules or extract critical data from text.
For those interested in experimenting with this technology, the full code and setup guidelines are available for community members.
Keywords
- Local AI
- Speech to Speech
- RAG
- Mistral 7B
- Faster Whisper
- Voice Commands
- TTS Engine
- Embeddings
- GPU Optimization
FAQ
Q1: What does RAG stand for, and why is it important?
A1: RAG stands for Retrieval-Augmented Generation. It enhances response accuracy by fetching relevant context based on user inputs, making the system more efficient in providing useful information.
Q2: Can this system work without a GPU?
A2: Yes, but the performance may significantly decrease. The system is optimized for GPU usage to handle inference tasks swiftly.
Q3: How can users interact with the assistant?
A3: Users interact with the assistant via voice commands, such as “insert info” to add data and “delete info” to remove content, all in a conversational manner.
Q4: Is the TTS engine suitable for real-time applications?
A4: Yes, the Open Voice TTS engine has been optimized for low latency, making it suitable for real-time voice applications.
Q5: Where can I find the code for this project?
A5: The code is available for community members in a GitHub repository linked in the article, providing a solid base for similar AI projects.
