100% Local AI Speech to Speech with RAG - Low Latency | Mistral 7B, Faster Whisper ++
TLDRThe video introduces a 100% local AI speech-to-speech system incorporating RAG for improved performance. It utilizes various models like Mistral 7B and Faster Whisper for transcription and low-latency text-to-speech conversion. The system can transcribe voice input, store it as text, and allow an AI chatbot agent to access this information, responding contextually. The setup leverages GPU for efficiency and is demonstrated through interactions between characters Chris and Emma, showcasing the system's capabilities in scheduling and information retrieval from a PDF.
Takeaways
- 🤖 The script introduces a 100% local AI speech-to-speech system incorporating RAG (Retrieval-Augmented Generation) for improved performance.
- 🗓️ The AI assistant named Emma helps manage the user's schedule, including an upcoming meeting with Nvidia at 1:00 a.m.
- 🌙 The user's ability to sleep during the day and attend meetings at unusual hours is discussed, highlighting the flexibility of the AI system.
- 📈 The script emphasizes the importance of using high-quality models for better RAG performance, with options like Dolphin, Mistol 7B, and others.
- 🚀 Local TTS (Text-to-Speech) engines are utilized, with XTTS 2 noted for quality and Open Voice for low latency.
- 🔍 The system uses Faster Whisper for transcription, converting voice input directly to text for immediate response or text file creation.
- 📂 The AI system can store and retrieve information from an 'embeddings' database, allowing the assistant to access and use the stored data in context.
- 📋 The script provides a look at the 'get relevant context' function, which retrieves the top K most relevant contexts from the embeddings based on user input.
- 🎉 The AI assistant's personality can be customized, as demonstrated by the conversational and slightly complaining manner of Emma.
- 📈 The importance of GPU utilization for inference time reduction is discussed, with the system designed to offload full model computations to the GPU.
- 🔗 The audience is encouraged to join the channel's community for access to full code and further resources, with a GitHub and Discord community available.
Q & A
What is the main feature of the AI system described in the transcript?
-The main feature of the AI system described is its 100% local speech-to-speech capability with RAG (Retrieval-Augmented Generation) included, allowing for efficient and low-latency interactions.
What type of meetings does the character Chris have scheduled?
-Chris has a meeting with Nvidia at 1:00 a.m., a meeting with Mell on Wednesday at 2 a.m., and a YouTube video recording on Friday about an LLM that becomes sentient and tries to take over the world.
How does the AI system handle voice inputs?
-The AI system transcribes voice inputs directly from voice to text using Faster Whisper, and the transcribed text can be either responded to by the AI agent or written into a text file that gets converted into embeddings.
What is the role of the TTS engine in the system?
-The TTS (Text-to-Speech) engine is responsible for converting text into spoken words. The system uses two TTS engines: XTTS 2 for higher quality voice and Open Voice for optimized low latency.
How does the AI system utilize embeddings?
-The AI system uses embeddings to store and retrieve relevant context from text inputs, voice commands, and uploaded documents, which are then used to inform the AI agent's responses.
What is the significance of the 'insert info' command?
-The 'insert info' command is used to write the user's spoken words into a text file (Vault), which is then converted into embeddings and stored for future reference by the AI agent.
How does the AI system manage to keep track of Chris's schedule?
-The AI system keeps track of Chris's schedule by processing the information provided through voice commands and storing it in the 'Vault' text file, which is accessible by the AI agent.
What is the purpose of the 'delete info' command?
-The 'delete info' command allows the user to remove specific information from the 'Vault' text file, which is confirmed before deletion to prevent accidental loss of data.
How does the AI system optimize for low latency?
-The AI system optimizes for low latency by using the Faster Whisper model for transcription and Open Voice TTS engine, and by leveraging GPU resources for inference to speed up processing times.
What is the role of the GPU in the AI system?
-The GPU is used to accelerate inference times for the AI system. It is utilized by models like Faster Whisper for transcription and XTTS for text-to-speech conversion to improve the system's overall performance.
How can the AI system's response personality be customized?
-The AI system's response personality can be customized by setting specific parameters within the system prompt, such as the agent's name and its conversational style, allowing for a more personalized interaction experience.
Outlines
🤖 Introduction to the Speech-to-Speech System
The paragraph introduces a local speech-to-speech system with RAG (Retrieval-Augmented Generation) included, highlighting the ability to choose different models for better performance. The system also features a local TTS (Text-to-Speech) engine and a low-latency TTS engine called Open Voice. The speaker discusses the functionality of the system, such as transcribing voice to text and embedding vector databases for the assistant chatbot agent. The speaker also mentions the use of open-source projects and shares some key lines of code for the system's functionality.
🚀 Leveraging GPU for Inference and XTS Model Features
This paragraph discusses the importance of using GPU to save inference time for the whisper model and the XTC model. It also explains how the system can be slow with only a CPU. The speaker mentions the LM Studio's offloading of the full model to the GPU for speed and the adjustable parameters of the XTS model, including temperature and speed functions to control the tone and pace of the text-to-speech output.
🗣️ Testing the System and Uploading PDFs
The speaker demonstrates the system's ability to handle voice commands for adding and deleting information from the embeddings. A live example is given, showing how meetings can be added to a schedule and then listed upon request. The speaker also shows how to upload a PDF, convert it into text, and integrate it into the embeddings for the chatbot agent to access. The chatbot agent is then able to extract and discuss information from the uploaded PDF, showcasing the system's capability to understand and respond to queries based on the new information.
Mindmap
Keywords
💡Local AI Speech to Speech
💡RAG (Retrieval-Augmented Generation)
💡Mistral 7B
💡Faster Whisper
💡Low Latency
💡TTS (Text-to-Speech)
💡Embeddings
💡Calendar Management
💡Open Source Projects
💡GPU Utilization
💡Chatbot Agent
Highlights
100% local speech-to-speech system with RAG (Retrieval-Augmented Generation) for efficient and localized information processing.
Integration of Mistral 7B, a powerful language model, to enhance the performance of RAG.
Faster Whisper++ for rapid and accurate transcription of voice to text.
Low latency TTS (Text-to-Speech) engine called Open Voice for immediate responses.
Customizable agent personality, such as Emma, the assistant with a complaining and whining conversational style.
The system can handle, store, and retrieve information from a user's calendar, such as meeting schedules.
Ability to transcribe and append voice commands into a text file for further processing.
Utilization of GPU for inference to optimize speed and performance of the AI system.
Open-source projects leveraged for various components of the system, including Mini LM L6 V2 for embeddings and Cuda for GPU acceleration.
Functionality to delete and manage information stored within the system through voice commands.
Dynamic adjustment of model parameters for TTS, such as temperature and speed, to control the output's emotional tone and pace.
Community access to the full codebase for members to fork, modify, and utilize in their own AI engineering projects.
Real-time demonstration showcasing the system's capability to process, store, and retrieve information from a PDF document.
Use of sampling and voting method to improve the performance and task handling of large language models with multiple agents.
The project serves as a solid baseline for AI engineering enthusiasts to build upon and experiment with.
Upcoming features and models, such as the switch to a 13B model from Quenet, to improve RAG operations.
Engagement with the audience through membership and community participation for further development and Q&A.