100% Local AI Speech to Speech with RAG - Low Latency | Mistral 7B, Faster Whisper ++

All About AI
14 Apr 202414:42

TLDRThe video introduces a 100% local AI speech-to-speech system incorporating RAG for efficient information retrieval and interaction. The system utilizes a local LLM, such as Mistral 7B, and various TTS engines like XTTS 2 and Open Voice for quality and low-latency responses. Users can issue voice commands to manage tasks, schedule, and transcribe meetings, with the system leveraging GPU for inference optimization. The video also demonstrates how to integrate a PDF into the system for the AI to analyze and respond to queries, showcasing the potential for AI-assisted project management and information access.

Takeaways

  • 🤖 The script introduces a 100% local AI speech-to-speech system with RAG (Retrieval-Augmented Generation) integrated for improved performance.
  • 🗓️ The AI assistant, Emma, helps manage the user's calendar, 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 personal schedules for important individuals.
  • 📝 The system supports various models, including Dolphin, Mistol 7B, and others, with the quality of RAG performance depending on the chosen model.
  • 🔊 Local TTS (Text-to-Speech) engines are utilized, with XTTS 2 for quality voice and Open Voice for low-latency responses.
  • 🎤 The system can transcribe voice input directly to text using Faster Whisper, which can then be used by the AI assistant or written into a text file.
  • 📂 The embedding vector database created from transcribed text can be accessed by the AI assistant to provide context-aware responses.
  • 🔗 Open source projects are used extensively in the system, including Mini LM L6 V2, XTTS V2, Faster Whisper, and Open Voice.
  • 💻 The importance of using GPU for inference to save time and improve system performance is emphasized, especially when working with large models.
  • 🎥 The video demonstrates real-time interaction with the AI system, showcasing the ease of adding and deleting information from the user's schedule and vault.
  • 📚 The system can process and respond to uploaded PDF documents, extracting relevant information and providing summaries based on the content.

Q & A

  • What is the main feature of the AI system described in the script?

    -The main feature of the AI system described is its 100% local speech-to-speech capability with RAG (Retrieval-Augmented Generation) included, which allows for efficient and low-latency interaction.

  • What does the acronym RAG stand for in the context of the script?

    -In the context of the script, RAG stands for Retrieval-Augmented Generation, a machine learning technique used to improve the performance of AI models by incorporating knowledge from a large database of text.

  • What are the two TTS engines mentioned in the script and how do they differ in terms of speed?

    -The two TTS (Text-to-Speech) engines mentioned are XTTS 2 and Open Voice. XTTS 2 is noted for producing higher quality voice but is slower, while Open Voice is optimized for low latency, meaning it is faster.

  • How does the AI system handle user commands for inserting and deleting information?

    -The AI system handles user commands through voice inputs that start with specific phrases like 'insert info' or 'delete info'. When 'insert info' is used, the system transcribes the user's speech into text and appends it to a text file called 'vault.text'. For deletion, the user must confirm the action before the system removes the specified content from 'vault.text'.

  • What is the purpose of the 'get relevant context' function in the script?

    -The 'get relevant context' function retrieves the top K most relevant pieces of text, or 'chunks', from the embeddings based on their cosine similarity to the user input. This helps the AI system provide contextually relevant responses.

  • How does the AI system utilize GPU to improve performance?

    -The AI system uses GPU (Graphics Processing Unit) for models like faster whisper and XTC to save inference time. By leveraging GPU, the system can perform complex computations more efficiently and at a faster pace, which is especially important when processing tasks that require heavy computation.

  • What is the significance of the parameters that can be adjusted in the XTTS model?

    -The adjustable parameters in the XTTS model, such as temperature and cont length, allow for customization of the text-to-speech output. For instance, the temperature parameter can influence the 'emotion' or tone of the generated speech, while the speed function controls how quickly the model speaks.

  • How does the AI assistant's personality influence its interactions with the user?

    -The AI assistant's personality, as set by the user, influences the tone and style of its responses. In the script, the assistant named Emma is programmed to respond in a slightly complaining and whining manner, adding a conversational and human-like touch to the interactions.

  • What happens when a PDF file is uploaded to the system?

    -When a PDF file is uploaded, it is first converted to text and then appended to the 'vault.text' file. The content of the PDF becomes part of the embeddings, which the AI system can access and use to provide contextually relevant information or responses.

  • How does the AI system demonstrate the use of sampling and voting in handling task difficulty?

    -The AI system extracts information from a paper uploaded as a PDF and uses a technique called sampling and voting. This involves multiple 'agents' contributing responses, which are then combined to improve the overall performance and handle task difficulty more effectively.

Outlines

00:00

🤖 Introduction to the Speech-to-Speech System

The paragraph introduces a local speech-to-speech system with RAG (Retrieval-Augmented Generation) included. The system allows users to choose different models, such as a 7B model, and highlights that better models improve RAG performance. It also mentions the local TTS (Text-to-Speech) engine and the use of open-source projects like mini LM L6 V2, xtts V2, faster whisper, and open voice. The system's functionality is demonstrated through a conversation between Chris and Emma, the assistant, where meetings are scheduled and information is printed using voice commands.

05:00

🚀 Leveraging GPU for Efficiency

This section discusses the importance of using GPU to save on inference time for the system's whisper and XTC models. It mentions that without a GPU, the system might run slow. The benefits of offloading the full model to the GPU for speed are emphasized. Additionally, the xtts model's adjustable parameters are highlighted, including temperature and speed functions, which allow for control over the emotional output and speech rate of the text-to-speech model.

10:01

📄 Managing and Testing Embeddings and Agent Interaction

The paragraph demonstrates how to add and delete content from the system's embeddings and how the agent, Emma, interacts with it. It explains the process of using voice commands to insert and delete information, as well as how to upload a PDF, convert it into text, and integrate it into the embeddings. The system's ability to retrieve and respond to information from the uploaded document using the embeddings model is tested and showcased, highlighting the use of a 13B parameter model for better performance in RAG operations.

Mindmap

Keywords

💡Local AI Speech to Speech

Local AI Speech to Speech refers to a system that processes and converts spoken language to text and then back to speech, all within the local environment without relying on external servers. In the context of the video, this system includes RAG (Retrieval-Augmented Generation) which enhances the performance by accessing relevant contextual information. This is exemplified when the user interacts with the AI named Emma, who responds and processes commands based on the user's spoken input.

💡Mistral 7B

Mistral 7B is a large language model with 7 billion parameters that is used in the AI system described in the video. It is designed to handle complex language tasks and generate human-like responses. In the video, the user can choose different models, and the better the model, the better the RAG performs. Mistral 7B is one of the options provided for users to select for higher quality responses and interactions.

💡Faster Whisper

Faster Whisper is a transcription system mentioned in the video that converts spoken language into text quickly and efficiently. It is used in the AI system to transcribe the user's voice directly, which can then be processed by the AI assistant or written into a text file. The use of Faster Whisper allows for real-time interactions and the ability to incorporate user inputs into the system's knowledge base.

💡Low Latency

Low latency refers to the minimal delay between the input and output of data in a system. In the context of the AI system, low latency is crucial for real-time interactions, ensuring that the AI assistant responds quickly to user inputs. The video mentions an optimized TTS (Text-to-Speech) engine called Open Voice, which is designed for low latency, allowing for faster and more immediate feedback from the AI.

💡TTS Engine

A TTS (Text-to-Speech) engine is a software system that converts written text into spoken words, replicating human speech. In the video, the TTS engine is used to generate audible responses from the AI assistant. The system offers different TTS engines, such as XTTS V2 and Open Voice, each with its own characteristics in terms of quality and latency, allowing users to choose the one that best fits their needs.

💡Embedding Vector Database

An embedding vector database is a type of database that stores vectors representing semantic meanings of words, phrases, or documents. In the AI system described, the embedding vector database is used to store transcribed and converted user inputs, which the AI assistant can then access to provide contextually relevant responses. This database is essential for the RAG system to retrieve and utilize relevant information during interactions with the user.

💡Get Relevant Context Function

The 'Get Relevant Context Function' is a part of the AI system that retrieves the most relevant context based on the user's input. It works by comparing the user's input with the stored embeddings and selecting the top K most similar contexts to provide a relevant response. In the video, the user has set the top K to three, meaning the system retrieves the top three most relevant pieces of context to inform the AI's response.

💡Open Source Projects

Open source projects are software projects where the source code is made publicly available, allowing anyone to view, use, modify, and distribute the code. In the video, the creator acknowledges the use of several open source projects, such as Mini LM L6 V2 for creating embeddings, XTTS V2 for quality voice generation, and Faster Whisper for transcription. These projects contribute to the functionality and efficiency of the AI system.

💡GPU Utilization

GPU (Graphics Processing Unit) utilization refers to the use of a GPU to accelerate computational processes, particularly in AI and machine learning tasks. In the context of the video, the creator emphasizes the importance of using a GPU to save inference time and improve the overall performance of the AI system. Models like Faster Whisper and XTC are mentioned to utilize CUDA, a parallel computing platform and API model created by Nvidia, to optimize their performance on GPUs.

💡Chatbot Agent

A chatbot agent is an AI program designed to simulate conversation with human users, providing information or assistance as needed. In the video, the chatbot agent, named Emma, interacts with the user, processing voice commands and accessing the embedding vector database to provide relevant responses. The chatbot agent's personality and behavior can be customized, as seen when the user sets up voice commands and defines Emma's role as an assistant who complains and whines in a conversational manner.

💡RAG (Retrieval-Augmented Generation)

RAG (Retrieval-Augmented Generation) is a machine learning technique that combines the capabilities of retrieval systems, which can fetch relevant information from a database, with generative models, which can create new text based on input. In the video, RAG is used to enhance the AI's ability to provide contextually relevant and informed responses by accessing an embedding vector database. This allows the AI to retrieve and incorporate information from various sources, such as text files or PDFs, into its responses.

Highlights

Introduction of a 100% local AI speech-to-speech system with RAG (Retrieval-Augmented Generation).

Integration of the Mistral 7B model for enhanced performance in RAG.

Utilization of a local TTS (Text-to-Speech) engine optimized for low latency.

Faster Whisper++ for efficient real-time voice transcription.

Demonstration of the system's ability to handle and respond to user commands through voice.

Showcase of the system's capability to transcribe and append voice input into a text file.

Explanation of how the embedding vector database is accessed by the assistant chatbot agent.

Use of open-source projects for the development of the AI system.

Functionality to delete and print files based on user voice commands.

Efforts to maximize GPU usage for saving inference time in the system.

Customization of the AI assistant's personality and interaction style.

Real-time testing of the system's ability to store and retrieve meeting information.

Integration of a PDF text upload feature into the system's embeddings.

Switching to a 13B model from Quenet for improved RAG operations.

Extraction and summarization of information from a PDF using the large language model.

Discussion on the method that scales the performance of large language models with the number of agents.

Conclusion and invitation for viewers to access the full code and join the community.

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