Best Groq Practice - Making a Voice Assistant with Human Reaction Speed
TLDRIn this tutorial from Yeyu Lab, viewers are guided through the creation of a voice assistant using Groq's lightning-fast LLM inference capabilities. The assistant, demonstrated through a 'Catch Me If You Can' Hugging Face demo, showcases impressive response times. The video covers API access on GroqCloud, rate limits for free use, and the cost-effectiveness of Groq's inference services. It also details code implementation, transitioning from Open AI to Groq, and concludes with a demo of a voice assistant with a user-friendly UI and natural conversation flow. The project integrates HTML, JavaScript, and Python Flask, offering real-time speech recognition and synthesis for an interactive user experience.
Takeaways
- π Groq offers a fast inference experience for language models, which can be utilized to create a voice assistant with human reaction speeds.
- π The 'Catch Me If You Can' demo on Huggingface showcases Groq's high-speed processing, capable of over 700 tokens per second with the Gemma 7B model.
- π Groq Cloud platform's API is currently free with rate limits, allowing for a stable service operation with 30 requests per minute and 14,000 requests per day.
- π° Groq's pricing for inference is competitive, with the Gemma 7B model costing only 10 cents for a million tokens.
- π οΈ Code implementation for integrating with Groq's inference API is straightforward for those familiar with the OpenAI API format.
- π Three main changes are needed to adapt existing projects to Groq: replacing OpenAI functions with Groq functions, swapping API keys, and updating the model name to one supported by Groq.
- π£οΈ The voice assistant demo features a user interface with smooth voice conversation capabilities, utilizing HTML, JavaScript, and a Python-based Flask program.
- ποΈ The system uses the Web Speech API for speech recognition, which balances speed and accuracy, and is robust enough for the language model to generate responses.
- π The Flask backend integrates with OpenAI's text-to-speech model and the Groq service to create a conversational voice assistant, handling real-time speech recognition and synthesis.
- π The backend manages cross-origin requests and initializes API endpoints with relevant keys, ensuring secure and efficient communication with the Groq service.
- π The voice assistant workflow includes starting the recognition, processing the speech with Groq, generating a response, and synthesizing the AI's text back into speech for the user.
Q & A
What is the main focus of the Yeyu lab's demonstration in the video?
-The main focus of the Yeyu lab's demonstration is the development of a voice assistant that utilizes Groq's fast inference capabilities with large language models (LLM) for real-time interaction.
What is the name of the demo on huggingface that the video refers to?
-The demo on huggingface referred to in the video is called 'Catch Me If You Can', which showcases the app powered by Groq and Gemma.
What is unique about the response speed of the voice assistant in the demo?
-The response speed of the voice assistant in the demo is unique because it is much faster than human typing speed, with a processing speed that can reach over 700 tokens per second.
What are the rate limits for the free use of Groq's API as mentioned in the video?
-The rate limits for the free use of Groq's API are 30 requests per minute, 14,000 requests per day, and 4,000 tokens per minute.
How much does it cost to use the Gemma 7B model for a million tokens according to the video?
-According to the video, using the Gemma 7B model for a million tokens costs 10 cents.
What are the three items that need to be changed when switching from the Open AI API to Groq inference API in an existing project?
-The three items that need to be changed are: replacing the Open AI function with Groq function, replacing the Open AI API key with the Groq API key, and replacing the Open AI model name with the model name supported by Groq.
What are the three models currently supported by Groq as mentioned in the video?
-The three models currently supported by Groq are LLaMA 2, Mixture 8, 7B, and Gemma 7B.
What is the role of the JavaScript in the voice assistant project demonstrated in the video?
-The JavaScript in the voice assistant project is responsible for managing functionalities like speech processing and speech recognition, as well as handling user actions to start and stop voice recognition.
What is the name of the open-source JavaScript library used for speech to text functionality in the demo?
-The open-source JavaScript library used for speech to text functionality in the demo is called the Web Speech API.
How does the voice assistant handle the situation when the speech input text is incomplete or broken?
-When the speech input text is incomplete or broken, the voice assistant asks the user to repeat or complete the message, ensuring the conversation remains coherent.
What is the purpose of the 'start session' function in the voice assistant project?
-The 'start session' function in the voice assistant project is used to reset the chat bot to its initial state by clearing up the history of messages.
Outlines
π Introduction to Groq and Voice Assistant Development
This paragraph introduces the development of a voice assistant using Groq's fast inference capabilities. The script discusses a demo called 'Catch Me If You Can' on Hugging Face, which showcases Groq's impressive inference speed with open-source language models. The user is encouraged to try the demo first. The paragraph also covers the access to Groq's API through the GroqCloud platform, which is currently free of charge but subject to rate limits. The pricing for API usage is mentioned, highlighting the cost-effectiveness of Groq's services. The transition from Open AI API to Groq API is discussed, emphasizing the ease of integration for those familiar with the Open AI API format. Three supported models by Groq are mentioned: Llama 2 Mixture 8 times 7B and Gemma 7B. The paragraph concludes with a teaser for a demo of a voice assistant with a user-friendly interface and smooth voice interaction.
π¨ Project Overview and Code Implementation
This paragraph provides an overview of the voice assistant project, detailing its basic HTML structure, styling with Bootstrap, and JavaScript functions for managing speech processing and recognition. It introduces a Python-based Flex program that works with Groq API and Open AI text-to-speech API. The user experience workflow is described, starting from the user's voice input, through Groq processing, to the AI response and speech synthesis. The code implementation is discussed step by step, starting from the main HTML body, focusing on the UI design, key elements like the talker button, and the script section. The JavaScript section is highlighted, explaining the event listener for managing user actions and the use of the Web Speech API for speech recognition. The paragraph concludes with the implementation details of voice recognition and the process speech function, which handles text processing and response generation.
π Backend Integration with Groq and Open AI
This paragraph delves into the backend integration of the voice assistant with Groq and Open AI. It describes the Python Flask backend that integrates with Groq service to create a conversational voice assistant. Key components of the Flask application are discussed, including the use of Flask CORS for handling cross-origin requests and the initialization of API endpoints with relevant API keys. The paragraph emphasizes the importance of keeping API keys secret and not hardcoding them in production code. The structure of historical messages is initialized to facilitate human-AI conversations. The speech input text processing is described, including the use of Groq inference LLM to generate responses. The paragraph concludes with the explanation of the synthesize speech process and the start speech route, which allows for resetting the chatbot to its initial state.
π Deployment and Conclusion
This final paragraph wraps up the tutorial by discussing the deployment of the voice assistant project. It suggests deploying the HTML file on a local or remote server for access via URL or running it directly by double-clicking the file. The paragraph highlights the seamless integration of Groq's fast inference API, resulting in a highly responsive and interactive user experience in both text and voice output. The project is summarized as combining HTML, JavaScript, and Python Flask to implement a typical client-server architecture with real-time speech recognition and synthesis. The source code for the project is mentioned to be available in the description, and the paragraph concludes with a call to action for likes, subscriptions, and notifications.
Mindmap
Keywords
π‘Voice Assistant
π‘Groq
π‘LLM (Large Language Model)
π‘Inference
π‘API (Application Programming Interface)
π‘Rate Limits
π‘Tokens
π‘HTML
π‘JavaScript
π‘Python
π‘Flask
π‘Speech Recognition
π‘Text-to-Speech
Highlights
Development of a voice assistant utilizing Groq's fast inference capabilities with LLM.
Introduction of a demo called 'Catch Me If You Can' showcasing Groq's high-speed processing.
Groq's inference speed can exceed 700 tokens per second with the Gemma 7B instruction model.
Access to Groq Cloud platform API is free with certain rate limits for stable service.
Groq's pricing for API usage is competitive, with the Gemma 7B model costing 10 cents per million tokens.
Code implementation is straightforward for those familiar with the Open AI API format.
Three models supported by Groq: LLama 2, Mixture 8 times 7B, and Gemma 7B.
Demonstration of a voice assistant with a user-friendly UI and smooth voice interaction.
The voice assistant's workflow includes speech recognition, processing with Groq, and response synthesis.
HTML structure uses Bootstrap for styling with JavaScript managing speech functionalities.
Use of the Web Speech API for speech recognition in the demo.
Implementation details of voice recognition configuration and event handling.
ProcessSpeech function sends text for processing and handles server responses for AI response display.
Speak function converts AI text into speech using a server-side service.
Python Flask backend integrates with Groq and Open AI models for conversational AI.
Key components include Flask, CORS, and custom text-to-speech functionality.
API endpoints for Groq and Open AI initialized with relevant keys for service interaction.
The system uses user roles to deliver instructions to the model due to the lack of system prompt support.
Root processSpeech handles user input text and updates conversation history for Groq inference.
Root synthesizeSpeech converts text to voice using Open AI's tts1 model.
StartSpeech route resets the chatbot to its initial state by clearing message history.
The voice assistant combines HTML, JavaScript, and Python Flask for real-time speech interaction.
The project demonstrates Groq's fast inference API for a responsive and interactive user experience.