Audioflare Project Introduction
Audioflare is an innovative all-in-one AI audio playground designed to handle various audio processing tasks, such as transcription, analysis, summarization, and translation. This project utilizes Cloudflare AI Workers to offer these capabilities and demonstrates how AI can effectively process an audio file of up to 30 seconds long. Here's a breakdown of what Audioflare offers:
Overview of Audioflare
Developed as a side project at Smol AI, Audioflare explores the potential of Cloudflare AI Workers, revealing a practical use case in audio processing. The key functionalities include:
- Transcription: Audioflare begins by transcribing the audio file using Cloudflare's Speech to Text worker, which relies on OpenAI's Whisper API.
- Summarization: The transcribed text is condensed using Cloudflare's LLM AI worker, based on the llama-2-7b-chat-int8 model. However, the model struggles with lengthy prompts.
- Sentiment Analysis: The system performs sentiment analysis on the text with Cloudflare's Text Classification AI worker, utilizing Huggingface’s distilbert-sst-2-int8 model.
- Translation: The transcribed text is translated into nine different languages using Cloudflare's Translation AI workers, based on Meta's m2m100-1.2b model.
- Performance Metrics: The project calculates and reveals the time taken for each request, offering insight into the efficiency of processing.
- Observability and Monitoring: Integration with Cloudflare AI Gateway ensures analytics, logging, caching, and rate limiting for the AI workers.
Key Features
Audioflare comes equipped with a range of features to optimize user experience and functionality:
- Audio Processing: Upload any audio file (drag and drop from a computer or choose from provided samples), keeping in mind only the first 30 seconds are processed.
- Text Summarization: Provides a concise summary of the transcribed text.
- Sentiment Analysis: Assesses the emotional tone within the transcribed text.
- Translation: Converts text into multiple languages, promoting accessibility and comprehension.
- Performance Metrics and Monitoring: Evaluates the time taken for processing requests and includes features like analytics, logging, and rate limiting to enhance the experience.
Technology Used
This project leverages various cutting-edge technologies implemented in 2023:
- React, Next.js: For creating a seamless user interface and efficient routing.
- Cloudflare, Vercel: For robust backend services and deployment.
- TailwindCSS, Bun: For styling and server-side execution.
- shadcn/ui: Enhances user interface component design and structure.
Getting Started
To experience Audioflare locally, follow these simple steps:
- Clone the repository from GitHub.
- Install necessary dependencies.
- Create a Cloudflare account and set up Wrangler.
- Set up the environment configurations.
- Run the application and access it at
http://localhost:3000.
Contribution and License
Audioflare is a fantastic resource for gaining insights into Cloudflare's AI Workers and Next.js API Routes. Developers are encouraged to fork the project, share feedback, and contribute to its development. The project is open-source, covered under the MIT License, and ready for community collaboration.
In summary, Audioflare is more than a tool; it's an exploratory platform for anyone interested in AI, bringing creative and technological advancements together through audio processing. Visit the live demo or the GitHub repository to delve deeper into what Audioflare has to offer.