This is a submission for the AssemblyAI Challenge : Sophisticated Speech-to-Text, No More Monkey Business
What I Built
I built RepodAI, an AI-powered podcasting platform designed to harness the capabilities of AssemblyAI’s Universal-2 Speech-to-Text Model. RepodAI is more than just a transcription tool—it integrates conversational intelligence, natural language processing, and sentiment analysis to enhance the podcast creation and consumption experience. From transcription to sentiment analysis, speaker identification, and translation, RepodAI empowers podcasters and listeners alike with rich features and seamless usability.
Demo
Screenshots:
Live Demo:
GitHub Repository:
CijeTheCreator
/
Repod
A powerpacked podcasting platform built around AssemblyAI.
This is a Next.js project bootstrapped with create-next-app.
Getting Started
First, run the development server:
npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun dev
Open http://localhost:3000 with your browser to see the result.
You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file.
This project uses next/font to automatically optimize and load Geist, a new font family for Vercel.
Learn More
To learn more about Next.js, take a look at the following resources:
- Next.js Documentation - learn about Next.js features and API.
- Learn Next.js - an interactive Next.js tutorial.
You can check out the Next.js GitHub repository - your feedback and contributions are welcome!
Deploy on Vercel
The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.
Check out our Next.js deployment documentation for more…
Journey
RepodAI began as a vision for a sophisticated podcasting platform that brings conversational intelligence to the forefront. Leveraging AssemblyAI’s Universal-2 model as the foundation, RepodAI transforms how users interact with audio content. Here’s how I incorporated AssemblyAI’s Speech-to-Text capabilities into this project:
Key Features
- Audio Upload and Transcription
- Profanity Filtering
- Speaker Identification and Sentiment Analysis
- Chapter Segmentation and Summarization
- Advanced Search and Navigation
- AI-Powered Interaction superchared by Lemur
- Multi-Language Translation
- Dynamic and Interactive Player
- Customizable Themes and Mobile Responsiveness
The Prompts I Worked On
Sophisticated Speech-to-Text
I utilized AssemblyAI’s transcription API for two main use cases:
Transcribing the Main Podcast
This step involved converting the uploaded audio file into text, ensuring that the podcast's spoken content was accurately captured and ready for processing by features such as summarization and sentiment analysis.Converting Asked Questions to Text
Questions asked to RepodAI's chatbot (via voice input) are transcribed into text before being processed by LeMUR, enabling precise and context-aware responses.
*No More Monkey Business *
I also employed LeMUR for the following key features:
RepodAI’s Chatbot
The chatbot generates insightful answers to user questions about the podcast by processing transcriptions of both the podcast and the user’s query.Creating the Initial Podcast Summary
During the upload process, RepodAI uses the transcribed content to generate an initial summary of the podcast, providing a quick overview for users.
Tech Stack 🚀
- Next.js 🖥️: For building the UI and backend.
- ShadcnUI 🎨: Component library for consistent and elegant UI.
- Neon Postgres 🐘: To store user-generated podcasts.
- Three.js 🎧: For audio visualization when asking AI questions.
- Universal-2 🗣️: Powering sophisticated speech-to-text transcription.
- LeMUR 🤖: Intelligent LLM-powered interaction with spoken data.
- OpenAI TTS 🗨️: For text-to-speech conversion.
References
The algorithm for this is from https://codepen.io/prakhar625 's audio visualiser codepen in which I altered the source code a little to suit my style of design and way of function for this project.
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