I designed and deployed an end-to-end AI system that transforms raw meeting audio into structured, speaker-aware transcripts, summaries, and shareable reports suitable for internal business workflows.
The Problem
Meetings generate valuable information, but most of it is lost. Audio recordings are difficult to search, time-consuming to review, and rarely transformed into actionable documentation.
Manual transcription and note-taking are slow and error-prone, while generic transcription tools fail to:
- Identify who said what
- Produce readable, structured summaries
- Generate outputs that teams can actually share and reuse
The challenge: build a system that converts raw meeting audio into clear, structured, and distributable intelligence — not just text.
The Solution
This project is a meeting intelligence assistant that processes recorded meeting audio and produces:
- Speaker-aware transcripts with timestamps
- High-level meeting summaries
- Polished PDF reports
- Optional email delivery for async collaboration
The system mirrors how internal AI tools are built inside real organizations — modular, extensible, and focused on usability.
System Architecture
The application follows a multi-stage speech intelligence pipeline:
- Users upload recorded meeting audio (MP3/WAV)
- Speech-to-text is performed using Deepgram or Whisper
- Speaker diarization identifies and segments individual speakers
- Transcript segments are aligned, cleaned, and normalized
- LLM-based post-processing generates structured summaries
- Outputs are packaged into readable transcripts and PDF reports
- Results can be delivered via email for easy sharing
Each stage is intentionally decoupled to allow future upgrades or replacements.
Key Engineering Decisions
Speaker-Aware Transcription
Rather than producing raw transcripts, the system performs speaker diarization with timestamp alignment, making transcripts readable, attributable, and suitable for business use.
Modular Speech Stack
The system supports both cloud-based (Deepgram) and local (Whisper / Pyannote) components, allowing trade-offs between accuracy, cost, and compute availability.
LLM-Based Post-Processing
LLaMA 3 (served via Groq) is used after transcription to generate concise, structured meeting summaries rather than verbose paraphrases.
Report-Ready Outputs
Transcripts are converted into polished PDF reports, making them immediately usable for documentation, client follow-ups, or internal archives.
Core Capabilities
- High-accuracy speech-to-text transcription
- Speaker diarization with timestamped segments
- Clean, readable meeting transcripts
- Automated meeting summaries
- PDF report generation
- Optional email delivery for async teams
- Web-based interface for easy access
Production-Oriented Implementation
- UI: Gradio 6.x for rapid, reliable interfaces
- Speech-to-Text: Deepgram Nova-2 or Whisper
- Diarization: Deepgram or Pyannote
- LLM: LLaMA 3 via Groq API
- Audio Processing: pydub, ffmpy
- Reporting: fpdf2
- Deployment: Hugging Face Spaces (CPU-optimized)
The system is designed to run efficiently in CPU-only environments, making it cost-effective to deploy.
Outcome & Impact
- Built a complete speech-intelligence pipeline from audio upload to shareable report
- Demonstrated real-world AI automation beyond chat interfaces
- Created a system adaptable to internal teams, founders, and client-facing workflows
This project shows applied AI engineering for knowledge capture and operational efficiency.
Live Demo
▶ Try the AI Meeting Assistant on Hugging Face
Upload a meeting recording and receive a full transcript, summary, and PDF report.
Skills Demonstrated
- Speech Intelligence Pipelines
- Speaker Diarization & Audio Processing
- LLM-Based Post-Processing
- AI-Driven Document Generation
- End-to-End System Design
- Production-Style AI Deployment
Planned Improvements
- Speaker name labeling and customization
- Calendar and meeting-platform integrations
- Streaming transcription for live meetings
- Analytics on meeting duration and participation
Why This Project Matters
This project shows how I build AI systems: focused on capturing real information, structuring it intelligently, and delivering outputs people actually use.
Want such a system custom built for your business?
I specialize in designing and deploying production-grade AI agents that solve real operational challenges. Let's discuss how we can automate your high-stakes workflows.
Contact Me