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14 changes: 7 additions & 7 deletions education-ai-suite/smart-classroom/README.md
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Expand Up @@ -4,13 +4,13 @@ The **Smart Classroom** project is a modular, extensible framework designed to p

The main features are as follows:

• Audio transcription with ASR models (e.g., Whisper, Paraformer)
• Summarization using powerful LLMs (e.g., Qwen, LLaMA)
• Plug-and-play architecture for integrating new ASR and LLM models
• API-first design ready for frontend integration
• Extensible roadmap for real-time streaming, diarization, translation, and video analysis
- Audio transcription with ASR models (e.g., Whisper, Paraformer)
- Summarization using powerful LLMs (e.g., Qwen, LLaMA)
- Plug-and-play architecture for integrating new ASR and LLM models
- API-first design ready for frontend integration
- Extensible roadmap for real-time streaming, diarization, translation, and video analysis

[![Smart Classroom UI](./docs/user-guide/images/smart_classroom_ui.png)]
![Smart Classroom UI](./docs/user-guide/images/smart_classroom_ui.png)

## Get Started

Expand All @@ -24,7 +24,7 @@ To see the system requirements and other installations, see the following guides
The basic architecture follows a modular pipeline designed for efficient audio summarisation. It begins with **audio preprocessing**, where FFMPEG chunks input audio into smaller segments for optimal handling. These segments are processed by an **ASR transcriber** (e.g., Whisper or Paraformer) to convert speech into text. Finally, an **LLM summariser** (such as Qwen or Llama), optimised through frameworks like OpenVINO IR, Llama.cpp, or IPEX, generates concise summaries, which are delivered via the **output handler** for downstream use.


[![High-Level System Diagram](./docs/user-guide/images/architecture.svg)]
![High-Level System Diagram](./docs/user-guide/images/architecture.svg)


## Learn More
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