Best LLM / AI to create thorough & not super broad notes from transcripts?

Harnessing AI for Detailed Lecture Note Creation from Transcripts: An Overview of Top Tools and Approaches

In the realm of academic and professional development, efficient note-taking remains a cornerstone for effective learning and information retention. For individuals who prefer recording lectures via audio, transforming those recordings into comprehensive, well-structured notes can be a time-consuming task. Fortunately, advances in artificial intelligence (AI) and natural language processing (NLP) have opened new avenues for automating this process. This article explores the most effective AI tools and techniques for generating thorough notes from lecture transcripts.

Understanding the Workflow: From Audio to Notes

The typical workflow begins with audio recording, which you then transcribe into text. For transcription, tools like OpenAI’s Whisper are widely used due to their accuracy and open-source availability. Whisper allows users to process audio on local machines, providing export options such as JSON, plain text, or other formats. Once the transcript is obtained, AI models can analyze and summarize the content to produce detailed notes.

Key Considerations for Effective Note Generation

1. Transcription Format and Data Handling
2. Export formats such as JSON or TXT each have their advantages. JSON preserves structural information, which can be useful for context-aware processing, while TXT offers a straightforward input for some NLP models.
3. Local vs. Cloud-Based AI Processing
4. Running large language models (LLMs) locally requires substantial computational resources, such as a high-performance GPU (e.g., NVIDIA RTX 3090). This approach offers data privacy and customization but demands technical expertise.
5. Cloud-based services (e.g., OpenAI’s GPT models) provide easy-to-use interfaces, often producing high-quality summaries, but involve ongoing costs and data transfer considerations.
6. Model Selection for Summarization
7. General-purpose models like ChatGPT and Google’s Gemini are capable of summarizing and extracting key points but may sometimes gloss over nuanced details.
8. Open-source models such as LLaMA or GPT-J, which can be run locally, allow for tailored fine-tuning and more control over the summarization process.

Recommended Tools and Resources

• Whisper for Transcription:
• An open-source speech recognition model that supports high-accuracy transcription locally.

• Supports exporting transcripts in various formats such as JSON or TXT, suitable for downstream processing.

• Local Language Models:

• tldw_server by rmusser01: An open