I’ve been working on this local language model scribe project that transcribes audio and summarizes it into outlines, all running locally with Python and Faster Whisper. So far, the transcription part triggers based on pauses in speech and outputs time-stamped text directly into Obsidian, which feels pretty seamless. The script runs quietly in the system tray, but I ran into a problem with notifications—when they’re enabled, the transcription stops after the first batch of audio files. For now, I’ve just disabled notifications to keep things running smoothly, but I still need to figure out why that happens and how to fix it.

I’m trying to make the whole setup more flexible. Right now, the transcript output location, audio inputs, and archiving of old files are pretty fixed, but I want to allow easy changes through configuration. That way I can swap models or change prompts without rewriting code. Speaking of models, I’m testing with Whisper but also exploring Mistral Q4 as a potential alternative. The plan is to integrate a large language model with a large context window to summarize the transcripts into key outlines. I’m still figuring out how to handle context limits effectively, especially when dealing with multiple long transcripts. Stitching transcripts together or adjusting context length is something I need to experiment with more.

On the Obsidian side, I’ve been working on a Python script that adds tags to transcripts by detecting certain words inline. It’s a bit tricky because some words can have multiple meanings or uses, so auto-tagging isn’t always straightforward. I’m also thinking about backlink creation, but that might require either running a live script or having a local model handle it, and I haven’t nailed down the best approach yet.

There are some weird formatting issues cropping up in the transcripts that I want to clean up, and I’m still testing how well the tagging script works in practice. Overall, I’ve renamed the project to better reflect its purpose as a local language model scribe, which feels more accurate than before. Next steps are fixing the notification problem, improving flexibility, and figuring out how to integrate the summarization model smoothly into the workflow. I’m also curious to see how well the tagging and backlink features can be made reliable without too much manual tweaking.

Two-Line Takeaway

Key step forward

The most important forward progress step is improving the flexibility of the transcription setup by allowing easy configuration changes and integrating a large language model to summarize transcripts into key outlines.

Significant barrier or risk

The most important barrier or risk is the notification issue that causes transcription to stop after the first batch of audio files, which currently requires disabling notifications to keep the system running smoothly.

Keynotes

Decisions & Observations

• Decided to disable notifications temporarily to prevent transcription from stopping after the first batch of audio files; needs to resolve the root cause of this issue.

Issues & Next Steps

• Needs to make transcript output location, audio inputs, and file archiving configurable to enable easier swapping of models and prompts without code changes.
• Will explore integrating large language models with large context windows to summarize transcripts and experiment with handling context limits by stitching transcripts or adjusting context length.
• Needs to improve the auto-tagging script to handle ambiguous word meanings more reliably and evaluate the feasibility of backlink creation via live scripts or local models.
• Will address formatting issues in transcripts and continue testing tagging script effectiveness.
• Next steps: fix notification problem, enhance configuration flexibility, integrate summarization model smoothly, and improve reliability of tagging and backlink features.

Generated with Nucleate by Watchlight Studio — distilled from full transcription.