notjoemartinez

Incident metadata and transcripts are stored in a PostgreSQL db indefinitely. We've logged over 250k incidents since January. Planning on combining the incident data with other sources for historical analysis, search and such.

If you email me the police department I you're curious about I can see if I can add it to the supported departments.

> are you doing this real time or in a batch mode/daily scrape?

I'm transcribing the previous day starting 1AM every day, which is about as much as my old MacBook I have can handle. I'm in the process of applying for the Broadcastify Calls API which should give me access to real time feeds but it will be a challenge to get the compute needed to handle real time transcriptions.

> personally it would be useful to maybe subscribe to key words and get notifications for specific cities

This is a feature I have planned. It will come with a major disclaimer about whisper transcript hallucinations.

Edit: spelling

Location: San Antonio, TX

Remote: Yes

Willing to Relocate: Yes

Technologies: Vue.js, Flask, Node.js, Express.js, PostgreSQL, SQLite, ChromaDB, Docker, Nginx, Windows Presentation Foundation // Infrastructure: AWS (EC2, Cloudfront, S3, Lambda), and Cloudflare (Workers, Pages, R2, KV) // Libraries: OpenCV, scikit-learn, LangChain, TensorFlow, Pandas, Matplotlib, and ChartJS // Languages: Python, JavaScript, C/C++, Bash, Java, C#, HTML, CSS.

Résumé/CV: https://notjoemartinez.com/about/resume.pdf

Email: [email protected]

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I am a software engineer with over 5 years of experience in full-stack development, a background in embedded systems, computer vision, and data visualization. I am currently seeking internship or full-time employment opportunities.

GitHub: https://github.com/NotJoeMartinez

Website: https://notjoemartinez.com

I've been trying to solve a problem with implementing semantic search on my YouTube search engine yt-fts (https://github.com/NotJoeMartinez/yt-fts). I've managed to substantially speed up search results by storing subtitle embeddings in Chroma. But a bigger problem has been with how to properly segment the text in a way that accounts for the duration and context of word embeddings while returning precise time stamps. This a blog post exploring what I've tried so far.

Thank you! I was able to integrate this into the project[1]. I'm also looking into using your openai-to-sqlite[2] library for semantic search.

[1]https://github.com/NotJoeMartinez/yt-fts/pull/25 [2]https://github.com/simonw/openai-to-sqlite

> yt-fts search "love" --channel "Lex Fridman" | grep "love" | wc -l

> 7060