| # This script will transcribe an audio file (mp3, wav, etc.) to text and then clean the text using a local LLM model via Ollama. Technically, this script will work with any LLM that supports the standard OpenAI bindings with minor adjustments. | |
| # GETTING STARTED: | |
| # 1. Install required python packages (pip install openai python-dotenv) | |
| # 2. Git clone a copy of ggerganov/whisper (https://github.com/ggerganov/whisper.cpp) | |
| # 3. Build the whisper binary (see the whisper.cpp README for instructions) | |
| # 4. Download one of the whisper models (largev2 is the most accurate for all languages, though the base model works reasonably well for English). | |
| # 5. Install ffmpeg (brew install ffmpeg on macOS, apt-get install ffmpeg) | |
| # 6. Install ollama (https://ollama.com/download) | |
| # 7. Download an LLM model (https://ollama.com/library) |
| There are two prompts, that chain together. The first prompt does most of the work, and the second prompt organizes the sections. I found because of the nature of how LLMs write, I couldn't get just one prompt to never jump back and forth in topics. | |
| Prompt 1, which takes as input a raw transcript and generates a structured-text version... | |
| """# Instructions | |
| A transcript is provided below of a voice memo I recorded as a "note to self". please extract all the points made or thoughts described, and put them in bullet-point form. use nested bullet points to indicate structure, e.g. a top-level bullet for each topic area and sub-bullets underneath. use multi-level nesting as appropriate to organize the thinking logically. use markdown formatting with `*` instead of `-` for bullet points. | |
| DO NOT OMIT ANY POINTS MADE. This is not a summarization task — your only goal is to structure the thoughts there so they are logically organized and easy to read. Be concise because the reader is busy, but again DO NOT omit any |
last update: 2022-11-12
This article is a response to mfiano’s From Common Lisp to Julia which might also convey some developments happening in Common Lisp. I do not intend to suggest that someone coming from a Matlab, R, or Python background should pickup Common Lisp. Julia is a reasonably good language when compared to what it intends to replace. You should pickup Common Lisp only if you are interested in programming in general, not limited to scientific computing, and envision yourself writing code for the rest of your life. It will expand your mind to what is possible, and that goes beyond the macro system. Along the same lines though, you should also pickup C, Haskell, Forth, and perhaps a few other languages that have some noteworthy things to teach, and that I too have been to lazy to learn.
/I also do not intend to offend anyone. I’m okay with criticizing Common Lisp (I myself have done it below!), but I want t
| // Can be used with https://github.com/xcv58/Custom-JavaScript-for-Websites-2 | |
| // This snippet is released under the terms of the CC0 license: https://creativecommons.org/publicdomain/zero/1.0/deed.en | |
| const cache_key = 'hn_comments_views' | |
| const cache = JSON.parse(localStorage.getItem(cache_key) || '{}') | |
| document.querySelectorAll('.athing.comtr').forEach(comm => { | |
| if (!cache[comm.id]) { | |
| const span = document.createElement('span') | |
| span.innerHTML = '🔔' // :bell: emoji |
This was a response to a Hacker News comment asking me what I've been up to since 2010. I'm posting it here since HN rejects it with "that comment is too long." I suppose that's fair, since this ended up being something of an autobiography.
--
What happened after 2010?
In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.
| silly gist hack, why do we need you? :( |
by xero updated 10.29.24
