tr

trv. A tool to turn a presentation and speaker notes into a video

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trv. A tool to turn a presentation and speaker notes into a video

Videos can be very effective in teaching, but I myself never make them because I don't like narrating videos nor video editing. So that got me thinking whether text-to-speech can be used to generate videos automatically. That's what I hacked together now in the `trv` Rust crate (it's a binary that you can install via cargo install, see https://github.com/transformrs/trv for the source code and docs). It's a tool that you can give a Typst presentation with speaker notes to. Next, the tool will turn the Typst file into images and audio, and then turn everything into a video. For example, I made one I video about a blog post that I wrote earlier. Unfortunately, I cannot directly upload a video here on HN, so here is a link: https://youtu.be/vn8-Asioxq8 . To give an idea of how the video was made, here is the first slide of the Typst presentation: #import "@preview/polylux:0.4.0": * #set page(paper: "presentation-16-9", margin: 1in) #set text(size: 30pt) #slide[ #toolbox.pdfpc.speaker-note( ```md Iterators are pretty cool. For example, in Python we could write the following code in a normal loop. Here we have a list of 3 values and we add 1 to each value. This returns a new list with the values `[2, 3, 4]`. ``` ) ```python values = [1, 2, 3] for i in range(len(values)): values[i] += 1 print(values) # [2, 3, 4] ``` ] Next, I ran the following command: $ trv --input=presentation.typ \ --model='hexgrad/Kokoro-82M' \ --voice='am_liam' \ --release" This created a video of 1.2 MB that I then uploaded to YouTube. Is a tool like this useful? What are your thoughts?

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, new, notes · Missing: mac, agents, macos
89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
TrustMRRFits verified-revenue profile · Strong signals: video · Missing: mobile apps, ios, personal
64%64% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, margin · Missing: mrr, revenue, profit
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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