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Tone 0.1.2 – hackable cross platform audio tagger

Hacker News

Tone 0.1.2 – hackable cross platform audio tagger

Hey HN, I just wanted to show the next version on my little (maybe useless) side project called tone[1] to get some qualified feedback. tone is a cross plattform command line audio tagger deployed as a single static binary without dependencies, so a wget should be enough to install on any platform (seems not to work on M1 Macs atm... if someone can help here I would really appreciate it). It gained some major improvements since the last version. As soon as .NET 7 is out, I plan to integrate AOT (ahead of time compilation)... Features: - dump and modify metadata for supports most common audio formats (mp3, m4a, flac, ape, etc.) - use JSONPath to query single metadata fields in dumps (--query='$.meta.album') - most common AND custom metadata fields (album, artist, but also sort-title, movement-name, etc.) - Chapter support for mp3 and m4a - Embeddable pictures - Custom `tone.json` metadata format to export / import all metadata in one file (including covers as base64) - Hackable via custom JavaScript functions (write your own taggers with url fetching and custom parameters)[2] - dump range of raw audiodata bytes (e.g. to calculate hashes) Thanks and have fun. [1]: https://github.com/sandreas/tone [2]: https://github.com/sandreas/tone#custom-scripted-taggers-experimental

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, para, including · Missing: reddit linkedin, podcasting, created
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, soon · Missing: plus, intuitive, reviews
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: mac, single · Missing: agents, macos, agent
39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io, including · Missing: https docs, excited, just released
28%28% 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 · Missing: arr, mrr, revenue
13%13% 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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