Tone 0.1.8 – hackable cross platform audio tagger

Hacker News

Today I released tone 0.1.8 [1] - the new version of my hackable cross platform command line audio tagger. Features: - dump and modify metadata for most common audio formats (mp3, m4a, flac, ape, etc.) - use JSONPath to query single metadata fields in dumps (--query='$.meta.album') - common AND custom metadata fields (album, artist, but also sort-title, movement-name, etc.) - Chapter support for mp3 and m4a - Embeddable pictures - Custom `tone.json` metadata format to export / import all metadata in one file (including covers as base64) - Hackable via custom JavaScript functions (write your own taggers with url fetching and custom parameters)[2] - dump range of raw audiodata bytes (e.g. to calculate hashes) Examples: # dump metadata for input.mp3 with json query tone dump "input.mp3" --format json --query "$.meta.album" # dump all files in audio-directory/, but only album and artist tone dump audio-directory/ --include-extension mp3 --format ffmetadata --include-property album --include-property artist # change title tag tone tag input.mp3 --meta-title "a title" # change tags of an audio book directory based on directory names tone tag --auto-import=covers --auto-import=chapters --path-pattern="audiobooks/%g/%a/%s/%p - %n.m4b" --path-pattern="audiobooks/%g/%a/%z/%n.m4b" audiobooks/ --dry-run I know not many people are tagging their audio files these days, but hey, it's a pet project, so feedback is very welcome. 1: https://github.com/sandreas/tone 2: https://github.com/sandreas/tone?tab=readme-ov-file#custom-scripted-taggers-experimental

Visit product View on Hacker NewsOct 24, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: para, including · Missing: supports, reddit linkedin, podcasting

78%78% 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.

TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io, including · Missing: https docs, excited, just released

29%29% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: new, single · Missing: mac, agents, macos

24%24% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

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