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it is as good as the models are. it is not perfect, especially for non-major languages, but it works.

one of my favorite artists and this one is one of my favorite tracks in general. cheers!

hi!

1. Maxwell+ should work well 3. I would need to explore, you can join the discord or the mailing list on the website!

cheers!

hi! answered here https://github.com/rzru/nightingale/issues/7

hey, thanks for the feedback, both noted!

so, as answered in one of the other threads - for me the transcript model performed poorly back in the days, however since then I've added lyrics lookup and tweaked a bunch of parameters, so it is worth trying!

it's a false positive, however feel free to build from the source while I'm figuring out what the virus trigger is about

thanks, share your feedback afterwards!

I'm not sure whether the analysis would work swiftly enough on steam deck, it might be better to analyze on a desktop device and transfer ~/.nightingale/songs to the steam deck. just saying!

it's a false positive, however feel free to build from the source while I'm figuring out what the virus trigger is about

noted, mate, and thanks for the feedback, really!

I'm not experienced in building desktop apps per-se, so I went with the thing that looked reasonable to me. all your comments are valid tho. I'll take a look how can I resolve this in the future.

cheers!

I'm just using cross https://github.com/cross-rs/cross to build cross-arch. you can still use `cargo build`, it will just work.

Simply because I was not aware of this option, that's it. Happy to consider it and also happy to receive contributions.

hey both!

thanks for your feedback and reports, I'd be happy if they are added as issues on github.

as said in the separate comment, I really wanted an app to be as "grandma-proof" as possible, therefore I really wanted to have one binary that does the magic for you. it's a karaoke app, not a tool that is aimed at engineers.

we can indeed look at the local packages before downloading an executable, it's just not done yet but might be added in the future.

I've built this project out of passion and it's 100% open-source and free, so please keep this in mind when criticizing.

hi there! pitch scoring is now pretty simplistic and the feature you're describing is not implemented. it's a great feature request tho!

indeed! the stem separation model is not ideal. you can try to change the stem separation model in the settings and reanalyze the song (make sure to click the trash button and then analyze, refresh button does not refresh the stems, only the transcript/alignment)

I haven't tried Mandarin and Cantonese, but tried Japanese. back at that time, it performed poorly. however, I've tweaked a bunch of settings since then, so maybe it has changed. Hanzi is a supported font and can be output, but the transcript/alignment quality might not be the best

No joke, feel free to try it! The beauty of the approach is that you don't have any limitations. Just be prepared to degraded experience, sometimes models struggle even with the simplest pop tracks :D

also model only affects the transcript job (I need to make it clearer in the UI). For the alignment, it's a single model provided by whisperx

I've just made it public a few days back :)

need more pioneers to try it out and spread the word!

not yet, but it's a great feature request!

hey, thanks! would be happy if you open it as issue on gh

oof, since it uses quite heavy models locally I would expect it to struggle on such hardware. M1+ macs & nvidia gpus work the best and fastest.

hey!

you can use + / - buttons on the keyboard to change the level of guidance according to your preference, generally there is a controls legend in the top right corner

https://nightingale.cafe/docs/controls

please try it out and share your feedback afterwards!

both transcript/alignment might not work perfectly, but it really depends on the song

stay tuned for more features!

hey mate! thanks for your feedback.

indeed, I'm running to two problems on the analyzer side: 1. align model sliding off (especially w/ chorus/back vocals present) 2. transcript skipping parts of lyrics in lyrics-heavy tracks (I tried a lot of russian rap, lol)

happy for contributions as I'm not that experienced w/ machine learning side of the project, mostly it was emperical "tweak the parameters and look what is changed"

I've been working on a karaoke app called Nightingale. You point it at your music folder and it turns your songs into karaoke - separates vocals from instrumentals, generates word-level synced lyrics, and lets you sing with highlighted lyrics and pitch scoring. Works with video files too.

Everything runs locally on your machine, nothing gets uploaded. No accounts, no subscriptions, no telemetry.

It ships as a single binary for Linux, macOS, and Windows. On first launch it sets up its own isolated Python environment and downloads the ML models it needs - no manual installation of dependencies required.

My two biggest drivers for the creation of this were:

    The lack of karaoke coverage for niche, avant-garde, and local tracks.

    Nostalgia for the good old cheesy karaoke backgrounds with flowing rivers, city panoramas, etc.

Some highlights:

    Stem separation using the UVR Karaoke model (preserves backing vocals) or Demucs

    Automatic lyrics via WhisperX transcription, or fetched from LRCLIB when available

    Pitch scoring with player profiles and scoreboards

    Gamepad support and TV-friendly UI scaling for party setups

    GPU acceleration on NVIDIA (CUDA) and Apple Silicon (CoreML/MPS)

    Built with Rust and the Bevy engine

The whole stack is open source. No premium tier, no "open core" - just the app.

Feedback and contributions welcome.