alt Hacker NewsI'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.
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
Feedback and contributions welcome.
Replies
Amazing work! I am thrilled someone was motivated to approach this problem and develop a creative solution like this. There are very limited options for Karaoke, especially in the FOSS space. Most Karaoke apps are super limited and that's driven many Karaoke enjoyers I know to YouTube in search of the songs they want to sing. This solution would give them the power to do even more songs, even better than what's out there now!
Questions for you:
1. What CUDA capability level is necessary for Nvidia GPU accelleration to work?
3. Are there any plans to support iGPU/NPU accelleration on AMD and Intel? Asking because those chips are most common in the mini computers sold at low cost these days.
My family members who love Karaoke and will be happy to try this. Looking forward to it!
I studied signal processing in university and my career evolved to not use what I studied. Decades ago, giving an algorithm a sound file and isolating tracks was difficult.
How does your implementation accomplish this? Were you involved or did you use something off the shelf?
Edit: ah, using neural nets, demucs. I wonder if there is pure math approach that can compete?
Can you bypass the LRCLIB lookup and always use Whisper?
Excited to try this out. How well does WhisperX deal with lyrics in say Mandarin or Cantonese? Does it output Hanzi?
how's this different from ultrastar singer?
https://github.com/rakuri255/UltraSinger
has anyone compared the quality of the two?
Struggled somewhat with Tjamuku Ngurra by the Tjintu Desert Band, absolutely nailed Mariah Carey's Ken Lee.
I just want to say how much I love that you used Dean Blunt in the example video
This looks like awesome awesome fun! Will let you know how it runs. What a wonderful idea <3
Just tried No_4mat's 1992... unfortunately it didn't work :(
Just tried it with B.E.D - Walk Away[0], unfortunately it lost track of the lyrics after 30 secs (Model is "large-v3"). Will play around a bit more, as it would be great to have a working karaoke generator.
Some quick feedback:
Thanks for keeping it FOSS![0]: https://www.youtube.com/watch?v=_MFT4H3VoNE