1000sharks.xyz
free-music-demixer
1000sharks.xyz | free-music-demixer | |
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1 | 7 | |
0 | 323 | |
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10.0 | 8.0 | |
over 1 year ago | about 1 month ago | |
HTML | Python | |
MIT License | MIT License |
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1000sharks.xyz
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Show HN: Free AI-based music demixing in the browser
OK, so, tangentially related: I tried to do something once - I took small chunks of songs generated by SampleRNN in an attempt to stitch together the ones that sounded the most similar.
The script [1] uses Essentia Chromaprint [2] to "grade" the similarity of audio tracks, and combine the ones with the closest chromaprint.
I have a track on Soundcloud which uses the above technique (mashing together short generated clips by their chromagram), trained on Cannibal Corpse [3]
1: https://github.com/sevagh/1000sharks.xyz/blob/master/sampler...
2: https://essentia.upf.edu/reference/std_Chromaprinter.html
3: https://soundcloud.com/user-167126026/1000sharks-domainal-sk...
free-music-demixer
- Ask HN: What are some of the best user experiences with AI?
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Free-music-demixer adds multi-threading to run Demucs faster in the browser
Hi HN,
Over the Christmas break I added multi-threading to the WASM Demucs module in freemusicdemixer
Demucs (v4 hybrid transformer) is a much higher quality model than the previous default, but it ran very slowly when limited to one worker: ~17 minutes for an average 4-minute song
I have since implemented multi-threading with WebWorkers.
If you raise the "MAX MEMORY" setting to 16 GB or 32 GB, your track will demix within 7-5 minutes, producing state-of-the-art results.
There is also support for the Demucs 6-source model which adds piano and guitar stems.
Please reach out and be loud about any bugs or UX issues you encounter!: https://github.com/sevagh/free-music-demixer/issues
- Show HN: Improved freemusicdemixer – AI music demixing in the browser
- Show HN: Improved freemusicdemixer (AI music demixing in the browser)
- FLaNK Stack Weekly for 17 July 2023
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Show HN: Free AI-based music demixing in the browser
* Post-processing step (bigger impact)
I tried to tackle the post-processing step in my C++ code (which would win ~1 dB in quality across all targets) but it's too tricky for now [2]. Maybe some other day.
1: https://github.com/sevagh/free-music-demixer/blob/main/examp...
2: https://github.com/sigsep/open-unmix-pytorch/blob/master/ope...
What are some alternatives?
open-unmix-pytorch - Open-Unmix - Music Source Separation for PyTorch
danswer - Gen-AI Chat for Teams - Think ChatGPT if it had access to your team's unique knowledge.
EfficientAT - This repository aims at providing efficient CNNs for Audio Tagging. We provide AudioSet pre-trained models ready for downstream training and extraction of audio embeddings.
umx.cpp - C++17 port of Open-Unmix-PyTorch with streaming LSTM inference, ggml, quantization, and Eigen
heimdall - Dashboard for operating Flink jobs and deployments.
spleeter - Deezer source separation library including pretrained models.
dt - dt - duct tape for your unix pipes
demucs - Code for the paper Hybrid Spectrogram and Waveform Source Separation, but the goddamm motherfucker doesn't work.
video2dataset - Easily create large video dataset from video urls
khoj - Your AI second brain. A copilot to get answers to your questions, whether they be from your own notes or from the internet. Use powerful, online (e.g gpt4) or private, local (e.g mistral) LLMs. Self-host locally or use our web app. Access from Obsidian, Emacs, Desktop app, Web or Whatsapp.
plate - The rich-text editor for React.