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Products that do what HeartMuLa does
Open-source AI music foundation model at commercial quality
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Hi all, I've spent some time working on music demixing or music source separation algorithms, which take in a mixed song and output estimates of isolated components (e.g. vocals, drums, bass, other). I took a popular PyTorch model with good performance (Open-Unmix, UMX-L weights), reimplemented the inference steps in C++, and compiled it to WebAssembly for a free client-side music demixer.
2023 · sevag.xyz
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Hey HN, My cofounder and I trained an AI music generation model and after a month of testing we're launching 1.0 today. Ours is interesting because it's a latent diffusion model instead of a language model, which makes it more controllable: https://sonauto.ai/ Others do music generation by training a Vector Quantized Variational Autoencoder like Descript Audio Codec (https://github.com/descriptinc/descript-audio-codec) to turn music into tokens, then training an LLM on those tokens. Instead, we ripped the tokenization part off and replaced it with a normal…
2024 · sonauto.ai
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Hi HN, Last time I showed free-music-demixer, which people seemed to enjoy. It was a static website with a Javascript + WASM module to perform music demixing (or music source separation) using an AI model UMX-L (Open-Unmix) running client-side in the browser. Since then, I have overhauled the project and made several improvements: - The demixing/separation quality is higher now, since I implemented the missing post-processing step - Memory usage is lower now by performing a custom segmented inference with a streaming LSTM, which should allow larger tracks (or, dare I say,…
2023 · freemusicdemixer.com
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2020 · lofimusic.app
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I am excited to announce a new tool for music producers and audio enthusiasts - a music audio search engine. With just a simple description of the groove you're looking for, our semantic search engine will output the most similar audio in seconds. I used the Freesound.org API to upload over 3,000 grooves to MongoDB, and combined all the relevant data such as tags, title, description, BPM, etc. into OpenAI's Text-Davinci to generate a unique description of each sound. I then embedded these descriptions using the Ada Embeddings Model and inserted them into Pinecone DB vector database, making…
2023 · muzic-sage.vercel.app
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2022 · github.com
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We’re a group of electronic music artists and PhD researchers working in AI music. We've been working on a project to help us discover new music in a more objective way. It's called Speak Music: https://speakmusic.sonophase.com/ We’ve trained an AI model to understand the correspondence between music and language. The model combines a machine listening and audio signal processing with transformers for text embeddings. Once trained, we index a huge catalogue of unseen audio, ensuring that the search system can efficiently scale to millions of tracks. At the moment, our model is…
2023 · speakmusic.sonophase.com
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