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Audio Modelling #9
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MLRequires machine-learning knowledge (can be built up on the fly)Requires machine-learning knowledge (can be built up on the fly)researchCreative project that might fail but could give high returnsCreative project that might fail but could give high returns
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MLRequires machine-learning knowledge (can be built up on the fly)Requires machine-learning knowledge (can be built up on the fly)researchCreative project that might fail but could give high returnsCreative project that might fail but could give high returns
There are multiple ways we could go about modelling audio. For example, we could tokenise sounds or audio snippets and autoregressively predict the next token. Whether the audio tokens come from a VQGAN or discrete Fourier transformation doesn't matter to the model but could change the performance of our generation a lot. This issue is about finding out how to model sound and develop an end-to-end pipeline to develop a prototype and see how it works.