Show HN: AI audio embeddings - Discover 100M+ iTunes songs with language models

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/ 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 optimised for our preferred music; electronic, techno, ambient, dub and relaxing etc. We’re currently fine-tuning to handle all kinds of genres and moods. Our model enables two types of discovery: (a) natural language search and (b) similarity search. (a) Speak: search for music using freeform natural language prompts. Describe the mood, aesthetic, texture, setting and context of a track. (b) Music: discover tracks that are acoustically similar to ANY reference track from Apple Music. We recently presented at Sonar+D in Barcelona. We hope you like it. Let us know what you think!