Building a Playlist Generator with Sentence Transformers

A short while ago I published a playlist generator that I’d built using Sentence Transformers and Gradio, and I followed that up with a reflection on how I try to use my projects as effective learning experiences. But how did I actually build the playlist generator? In this post we’ll break down that project and look at two technical details: how the embeddings were generated, and how the multi-step Gradio demo was built.

As we’ve explored in previous posts on the Hugging Face blog, Sentence Transformers (ST) is a library that gives us tools to generate sentence embeddings, which have a variety of uses. Since I had access to a dataset of song lyrics, I decided to leverage ST’s semantic search functionality to generate playlists from a given text prompt. Specifically, the goal was to create an embedding from the prompt, use that embedding for a semantic search across a set of pre-generated lyrics embeddings to generate a relevant set of songs. This would all be wrapped up in a Gradio app using the new Blocks API, hosted on Hugging Face Spaces.

We’ll be looking at a slightly advanced use of Gradio, so if you’re a beginner to the library I recommend reading the Introduction to Blocks before tackling the Gradio-specific parts of this post. Also, note that while I won’t be releasing the lyrics dataset, the lyrics embeddings are available on the Hugging Face Hub for you to play around with. Let’s jump in! 🪂

Sentence Transformers: Embeddings and Semantic Search

Embeddings are key in Sentence Transformers! We’ve learned about what embeddings are and how we generate them in a previous article, and I recommend checking that out before continuing with this post.

To actually generate the embeddings, you can call the .encode() method of the Sentence Transformers model and pass it a list of strings. Then you can save the embeddings however you like – in this case I opted to pickle them.