Open-Source Music Generation: Text-to-Music & Lyrics-to-Song - AI Build & Learn

​Welcome to AI Build & Learn, a weekly AI engineering stream where we pick a new topic and learn by building together.

• RSVP on the Luma to get direct live links and better reminders
• You can always find the live on YouTube: https://www.youtube.com/@sagecodes/streams

​This event is about generating music and audio with AI. As with the image and video events, there's no single model we're locked into — the point is to explore what's out there and actually try a few. We'll focus on open-source models, but you're welcome to bring commercial ones (Suno, Udio, and friends) if you want to compare — worth noting there isn't a fully open-source Suno equivalent yet, though the gap is closing.

​We'll look at the two main flavors: text-to-music (instrumental / sound design from a prompt) and lyrics-to-song (full tracks with vocals and accompaniment). Under the hood these lean on the same diffusion and transformer/language-model approaches as image and video, applied to audio. I'll research and try some of the best open-source options ahead of the stream, and we'll talk through the practical tradeoffs: quality, track length, controllability, speed, and licensing.

​Some things to look up to get started:
​Open-source models:

• ​YuE (YuE AI): lyrics-to-song — full tracks up to ~5 min with synchronized vocals and accompaniment
• ​ACE-Step: fast and controllable — a ~4-min song in seconds; diffusion + linear-transformer design
• ​MusicGen (Meta / AudioCraft): versatile text-to-music with melody conditioning (note: CC BY-NC — non-commercial output license)
• ​Stable Audio Open (Stability AI): great for ambient/textural audio, SFX, and samples (short clips, not full songs)

​Tooling:

• ​AudioCraft (Meta) — MusicGen / AudioGen: https://github.com/facebookresearch/audiocraft
• ​Hugging Face — audio models and pipelines: https://huggingface.co/models?pipeline_tag=text-to-audio

​​​Resources

• ​​​GitHub: https://github.com/sagecodes/ai-build-and-learn
• ​​​Events Calendar: https://luma.com/ai-builders-and-learners
• ​​​Slack (Discuss during the week): https://slack.flyte.org/
• ​​​Hosted by Sage Elliott: https://www.linkedin.com/in/sageelliott/

​​In this stream

• Intro to topic
• ​​​​Community Discussion
• Practical examples

​​​Community challenge (optional)
​​​Try spending 30–90 minutes during the week learning or building something related to the topic, then share what you’re working on in Slack.

​​​Note on Flyte / Union
​​​You may see Flyte used in some demos. Flyte is an open-source AI orchestration platform maintained by Union (where I work) for building scalable, durable, and observable AI workflows. You do not need to use Flyte to participate.

• ​​​Union: https://www.union.ai/
• ​​​Flyte: https://flyte.org/

​​​Drop a comment with ideas for future topics (agents, RAG, MLOps, robotics, frameworks, and more).