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heartmula

Generate full songs from lyrics and style tags using HeartMuLa open-source music models.

Updated Sep 10, 2026

Search Tags:#audio #gpu #music generation #open-source #lyrics #heartmula #ai songs

One-click install

npx skills add https://github.com/loteiron/ZeusAgent --skill heartmula-loteiron

Or copy as Structured Prompt for Agent▼

Please help me install this Agent Skill.
Skill: heartmula
Source: https://github.com/loteiron/ZeusAgent/tree/main/optional-skills/creative/heartmula
Command: npx skills add https://github.com/loteiron/ZeusAgent --skill heartmula-loteiron

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Generating complete songs with vocals from text requires either paid services like Suno or complex local model setups. This Skill guides you through installing, patching, and running HeartMuLa, an open-source music foundation model family, so you can produce full songs locally from lyrics and style tags. ## Core Features & Use Cases - Song Generation from Lyrics + Tags: Produce MP3 songs (48kHz stereo) conditioned on bracketed lyrics and comma-separated style tags using the 3B or 7B HeartMuLa models. - Complete Setup Guidance: Covers cloning heartlib, creating a Python 3.10 environment, fixing dependency conflicts, and applying required source patches for transformers 5.x compatibility. - Hardware-Aware Configuration: Supports lazy loading for 8GB VRAM GPUs, multi-GPU splitting, and CPU fallback with clear performance expectations. - Use Case: A musician wants a local Suno alternative to prototype song ideas. Provide lyrics with [Verse]/[Chorus] structure and tags like "piano,happy,wedding" to generate a 4-minute MP3 in roughly 4 minutes on a single GPU. ## Quick Start Ask the agent to install HeartMuLa and generate a song from your lyrics file and style tags, saving the result as an MP3.

Frequently Asked Questions about heartmula

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate a song from lyrics with AI locally?▼

Install the heartlib repository, download the HeartMuLa and HeartCodec checkpoints, then run run_music_generation.py with a lyrics file using [Verse]/[Chorus] tags and a comma-separated tags file. Output is a 48kHz stereo MP3 generated at roughly real-time speed on GPU.

What open-source alternative to Suno exists for music generation?▼

HeartMuLa is an Apache-2.0 open-source family of music foundation models comparable to Suno. It generates full songs conditioned on lyrics and style tags, with multilingual support, using the HeartMuLa language model and HeartCodec audio codec.

How much VRAM does HeartMuLa need to run?▼

HeartMuLa requires a minimum of 8GB VRAM with lazy loading enabled, which peaks around 6.2GB for the 3B model. 16GB or more is recommended for comfortable use, and multi-GPU setups can split the model and codec across devices.

Can HeartMuLa run on CPU or without an NVIDIA GPU?▼

Yes, CPU mode works via --mula_device cpu --codec_device cpu, but generation is extremely slow, potentially 30-60+ minutes per song, and needs 12GB+ free RAM. Without a GPU, a cloud GPU service or the online demo is recommended.

Why does HeartMuLa fail after installing with newer transformers?▼

The pinned dependencies conflict with newer packages, requiring upgrades to datasets and transformers plus two source patches: RoPE cache reinitialization in modeling_heartmula.py and ignore_mismatched_sizes=True in HeartCodec loading calls.

What are the limitations of HeartMuLa music generation?▼

Style tags may be ignored since lyrics tend to dominate conditioning, Triton GPU acceleration is unavailable on macOS, RTX 5080 incompatibility has been reported, and bf16 for HeartCodec degrades audio quality so fp32 should be used.

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