heartmula

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

Updated May 3, 2026
One-click install
npx skills add https://github.com/80portisfound/vibe-learning --skill heartmula-80portisfound
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: heartmula
Source: https://github.com/80portisfound/vibe-learning/tree/main/packages/hermes/skills/media/heartmula
Command: npx skills add https://github.com/80portisfound/vibe-learning --skill heartmula-80portisfound

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 the installation, patching, and execution of HeartMuLa, an open-source music generation model family, so you can produce songs locally from lyrics and tags. ## Core Features & Use Cases - Lyrics-to-Song Generation: Produces full MP3 songs (48kHz stereo) from bracketed lyric structures and comma-separated style tags using 3B/7B models. - Dependency & Patch Guidance: Includes required fixes for transformers 5.x RoPE cache issues and HeartCodec checkpoint loading mismatches. - Hardware Flexibility: Supports lazy loading for 8GB VRAM GPUs, multi-GPU splitting, and CPU fallback. - Use Case: A musician wants an open-source Suno alternative to prototype song ideas locally; this Skill walks through cloning heartlib, downloading checkpoints, and generating a 4-minute track from a lyrics file. ## Quick Start Ask the assistant to install HeartMuLa and generate a song from your lyrics file with tags like 'piano,happy,wedding' using the 3B model with lazy loading enabled.

Frequently Asked Questions about heartmula

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

FAQPage Schema
How do I generate music from lyrics with HeartMuLa?▼

Clone the heartlib repository, install it in a Python 3.10 virtual environment, download the HeartMuLa and HeartCodec checkpoints, then run examples/run_music_generation.py with paths to your lyrics file, tags file, and model directory. Lyrics use bracketed section tags like [Verse] and [Chorus].

What GPU do I need for HeartMuLa music generation?▼

HeartMuLa requires a minimum of 8GB VRAM using the --lazy_load true flag, which peaks around 6.2GB with the 3B model. 16GB or more is recommended, and you can split models across GPUs with --mula_device and --codec_device flags.

Can HeartMuLa run on CPU or macOS?▼

CPU mode works via --mula_device cpu --codec_device cpu but is extremely slow, taking 30-60+ minutes per song with 12GB+ RAM needed. Triton is unavailable on macOS, so GPU acceleration requires Linux with an NVIDIA GPU.

Why does HeartMuLa fail with newer transformers versions?▼

The pinned dependencies conflict with current packages, and transformers 5.x skips RoPE cache building during meta-device loading. You must upgrade datasets and transformers, then patch modeling_heartmula.py to reinitialize Llama3ScaledRoPE caches and add ignore_mismatched_sizes=True to HeartCodec loading.

How long does HeartMuLa take to generate a song?▼

Generation runs at roughly real-time on GPU, so a 4-minute song takes about 4 minutes. Output is MP3 at 48kHz stereo, 128kbps, with maximum length controlled by --max_audio_length_ms (default 240 seconds).