| name | community-heartmula |
| description | Set up and run HeartMuLa, the open-source music generation model family (Suno-like). Generates full songs from lyrics + tags with multilingual support. |
| version | 1.0.0 |
| metadata | {"community":{"tags":["music","audio","generation","ai","heartmula","heartcodec","lyrics","songs"],"related_skills":["audiocraft"]},"upstream_import":{"original_name":"heartmula","source":"community catalog active profile ~/.argent/skills"}} |
HeartMuLa - Open-Source Music Generation
Overview
HeartMuLa is a family of open-source music foundation models (Apache-2.0) that generates music conditioned on lyrics and tags. Comparable to Suno for open-source. Includes:
- HeartMuLa - Music language model (3B/7B) for generation from lyrics + tags
- HeartCodec - 12.5Hz music codec for high-fidelity audio reconstruction
- HeartTranscriptor - Whisper-based lyrics transcription
- HeartCLAP - Audio-text alignment model
When to Use
- User wants to generate music/songs from text descriptions
- User wants an open-source Suno alternative
- User wants local/offline music generation
- User asks about HeartMuLa, heartlib, or AI music generation
Hardware Requirements
- Minimum: 8GB VRAM with
--lazy_load true (loads/unloads models sequentially)
- Recommended: 16GB+ VRAM for comfortable single-GPU usage
- Multi-GPU: Use
--mula_device cuda:0 --codec_device cuda:1 to split across GPUs
- 3B model with lazy_load peaks at ~6.2GB VRAM
Installation Steps
1. Clone Repository
cd ~/
git clone https://github.com/HeartMuLa/heartlib.git
cd heartlib
2. Create Virtual Environment (Python 3.10 required)
uv venv --python 3.10 .venv
. .venv/bin/activate
uv pip install -e .
3. Fix Dependency Compatibility Issues
IMPORTANT: As of Feb 2026, the pinned dependencies have conflicts with newer packages. Apply these fixes:
uv pip install --upgrade datasets
uv pip install --upgrade transformers
4. Patch Source Code (Required for transformers 5.x)
Patch 1 - RoPE cache fix in src/heartlib/heartmula/modeling_heartmula.py:
In the setup_caches method of the HeartMuLa class, add RoPE reinitialization after the reset_caches try/except block and before the with device: block:
from torchtune.models.llama3_1._position_embeddings import Llama3ScaledRoPE
for module in self.modules():
if isinstance(module, Llama3ScaledRoPE) and not module.is_cache_built:
module.rope_init()
module.to(device)
Why: from_pretrained creates model on meta device first; Llama3ScaledRoPE.rope_init() skips cache building on meta tensors, then never rebuilds after weights are loaded to real device.
Patch 2 - HeartCodec loading fix in src/heartlib/pipelines/music_generation.py:
Add ignore_mismatched_sizes=True to ALL HeartCodec.from_pretrained() calls (there are 2: the eager load in __init__ and the lazy load in the codec property).
Why: VQ codebook initted buffers have shape [1] in checkpoint vs [] in model. Same data, just scalar vs 0-d tensor. Safe to ignore.
5. Download Model Checkpoints
cd heartlib
hf download --local-dir './ckpt' 'HeartMuLa/HeartMuLaGen'
hf download --local-dir './ckpt/HeartMuLa-oss-3B' 'HeartMuLa/HeartMuLa-oss-3B-happy-new-year'
hf download --local-dir './ckpt/HeartCodec-oss' 'HeartMuLa/HeartCodec-oss-20260123'
All 3 can be downloaded in parallel. Total size is several GB.
GPU / CUDA
HeartMuLa uses CUDA by default (--mula_device cuda --codec_device cuda). No extra setup needed if the user has an NVIDIA GPU with PyTorch CUDA support installed.
- The installed
torch==2.4.1 includes CUDA 12.1 support out of the box
torchtune may report version 0.4.0+cpu — this is just package metadata, it still uses CUDA via PyTorch- To verify GPU is being used, look for "CUDA memory" lines in the output (e.g. "CUDA memory before unloading: 6.20 GB")
- No GPU? You can run on CPU with
--mula_device cpu --codec_device cpu, but expect generation to be extremely slow (potentially 30-60+ minutes for a single song vs ~4 minutes on GPU). CPU mode also requires significant RAM (~12GB+ free). If the user has no NVIDIA GPU, recommend using a cloud GPU service (Google Colab free tier with T4, Lambda Labs, etc.) or the online demo at https://heartmula.github.io/ instead.
Usage
Basic Generation
cd heartlib
. .venv/bin/activate
python ./examples/run_music_generation.py \
--model_path=./ckpt \
--version="3B" \
--lyrics="./assets/lyrics.txt" \
--tags="./assets/tags.txt" \
--save_path="./assets/output.mp3" \
--lazy_load true
Input Formatting
Tags (comma-separated, no spaces):
piano,happy,wedding,synthesizer,romantic
or
rock,energetic,guitar,drums,male-vocal
Lyrics (use bracketed structural tags):
[Intro]
[Verse]
Your lyrics here...
[Chorus]
Chorus lyrics...
[Bridge]
Bridge lyrics...
[Outro]
Key Parameters
| Parameter | Default | Description |
|---|
--max_audio_length_ms | 240000 | Max length in ms (240s = 4 min) |
--topk | 50 | Top-k sampling |
--temperature | 1.0 | Sampling temperature |
--cfg_scale | 1.5 | Classifier-free guidance scale |
--lazy_load | false | Load/unload models on demand (saves VRAM) |
--mula_dtype | bfloat16 | Dtype for HeartMuLa (bf16 recommended) |
--codec_dtype | float32 | Dtype for HeartCodec (fp32 recommended for quality) |
Performance
- RTF (Real-Time Factor) ≈ 1.0 — a 4-minute song takes ~4 minutes to generate
- Output: MP3, 48kHz stereo, 128kbps
Pitfalls
- Do NOT use bf16 for HeartCodec — degrades audio quality. Use fp32 (default).
- Tags may be ignored — known issue (#90). Lyrics tend to dominate; experiment with tag ordering.
- Triton not available on macOS — Linux/CUDA only for GPU acceleration.
- RTX 5080 incompatibility reported in upstream issues.
- The dependency pin conflicts require the manual upgrades and patches described above.
Links
