---
license: cc-by-nc-4.0
tags:
  - audiocraft
  - melodyflow
  - music-generation
  - music-editing
  - flow-matching
language:
  - en
---

# MelodyFlow — AEmotionStudio mirror

1:1 mirror of [facebook/melodyflow-t24-30secs](https://huggingface.co/facebook/melodyflow-t24-30secs). Used by the MAESTRO / Æmotion Studio AI Workstation's **MelodyFlow** panel (Design → MelodyFlow).

## License — Non-Commercial

**Weights:** CC-BY-NC-4.0. Generated outputs may NOT be used in commercial projects, paid releases, or client work.

**Code (audiocraft):** MIT. MelodyFlow's inference code lives in the [`facebook/MelodyFlow`](https://huggingface.co/spaces/facebook/MelodyFlow) HuggingFace Space — Meta uploaded it there but never merged it into audiocraft `main`. MAESTRO vendors that Space's `audiocraft/` subtree under `backend/ai/melodyflow_pkg/`. The non-commercial clause attaches only to the weights and to anything derived from running them.

## Format

This mirror keeps the upstream `.bin` layout (PyTorch pickle) verbatim — `state_dict.bin` (the flow-matching DiT language model) plus `compression_state_dict.bin` (the EnCodec compression model, 2-channel / 32 kHz). We do NOT convert to safetensors here because the vendored audiocraft loader expects pickled `{xp.cfg, best_state}` packages and reads the OmegaConf cfg blob alongside the tensor dict in one `torch.load` call. Splitting cfg into a sidecar would require a custom loader — deferred.

PyTorch 2.6+'s default `weights_only=True` rejects these pickles (numpy scalars in `xp.cfg`). MAESTRO's runner wraps the load in a `_TorchLoadWeightsOnlyShim` context manager; vanilla audiocraft users on torch ≥ 2.6 will hit the same issue and need a similar shim.

## Loading

```python
# Requires the facebook/MelodyFlow Space's audiocraft subtree on PYTHONPATH
# (the upstream audiocraft PyPI release does NOT include MelodyFlow).
from audiocraft.models import MelodyFlow
model = MelodyFlow.get_pretrained('AEmotionStudio/melodyflow-models', device='cuda')

# Generate from text alone:
model.set_generation_params(solver='midpoint', steps=64, duration=10.0)
wav = model.generate(descriptions=['cinematic strings'])

# OR edit a source clip via regularized latent inversion:
import torchaudio
src, sr = torchaudio.load('source.wav')   # MelodyFlow's EnCodec is stereo
if src.shape[0] == 1: src = src.repeat(2, 1)
src = src.unsqueeze(0).to('cuda')
import torch
with torch.no_grad():
    prompt_tokens = model.encode_audio(src)
model.set_editing_params(solver='euler', steps=25, regularize=True,
                          regularize_iters=4, lambda_kl=0.2)
edited = model.edit(prompt_tokens=prompt_tokens,
                     descriptions=['solo piano with reverb'],
                     src_descriptions=['gentle arpeggio'])
torchaudio.save('edited.wav', edited[0].cpu(), model.sample_rate)
```

## Citation

MelodyFlow is described in:

> Le Lan, G., Nagaraja, V., Chang, E., Kant, D., Ni, Z., Shi, Y., Iandola, F., & Chandra, V. (2024). **High Fidelity Text-Guided Music Editing via Single-Stage Flow Matching**. arXiv:2407.03648.