metadata

title: salad bowl (vampnet)
emoji: 🥗
colorFrom: yellow
colorTo: green
sdk: gradio
sdk_version: 4.7.1
python_version: 3.9.17
app_file: app.py
pinned: false
license: cc-by-nc-4.0

VampNet

This repository contains recipes for training generative music models on top of the Descript Audio Codec.

Setting up

Requires Python 3.9.

you'll need a Python 3.9 environment to run VampNet. This is due to a known issue with madmom.

(for example, using conda)

conda create -n vampnet python=3.9
conda activate vampnet

install VampNet

git clone https://github.com/hugofloresgarcia/vampnet.git
pip install -e ./vampnet

Usage

quick start!

import random
import vampnet
import audiotools as at

# load the default vampnet model
interface = vampnet.interface.Interface.default()

# list available finetuned models
finetuned_model_choices = interface.available_models()
print(f"available finetuned models: {finetuned_model_choices}")

# pick a random finetuned model
model_choice = random.choice(finetuned_model_choices)
print(f"choosing model: {model_choice}")

# load a finetuned model
interface.load_finetuned(model_choice)

# load an example audio file
signal = at.AudioSignal("assets/example.wav")

# get the tokens for the audio
codes = interface.encode(signal)

# build a mask for the audio
mask = interface.build_mask(
    codes, signal,
    periodic_prompt=7, 
    upper_codebook_mask=3,
)

# generate the output tokens
output_tokens = interface.vamp(
    codes, mask, return_mask=False,
    temperature=1.0, 
    typical_filtering=True, 
)

# convert them to a signal
output_signal = interface.decode(output_tokens)

# save the output signal
output_signal.write("scratch/output.wav")

Launching the Gradio Interface

You can launch a gradio UI to play with vampnet.

python app.py --args.load conf/interface.yml --Interface.device cuda

Training / Fine-tuning

Training a model

To train a model, run the following script:

python scripts/exp/train.py --args.load conf/vampnet.yml --save_path /path/to/checkpoints

for multi-gpu training, use torchrun:

torchrun --nproc_per_node gpu scripts/exp/train.py --args.load conf/vampnet.yml --save_path path/to/ckpt

You can edit conf/vampnet.yml to change the dataset paths or any training hyperparameters.

For coarse2fine models, you can use conf/c2f.yml as a starting configuration.

See python scripts/exp/train.py -h for a list of options.

Debugging training

To debug training, it's easier to debug with 1 gpu and 0 workers

CUDA_VISIBLE_DEVICES=0 python -m pdb scripts/exp/train.py --args.load conf/vampnet.yml --save_path /path/to/checkpoints --num_workers 0

Fine-tuning

To fine-tune a model, use the script in scripts/exp/fine_tune.py to generate 3 configuration files: c2f.yml, coarse.yml, and interface.yml. The first two are used to fine-tune the coarse and fine models, respectively. The last one is used to launch the gradio interface.

python scripts/exp/fine_tune.py "/path/to/audio1.mp3 /path/to/audio2/ /path/to/audio3.wav" <fine_tune_name>

This will create a folder under conf/<fine_tune_name>/ with the 3 configuration files.

The save_paths will be set to runs/<fine_tune_name>/coarse and runs/<fine_tune_name>/c2f.

launch the coarse job:

python scripts/exp/train.py --args.load conf/generated/<fine_tune_name>/coarse.yml

this will save the coarse model to runs/<fine_tune_name>/coarse/ckpt/best/.

launch the c2f job:

python  scripts/exp/train.py --args.load conf/generated/<fine_tune_name>/c2f.yml

A note on argbind

This repository relies on argbind to manage CLIs and config files. Config files are stored in the conf/ folder.

Take a look at the pretrained models

All the pretrained models (trained by hugo) are stored here: https://huggingface.co/hugggof/vampnet

Licensing for Pretrained Models:

The weights for the models are licensed CC BY-NC-SA 4.0. Likewise, any VampNet models fine-tuned on the pretrained models are also licensed CC BY-NC-SA 4.0.

Download the pretrained models from this link. Then, extract the models to the models/ folder.