This is the official repository of the CompoNet baseline defined in COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations. The code for the proper COCOLA model can be found at https://github.com/gladia-research-group/cocola.
conda create --name componet python=3.11
conda activate componet
pip install -r requirements.txt| Model Checkpoint | Train Dataset | Train Config | Description |
|---|---|---|---|
| musdb-conditional_epoch=423.ckpt | MusDB | exp/train_musdb_conditional.yaml |
CompoNet model trained on MusDB dataset using AudioLDM2-large as base model, finetuing ControlNet adapter. |
| moisesdb-conditional_epoch=250.ckpt | MoisesDB | exp/train_moisesdb_conditional.yaml |
CompoNet model trained on MoisesDB dataset using AudioLDM2-large as base model, finetuing ControlNet adapter. |
| slakh-conditional_epoch=93.ckpt | Slakh2100 | exp/train_slakh_conditional_attentions.yaml |
CompoNet model trained on Slakh2100 dataset using AudioLDM2-large as base model, finetuing ControlNet adapter and UNet cross-attentions. |
Inference can be performed using inference.ipynb. The model is first instantiated and the checkpoint loaded. Specify
the model config (the Train Config in the table above without .yaml extension) as exp_cfg and checkpoint path in ckpt_path.
Then, load your input with:
y, sr = torchaudio.load("in.wav") # load you audio inputAnd specify the inference prompt.
Full example with musdb-conditional:
import hydra
import torch
import torchaudio
exp_cfg = "train_musdb_conditional"
ckpt_path = "../ckpts/musdb-conditional_epoch=423.ckpt"
with hydra.initialize(config_path="..", version_base=None):
cond_cfg = hydra.compose(config_name="config", overrides=[f'exp={exp_cfg}'])
model = hydra.utils.instantiate(cond_cfg["model"])
ckpt = torch.load(ckpt_path, map_location="cpu")
model.load_state_dict(ckpt['state_dict'], strict=False)
model = model.cuda()
y, sr = torchaudio.load("in.wav") # load you audio input
prompt = "in: other_1, vocals_1; out: vocals_1"
assert sr == 16000
y = torch.clip(y, -1, 1)
y_melspec = model.stft.cpu().mel_spectrogram(y.cpu())[0]
y_latents = model.vae.encode(y_melspec.cuda().permute(0, 2, 1).unsqueeze(1)).latent_dist.sample()
y_latents = y_latents * model.vae.config.scaling_factor
samples = model.pipeline([prompt], num_inference_steps=150,
guidance_scale=1.0, audio_length_in_s=10.23, controlnet_cond=y_latents.cuda()).audios
torchaudio.save(f"out.wav", torch.tensor(samples[0]).unsqueeze(0), sample_rate=sr)For musdb-conditional and slakh-conditional the prompts do not have a genre attribute. For example:
prompt = "in: other_1, vocals_1; out: vocals_1"For moisesdb-conditional you have to specify a lowercase genre (e.g., pop, rock) preceding the input and output
tags:
prompt = "genre: pop; in: guitar_1, vocals_1; out: other_keys_1, drums_1"The available stem tags for musdb-conditional are
STEMS = ['bass', 'drums', 'vocals', 'other']The available stem tags for slakh-conditional are
STEMS = ['bass', 'drums', 'guitar', 'piano']The available stem and genre tags for moisesdb-conditional are
STEMS = ['bass', 'bowed_strings', 'drums', 'guitar', 'other', 'other_keys', 'other_plucked', 'percussion', 'piano', 'vocals', 'wind']
GENRES = ['blues', 'bossa_nova', 'country', 'electronic', 'jazz', 'musical_theatre', 'pop', 'rap', 'reggae', 'rock', 'singer_songwriter', 'world_folk']For training first copy .env.tmp and remove the .tmp extension. Then modify the resulting .env file changing the
following fields with your wandb data:
WANDB_PROJECT=wandbprojectname
WANDB_ENTITY=wandbuser
WANDB_API_KEY=wandbapikey
Training then can be run by calling train.py with the desired experiment. Data has to be provided as webdataset
shards: scripts for sharding the datasets will be provided. You also have to specify a TAG describing the experiment.
TAG=moisesdb-conditional python train.py exp=train_moisesdb_conditional datamodule.train_dataset.path=data/moisesdb/{0..18}.tar datamodule.val_dataset.path=data/moisesdb/19.tar
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