Repository for training models for music source separation. Repository is based on kuielab code for SDX23 challenge. The main idea of this repository is to create training code, which is easy to modify for experiments. Brought to you by MVSep.com.

Model can be chosen with --model_type arg.

Available models for training:

• MDX23C based on KUIELab TFC TDF v3 architecture. Key: mdx23c.
• Demucs4HT [Paper]. Key: htdemucs.
• VitLarge23 based on Segmentation Models Pytorch. Key: segm_models.
• TorchSeg based on TorchSeg module. Key: torchseg.
• Band Split RoFormer [Paper, Repository] . Key: bs_roformer.
• Mel-Band RoFormer [Paper, Repository]. Key: mel_band_roformer.
• Swin Upernet [Paper] Key: swin_upernet.
• BandIt Plus [Paper, Repository] Key: bandit.
• SCNet [Paper, Official Repository, Unofficial Repository] Key: scnet.
• BandIt v2 [Paper, Repository] Key: bandit_v2.

1. Note 1: For segm_models there are many different encoders is possible. Look here.
2. Note 2: Thanks to @lucidrains for recreating the RoFormer models based on papers.
3. Note 3: For torchseg gives access to more than 800 encoders from timm module. It's similar to segm_models.

To train model you need to:

1. Choose model type with option --model_type, including: mdx23c, htdemucs, segm_models, mel_band_roformer, bs_roformer.
2. Choose location of config for model --config_path <config path>. You can find examples of configs in configs folder. Prefixes config_musdb18_ are examples for MUSDB18 dataset.
3. If you have a check-point from the same model or from another similar model you can use it with option: --start_check_point <weights path>
4. Choose path where to store results of training --results_path <results folder path>

python train.py \ 
    --model_type mel_band_roformer \ 
    --config_path configs/config_mel_band_roformer_vocals.yaml \
    --start_check_point results/model.ckpt \
    --results_path results/ \
    --data_path 'datasets/dataset1' 'datasets/dataset2' \
    --valid_path datasets/musdb18hq/test \
    --num_workers 4 \
    --device_ids 0

All training parameters are here.

python inference.py \  
    --model_type mdx23c \
    --config_path configs/config_mdx23c_musdb18.yaml \
    --start_check_point results/last_mdx23c.ckpt \
    --input_folder input/wavs/ \
    --store_dir separation_results/

All inference parameters are here.

• All batch sizes in config are adjusted to use with single NVIDIA A6000 48GB. If you have less memory please adjust correspodningly in model config training.batch_size and training.gradient_accumulation_steps.
• It's usually always better to start with old weights even if shapes not fully match. Code supports loading weights for not fully same models (but it must have the same architecture). Training will be much faster.

• configs/config_*.yaml - configuration files for models
• models/* - set of available models for training and inference
• dataset.py - dataset which creates new samples for training
• inference.py - process folder with music files and separate them
• train.py - main training code
• utils.py - common functions used by train/valid
• valid.py - validation of model with metrics

If you trained some good models, please, share them. You can post config and model weights in this issue.

Note: Metrics measured on Multisong Dataset.

Note: All HTDemucs4 FT models output 4 stems, but quality is best only on target stem (all other stems are dummy).

* Note: Model was trained only on MUSDB18HQ dataset (100 songs train data)

Look here: Dataset types

Look here: Augmentations

• arxiv paper

@misc{solovyev2023benchmarks,
      title={Benchmarks and leaderboards for sound demixing tasks}, 
      author={Roman Solovyev and Alexander Stempkovskiy and Tatiana Habruseva},
      year={2023},
      eprint={2305.07489},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}