Separation Performances

Pretrained models

The pretrained 4stems model has the following performances when tested on the musDB test set:

	Spleeter Mask	Spleeter MWF
Vocals SDR	6.55	6.86
Vocals SIR	15.19	15.86
Vocals SAR	6.44	6.99
Vocals ISR	12.01	11.95
Bass SDR	5.10	5.51
Bass SIR	10.01	10.30
Bass SAR	5.15	5.96
Bass ISR	9.18	9.61
Drums SDR	5.93	6.71
Drums SIR	12.24	13.67
Drums SAR	5.78	6.54
Drums ISR	10.50	10.69
Other SDR	4.24	4.55
Other SIR	7.86	8.16
Other SAR	4.63	4.88
Other ISR	9.83	9.87

The first column of this table can be reproduced using the following command (you need to download musdb first and to put it into the folder):

spleeter evaluate -p spleeter:4stems -o spleeter_mask_results --mus_dir <musdb path>

The second column can be reproduced using the following command:

spleeter evaluate -p spleeter:4stems -o spleeter_mwf_results --mus_dir <musdb path> -m

Training on musdb

The pre-trained models were trained on a large private dataset. It is also possible to train a model with the public musDB train dataset. To do so use the configs/musdb_config.json:

spleeter train --verbose -p configs/musdb_config.json -d <musdb path>

This should provide the following results on the musDB test dataset (note that results may differ a bit due to a not perfectly deterministic data pipeline):

	SDR	SAR	SIR	ISR
Vocals	5.10	5.44	12.45	9.58
Drums	5.15	5.25	10.68	8.89
Bass	4.27	5.42	7.23	9.38
Other	3.21	3.89	5.37	7.80

Once the model trained, the results of this table can be generated with:

spleeter evaluate -p configs/musdb_config.json -o musdb_trained_spleeter_results --mus_dir <musdb path> -m