Awesome
Docker Facebook Demucs
This repository dockerizes Facebook Demucs to split music tracks into different tracks (bass, drums, voice, others).
Usage
Clone this repository
git clone https://github.com/xserrat/docker-facebook-demucs.git demucs
Split a music track
- Copy the track you want to split into the
input
folder (e.g.,input/mysong.mp3
). - Execute
demucs
via therun
job in theMakefile
, specifying thetrack
argument with only the name of the file:
make run track=mysong.mp3
This process will take some time the first time it is run, as the execution will:
- Download the Docker image that is setup to run the
facebook demucs
script. - Download the pretrained models.
- Execute
demucs
to split the track.
Subsequent runs will not need to download the Docker image or download the models, unless the model specified has not yet been used.
Options
The following options are available when splitting music tracks with the run
job:
Option | Default Value | Description |
---|---|---|
gpu | false | Enable Nvidia CUDA support (requires an Nvidia GPU). |
model | demucs | The model used for audio separation. See https://github.com/facebookresearch/demucs#separating-tracks for a list of available models to use. |
mp3output | false | Output separated audio in mp3 format instead of the default wav format. |
splittrack | Individual track to split/separate from the others (e.g., you only want to separate drums). Valid options are bass , drums , vocals and other . Other values may be allowed if the model can separate additional track types. |
Example commands:
# Use the "fine tuned" demucs model
make run track=mysong.mp3 model=htdemucs_ft
# Enable Nvidia CUDA support and output separated audio in mp3 format
make run track=mysong.mp3 gpu=true mp3output=true
Run Interactively
To experiment with other demucs
options on the command line, you can also run the Docker image interactively via the run-interactive
job. Note that only the gpu
option is applicable for this job.
Example:
make run-interactive gpu=true
Building the Image
The Docker image can be built locally via the build
job:
make build
License
This repository is released under the MIT license as found in the LICENSE file.