NOTE: Stemgen currently doesn't have a stable release. Please use carefully!
Stemgen is a library and tool that can be used to generate NI stem files from most audio files. It is inspired from the the tool of the same name Stemgen. Here is how it compares:
Under the hood, it uses:
Currently, the tool was only tested on
linux/amd64
. All used dependency are meant to be cross platform, but some additional work my be required to get it working natively. Please open a issue if your platform isn't supported
pip install -e "git+https://github.com/acolombier/stemgen.git@0.2.0#egg=stemgen"
# Install FFmpeg and TagLib 2.0
sudo apt install -y ffmpeg cmake libutfcpp-dev
wget -O taglib.tar.gz https://github.com/taglib/taglib/releases/download/v2.0.1/taglib-2.0.1.tar.gz
tar xf taglib.tar.gz
cd taglib-2.0.1
cmake -DCMAKE_INSTALL_PREFIX=/usr \
-DCMAKE_BUILD_TYPE=Release \
-DBUILD_SHARED_LIBS=ON .
make -j
sudo make install
cd ..
rm -rf taglib-2.0.1 taglib.tar.gz
Usage: stemgen [OPTIONS] FILES... OUTPUT
Generate a NI STEM file out of an audio stereo file.
FILES path(s) to a file supported by the FFmpeg codec available on your
machine
OUTPUT path to an existing directory where to store the generated STEM
file(s)
Options:
--model <model_name> Demucs model.
--device <cpu or cuda> Device for the demucs model inference
--ext TEXT Extension for the STEM file
--force Proceed even if the output file already
exists
--verbose Display verbose information which may be
useful for debugging
--repo DIRECTORY The local directory to use to fetch models
for demucs.
--model TEXT The model to use with demucs. Use --list-
models to list the supported models. Default
to htdemucs fine-trained
--shifts INTEGER Number of random shifts for equivariant
stabilization to use for demucs. Increase
separation time but improves quality for
Demucs. 10 was used in the original paper.
--overlap FLOAT Overlap between the splits to use for
demucs.
--jobs INTEGER The number of jobs to use for demucs.
--use-alac / --use-aac The codec to use for the stem stream stored
in the output MP4.
--drum-stem-label <label> Custom label for the drum STEM (the first
one)
--drum-stem-color <hex-color> Custom color for the drum STEM (the first
one)
--bass-stem-label <label> Custom label for the drum STEM (the second
one)
--bass-stem-color <hex-color> Custom color for the drum STEM (the second
one)
--other-stem-label <label> Custom label for the drum STEM (the third
one)
--other-stem-color <hex-color> Custom color for the drum STEM (the third
one)
--vocal-stem-label <label> Custom label for the drum STEM (the fourth
and last one)
--vocal-stem-color <hex-color> Custom color for the drum STEM (the fourth
and last one)
--list-models List detected and supported models usable by
demucs and exit
--version Display the stemgen version and exit
--help Show this message and exit.
Simple usage
stemgen "Artist - Title.mp3" .
Using htdemucs_ft
for better result, but more memory usage (see
the benchmark section)
stemgen "Artist - Title.mp3" . --model htdemucs_ft
NI recommends using the following labels for the stem:
Benchmarks are performed with a 3m30s song with CUDA, running on the following machine spec:
12th Gen Intel(R) Core(TM) i7-12700H
64 GB RAM
NVIDIA GeForce RTX 3050
Samsung 980 PRO SSD
Model | Memory usage peak | Real time |
---|---|---|
htdemucs (default) |
1.8 GB | 1m6.427s |
htdemucs_ft |
3.3 GB | 32.637s |
If you don't want to install stemgen
on your machine, you can use the Docker
container. Here the simple way to use it:
docker run \
-v /path/to/folder:/path/to/folder \
-it --rm \
aclmb/stemgen:0.2.0 \
/path/to/folder/Artist\ -\ Title.mp3 \
/path/to/folder
if you want to use CUDA acceleration, and cache the model not to download it every time, you can do the following:
docker run \
-v /path/to/folder:/path/to/folder \
-v stemgen_torch_cache:/root/.cache/torch/hub/ \
-it --gpus --rm \
aclmb/stemgen:0.2.0 \
/path/to/folder/Artist\ -\ Title.mp3 \
/path/to/folder
Stemgen is released under a MIT license. stembox
, which is a
component of Stemgen used to generate stem manifest is released under a
LGPL License as it reuse battle-tested code from TagLib