Summary: Easy to use audio stem separation from the command line or as a dependency in your own Python project, using the amazing MDX-Net, VR Arch, Demucs and MDXC models available in UVR by @Anjok07 & @aufr33.
Audio Separator is a Python package that allows you to separate an audio file into various stems, using models trained by @Anjok07 for use with UVR (https://github.com/Anjok07/ultimatevocalremovergui).
The simplest (and probably most used) use case for this package is to separate an audio file into two stems, Instrumental and Vocals, which can be very useful for producing karaoke videos! However, the models available in UVR can separate audio into many more stems, such as Drums, Bass, Piano, and Guitar, and perform other audio processing tasks, such as denoising or removing echo/reverb.
- Separate audio into multiple stems, e.g. instrumental and vocals.
- Supports all common audio formats (WAV, MP3, FLAC, M4A, etc.)
- Ability to inference using a pre-trained model in PTH or ONNX format.
- CLI support for easy use in scripts and batch processing.
- Python API for integration into other projects.
If you're able to use docker, you don't actually need to install anything - there are images published on Docker Hub for GPU (CUDA) and CPU inferencing, for both amd64
and arm64
platforms.
You probably want to volume-mount a folder containing whatever file you want to separate, which can then also be used as the output folder.
For instance, if your current directory has the file input.wav
, you could execute audio-separator
as shown below (see usage section for more details):
docker run -it -v `pwd`:/workdir beveradb/audio-separator input.wav
If you're using a machine with a GPU, you'll want to use the GPU specific image and pass in the GPU device to the container, like this:
docker run -it --gpus all -v `pwd`:/workdir beveradb/audio-separator:gpu input.wav
If the GPU isn't being detected, make sure your docker runtime environment is passing through the GPU correctly - there are various guides online to help with that.
Supported CUDA Versions: 11.8 and 12.2
💬 If successfully configured, you should see this log message when running audio-separator --env_info
:
ONNXruntime has CUDAExecutionProvider available, enabling acceleration
Conda: conda install pytorch=*=*cuda* onnxruntime=*=*cuda* audio-separator -c pytorch -c conda-forge
Pip: pip install "audio-separator[gpu]"
Docker: beveradb/audio-separator:gpu
💬 If successfully configured, you should see this log message when running audio-separator --env_info
:
ONNXruntime has CoreMLExecutionProvider available, enabling acceleration
Pip: pip install "audio-separator[cpu]"
Conda: conda install audio-separator-c pytorch -c conda-forge
Pip: pip install "audio-separator[cpu]"
Docker: beveradb/audio-separator
💬 To test if audio-separator
has been successfully configured to use FFmpeg, run audio-separator --env_info
. The log will show FFmpeg installed
.
If you installed audio-separator
using conda
or docker
, FFmpeg should already be avaialble in your environment.
You may need to separately install FFmpeg. It should be easy to install on most platforms, e.g.:
🐧 Debian/Ubuntu: apt-get update; apt-get install -y ffmpeg
macOS:brew update; brew install ffmpeg
In theory, all you should need to do to get audio-separator
working with a GPU is install it with the [gpu]
extra as above.
However, sometimes getting both PyTorch and ONNX Runtime working with CUDA support can be a bit tricky so it may not work that easily.
You may need to reinstall both packages directly, allowing pip to calculate the right versions for your platform, for example:
pip uninstall torch onnxruntime
pip cache purge
pip install --force-reinstall torch torchvision torchaudio
pip install --force-reinstall onnxruntime-gpu
I generally recommend installing the latest version of PyTorch for your environment using the command recommended by the wizard here: https://pytorch.org/get-started/locally/
Depending on your CUDA version and environment, you may need to install specific version(s) of CUDA libraries for ONNX Runtime to use your GPU.
🧪 Google Colab, for example, now uses CUDA 12 by default, but ONNX Runtime still needs CUDA 11 libraries to work.
If you see the error Failed to load library
or cannot open shared object file
when you run audio-separator
, this is likely the issue.
You can install the CUDA 11 libraries alongside CUDA 12 like so:
apt update; apt install nvidia-cuda-toolkit
Note: if anyone knows how to make this cleaner so we can support both different platform-specific dependencies for hardware acceleration without a separate installation process for each, please let me know or raise a PR!
You can use Audio Separator via the command line, for example:
audio-separator /path/to/your/input/audio.wav --model_filename UVR-MDX-NET-Inst_HQ_3.onnx
This command will download the specified model file, process the audio.wav
input audio and generate two new files in the current directory, one containing vocals and one containing instrumental.
Note: You do not need to download any files yourself - audio-separator does that automatically for you!
To see a list of supported models, run audio-separator --list_models
Any file listed in the list models output can be specified (with file extension) with the model_filename parameter (e.g. --model_filename UVR_MDXNET_KARA_2.onnx
) and it will be automatically downloaded to the --model_file_dir
(default: /tmp/audio-separator-models/
) folder on first usage.
usage: audio-separator [-h] [-v] [-d] [-e] [-l] [--log_level LOG_LEVEL] [-m MODEL_FILENAME] [--output_format OUTPUT_FORMAT] [--output_dir OUTPUT_DIR] [--model_file_dir MODEL_FILE_DIR] [--invert_spect]
[--normalization NORMALIZATION] [--single_stem SINGLE_STEM] [--sample_rate SAMPLE_RATE] [--mdx_segment_size MDX_SEGMENT_SIZE] [--mdx_overlap MDX_OVERLAP] [--mdx_batch_size MDX_BATCH_SIZE]
[--mdx_hop_length MDX_HOP_LENGTH] [--mdx_enable_denoise] [--vr_batch_size VR_BATCH_SIZE] [--vr_window_size VR_WINDOW_SIZE] [--vr_aggression VR_AGGRESSION] [--vr_enable_tta]
[--vr_high_end_process] [--vr_enable_post_process] [--vr_post_process_threshold VR_POST_PROCESS_THRESHOLD] [--demucs_segment_size DEMUCS_SEGMENT_SIZE] [--demucs_shifts DEMUCS_SHIFTS]
[--demucs_overlap DEMUCS_OVERLAP] [--demucs_segments_enabled DEMUCS_SEGMENTS_ENABLED] [--mdxc_segment_size MDXC_SEGMENT_SIZE] [--mdxc_override_model_segment_size]
[--mdxc_overlap MDXC_OVERLAP] [--mdxc_batch_size MDXC_BATCH_SIZE] [--mdxc_pitch_shift MDXC_PITCH_SHIFT]
[audio_file]
Separate audio file into different stems.
positional arguments:
audio_file The audio file path to separate, in any common format.
options:
-h, --help show this help message and exit
Info and Debugging:
-v, --version Show the program's version number and exit.
-d, --debug Enable debug logging, equivalent to --log_level=debug
-e, --env_info Print environment information and exit.
-l, --list_models List all supported models and exit.
--log_level LOG_LEVEL Log level, e.g. info, debug, warning (default: info).
Separation I/O Params:
-m MODEL_FILENAME, --model_filename MODEL_FILENAME model to use for separation (default: UVR-MDX-NET-Inst_HQ_3.onnx). Example: -m 2_HP-UVR.pth
--output_format OUTPUT_FORMAT output format for separated files, any common format (default: FLAC). Example: --output_format=MP3
--output_dir OUTPUT_DIR directory to write output files (default: <current dir>). Example: --output_dir=/app/separated
--model_file_dir MODEL_FILE_DIR model files directory (default: /tmp/audio-separator-models/). Example: --model_file_dir=/app/models
Common Separation Parameters:
--invert_spect invert secondary stem using spectogram (default: False). Example: --invert_spect
--normalization NORMALIZATION value by which to multiply the amplitude of the output files (default: 0.9). Example: --normalization=0.7
--single_stem SINGLE_STEM output only single stem, e.g. Instrumental, Vocals, Drums, Bass, Guitar, Piano, Other. Example: --single_stem=Instrumental
--sample_rate SAMPLE_RATE set the sample rate of the output audio (default: 44100). Example: --sample_rate=44100
MDX Architecture Parameters:
--mdx_segment_size MDX_SEGMENT_SIZE larger consumes more resources, but may give better results (default: 256). Example: --mdx_segment_size=256
--mdx_overlap MDX_OVERLAP amount of overlap between prediction windows, 0.001-0.999. higher is better but slower (default: 0.25). Example: --mdx_overlap=0.25
--mdx_batch_size MDX_BATCH_SIZE larger consumes more RAM but may process slightly faster (default: 1). Example: --mdx_batch_size=4
--mdx_hop_length MDX_HOP_LENGTH usually called stride in neural networks; only change if you know what you're doing (default: 1024). Example: --mdx_hop_length=1024
--mdx_enable_denoise enable denoising after separation (default: False). Example: --mdx_enable_denoise
VR Architecture Parameters:
--vr_batch_size VR_BATCH_SIZE number of "batches" to process at a time. higher = more RAM, slightly faster processing (default: 1). Example: --vr_batch_size=16
--vr_window_size VR_WINDOW_SIZE balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: 512). Example: --vr_window_size=320
--vr_aggression VR_AGGRESSION intensity of primary stem extraction, -100 - 100. typically 5 for vocals & instrumentals (default: 5). Example: --vr_aggression=2
--vr_enable_tta enable Test-Time-Augmentation; slow but improves quality (default: False). Example: --vr_enable_tta
--vr_high_end_process mirror the missing frequency range of the output (default: False). Example: --vr_high_end_process
--vr_enable_post_process identify leftover artifacts within vocal output; may improve separation for some songs (default: False). Example: --vr_enable_post_process
--vr_post_process_threshold VR_POST_PROCESS_THRESHOLD threshold for post_process feature: 0.1-0.3 (default: 0.2). Example: --vr_post_process_threshold=0.1
Demucs Architecture Parameters:
--demucs_segment_size DEMUCS_SEGMENT_SIZE size of segments into which the audio is split, 1-100. higher = slower but better quality (default: Default). Example: --demucs_segment_size=256
--demucs_shifts DEMUCS_SHIFTS number of predictions with random shifts, higher = slower but better quality (default: 2). Example: --demucs_shifts=4
--demucs_overlap DEMUCS_OVERLAP overlap between prediction windows, 0.001-0.999. higher = slower but better quality (default: 0.25). Example: --demucs_overlap=0.25
--demucs_segments_enabled DEMUCS_SEGMENTS_ENABLED enable segment-wise processing (default: True). Example: --demucs_segments_enabled=False
MDXC Architecture Parameters:
--mdxc_segment_size MDXC_SEGMENT_SIZE larger consumes more resources, but may give better results (default: 256). Example: --mdxc_segment_size=256
--mdxc_override_model_segment_size override model default segment size instead of using the model default value. Example: --mdxc_override_model_segment_size
--mdxc_overlap MDXC_OVERLAP amount of overlap between prediction windows, 2-50. higher is better but slower (default: 8). Example: --mdxc_overlap=8
--mdxc_batch_size MDXC_BATCH_SIZE larger consumes more RAM but may process slightly faster (default: 1). Example: --mdxc_batch_size=4
--mdxc_pitch_shift MDXC_PITCH_SHIFT shift audio pitch by a number of semitones while processing. may improve output for deep/high vocals. (default: 0). Example: --mdxc_pitch_shift=2
You can use Audio Separator in your own Python project. Here's a minimal example using the default two stem (Instrumental and Vocals) model:
from audio_separator.separator import Separator
# Initialize the Separator class (with optional configuration properties, below)
separator = Separator()
# Load a machine learning model (if unspecified, defaults to 'model_mel_band_roformer_ep_3005_sdr_11.4360.ckpt')
separator.load_model()
# Perform the separation on specific audio files without reloading the model
output_files = separator.separate('audio1.wav')
print(f"Separation complete! Output file(s): {' '.join(output_files)}")
Here's an example of how you can process a single input file with multiple different models to get desired results.
This example came from a user who wanted the following outputs:
Vocals.wav
Instrumental.wav
Vocals (Reverb).wav
Vocals (No Reverb).wav
Lead Vocals.wav
Backing Vocals.wav
To achieve this, they used the following code, leveraging three different models in sequence and renaming the output files:
import os
from audio_separator.separator import Separator
input = "/content/input.mp3"
output = "/content/output"
separator = Separator(output_dir=output)
# Vocals and Instrumental
vocals = os.path.join(output, 'Vocals.wav')
instrumental = os.path.join(output, 'Instrumental.wav')
# Vocals with Reverb and Vocals without Reverb
vocals_reverb = os.path.join(output, 'Vocals (Reverb).wav')
vocals_no_reverb = os.path.join(output, 'Vocals (No Reverb).wav')
# Lead Vocals and Backing Vocals
lead_vocals = os.path.join(output, 'Lead Vocals.wav')
backing_vocals = os.path.join(output, 'Backing Vocals.wav')
# Splitting a track into Vocal and Instrumental
separator.load_model(model_filename='model_bs_roformer_ep_317_sdr_12.9755.ckpt')
voc_inst = separator.separate(input)
os.rename(os.path.join(output, voc_inst[0]), instrumental) # Rename file to “Instrumental.wav”
os.rename(os.path.join(output, voc_inst[1]), vocals) # Rename file to “Vocals.wav”
# Applying DeEcho-DeReverb to Vocals
separator.load_model(model_filename='UVR-DeEcho-DeReverb.pth')
voc_no_reverb = separator.separate(vocals)
os.rename(os.path.join(output, voc_no_reverb[0]), vocals_no_reverb) # Rename file to “Vocals (No Reverb).wav”
os.rename(os.path.join(output, voc_no_reverb[1]), vocals_reverb) # Rename file to “Vocals (Reverb).wav”
# Separating Back Vocals from Main Vocals
separator.load_model(model_filename='mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt')
backing_voc = separator.separate(vocals_no_reverb)
os.rename(os.path.join(output, backing_voc[0]), backing_vocals) # Rename file to “Backing Vocals.wav”
os.rename(os.path.join(output, backing_voc[1]), lead_vocals) # Rename file to “Lead Vocals.wav”
Thanks to @Bebra777228 for contributing this example!
You can process multiple files without reloading the model to save time and memory.
You only need to load a model when choosing or changing models. See example below:
from audio_separator.separator import Separator
# Initialize the Separator with other configuration properties, below
separator = Separator()
# Load a model
separator.load_model(model_filename='UVR-MDX-NET-Inst_HQ_3.onnx')
# Separate multiple audio files without reloading the model
output_file_paths_1 = separator.separate('audio1.wav')
output_file_paths_2 = separator.separate('audio2.wav')
output_file_paths_3 = separator.separate('audio3.wav')
# Load a different model
separator.load_model(model_filename='UVR_MDXNET_KARA_2.onnx')
# Separate the same files with the new model
output_file_paths_4 = separator.separate('audio1.wav')
output_file_paths_5 = separator.separate('audio2.wav')
output_file_paths_6 = separator.separate('audio3.wav')
- log_level: (Optional) Logging level, e.g., INFO, DEBUG, WARNING. Default: logging.INFO
- log_formatter: (Optional) The log format. Default: None, which falls back to '%(asctime)s - %(levelname)s - %(module)s - %(message)s'
- model_file_dir: (Optional) Directory to cache model files in. Default: /tmp/audio-separator-models/
- output_dir: (Optional) Directory where the separated files will be saved. If not specified, uses the current directory.
- output_format: (Optional) Format to encode output files, any common format (WAV, MP3, FLAC, M4A, etc.). Default: WAV
- normalization_threshold: (Optional) The amount by which the amplitude of the output audio will be multiplied. Default: 0.9
- amplification_threshold: (Optional) The minimum amplitude level at which the waveform will be amplified. If the peak amplitude of the audio is below this threshold, the waveform will be scaled up to meet it. Default: 0.6
- output_single_stem: (Optional) Output only a single stem, such as 'Instrumental' and 'Vocals'. Default: None
- invert_using_spec: (Optional) Flag to invert using spectrogram. Default: False
- sample_rate: (Optional) Set the sample rate of the output audio. Default: 44100
- use_soundfile: (Optional) Use soundfile for output writing, can solve OOM issues, especially on longer audio.
- mdx_params: (Optional) MDX Architecture Specific Attributes & Defaults. Default: {"hop_length": 1024, "segment_size": 256, "overlap": 0.25, "batch_size": 1}
- vr_params: (Optional) VR Architecture Specific Attributes & Defaults. Default: {"batch_size": 1, "window_size": 512, "aggression": 5, "enable_tta": False, "enable_post_process": False, "post_process_threshold": 0.2, "high_end_process": False}
- demucs_params: (Optional) VR Architecture Specific Attributes & Defaults. {"segment_size": "Default", "shifts": 2, "overlap": 0.25, "segments_enabled": True}
Python >= 3.10
Libraries: torch, onnx, onnxruntime, numpy, librosa, requests, six, tqdm, pydub
This project uses Poetry for dependency management and packaging. Follow these steps to setup a local development environment:
- Make sure you have Python 3.10 or newer installed on your machine.
- Install Conda (I recommend Miniforge: https://github.com/conda-forge/miniforge) to manage your Python virtual environments
Clone the repository to your local machine:
git clone https://github.com/YOUR_USERNAME/audio-separator.git
cd audio-separator
Replace YOUR_USERNAME with your GitHub username if you've forked the repository, or use the main repository URL if you have the permissions.
To create and activate the conda environment, use the following commands:
conda env create
conda activate audio-separator-dev
Once you're inside the conda env, run the following command to install the project dependencies:
poetry install
Install extra dependencies depending if you're running with GPU or CPU.
poetry install --extras "cpu"
or
poetry install --extras "gpu"
You can run the CLI command directly within the virtual environment. For example:
audio-separator path/to/your/audio-file.wav
Once you are done with your development work, you can exit the virtual environment by simply typing:
conda deactivate
To build the package for distribution, use the following command:
poetry build
This will generate the distribution packages in the dist directory - but for now only @beveradb will be able to publish to PyPI.
- Link Input:
-
video_url: This input is where you paste the URL of the audio or video you want to download. It can be from various platforms supported by yt-dlp. For a full list of supported websites, refer to this link.
-
Example:
https://www.youtube.com/watch?v=exampleID
- Input Audio File for Separation:
-
input: This is the file path of the audio you want to separate. After downloading the audio file, you will need to specify this path to continue with separation.
-
Example:
/content/ytdl/your_downloaded_audio.wav
- Output Directory:
-
output: This is the path where the separated files will be saved. It defaults to
/content/output
but can be changed to another directory if desired. -
Example:
/content/custom_output
-
Contributions are very much welcome! Please fork the repository and submit a pull request with your changes, and I'll try to review, merge and publish promptly!
- This project is 100% open-source and free for anyone to use and modify as they wish.
- If the maintenance workload for this repo somehow becomes too much for me I'll ask for volunteers to share maintainership of the repo, though I don't think that is very likely
- Development and support for the MDX-Net separation models is part of the main UVR project, this repo is just a CLI/Python package wrapper to simplify running those models programmatically. So, if you want to try and improve the actual models, please get involved in the UVR project and look for guidance there!
This project is licensed under the MIT License.
- Please Note: If you choose to integrate this project into some other project using the default model or any other model trained as part of the UVR project, please honor the MIT license by providing credit to UVR and its developers!
- Anjok07 - Author of Ultimate Vocal Remover GUI, which almost all of the code in this repo was copied from! Definitely deserving of credit for anything good from this project. Thank you!
- DilanBoskan - Your contributions at the start of this project were essential to the success of UVR. Thank you!
- Kuielab & Woosung Choi - Developed the original MDX-Net AI code.
- KimberleyJSN - Advised and aided the implementation of the training scripts for MDX-Net and Demucs. Thank you!
- Hv - Helped implement chunks into the MDX-Net AI code. Thank you!
- zhzhongshi - Helped add support for the MDXC models in
audio-separator
. Thank you!
For questions or feedback, please raise an issue or reach out to @beveradb (Andrew Beveridge) directly.