Implementation of the Wave-U-Net for audio source separation.
Listen to vocal separation results here and to multi-instrument separation results here
The Wave-U-Net is a convolutional neural network applicable to audio source separation tasks, which works directly on the raw audio waveform, presented in this paper.
The Wave-U-Net is an adaptation of the U-Net architecture to the one-dimensional time domain to perform end-to-end audio source separation. Through a series of downsampling and upsampling blocks, which involve 1D convolutions combined with a down-/upsampling process, features are computed on multiple scales/levels of abstraction and time resolution, and combined to make a prediction.
See the diagram below for a summary of the network architecture.
The Wave-U-Net also participated in the SiSec separation campaign as submissions STL1 and STL2 and achieved a good performance, especially considering the limited dataset we used compared to many other submissions despite having a more data-hungry end-to-end approach (we have to learn the frequency decomposition front-end from data as well).
GPU strongly recommended to avoid very long training times. Python 2.7 Python package requirements below:
sacred==0.7.3
tensorflow-gpu==1.8.0
librosa==0.6.1
scikit-image==0.13.1
soundfile==0.10.2
scikits.audiolab==0.11.0
lxml==4.2.1
musdb==0.2.3
museval==0.2.0
These required packages are also saved in the file requirements.txt located in this repository, so you can clone the repository and then execute the following in the downloaded repository's path to install all the required packages at once:
pip install -r requirements.txt
To try our vocal separator model variant on your own songs, skip to the last section, since the datasets are not needed. To reproduce the experiments in the paper (and train all the models), you need to download the datasets below. You can of course use your own datasets for separation, but for this you would need to modify the code manually, which will not be discussed here.
Download the full MUSDB18 dataset and extract it into a folder of your choice. It should have two subfolders: "test" and "train" as well as a README.md file.
If you want to replicate the vocal separation experiments and not only the multi-instrument experiments, you also need to download the CCMixter vocal separation database from https://members.loria.fr/ALiutkus/kam/. Extract this dataset into a folder of your choice. Its main folder should contain one subfolder for each song.
Now you need to set up the correct file paths for the datasets and the location where source estimates should be saved.
Open the Config.py file, and set the musdb_path entry of the model_config dictionary to the location of the main folder of the MUSDB18 dataset.
Also set the estimates_path entry of the same model_config dictionary to the path pointing to an empty folder where you want the final source estimates of the model to be saved into.
If you use CCMixter, open the CCMixter.xml in the main repository folder, and replace the given file path tagged as databaseFolderPath with your path to the main folder of CCMixter.
Since the paper investigates many model variants of the Wave-U-Net and also trains the U-Net proposed for vocal separation, which achieved state-of-the-art performance, as a comparison, we give a list of model variants to train and the command needed to start training them:
| Model name (from paper) | Description | Separate vocals or multi-instrument? | Command for training |
|---|---|---|---|
| M1 | Baseline Wave-U-Net model | Vocals | python Training.py |
| M2 | M1 + difference output layer | Vocals | python Training.py with cfg.baseline_diff |
| M3 | M2 + proper input context | Vocals | python Training.py with cfg.baseline_context |
| M4 | BEST-PERFORMING: M3 + Stereo I/O | Vocals | python Training.py with cfg.baseline_stereo |
| M5 | M4 + Learned upsampling layer | Vocals | python Training.py with cfg.full |
| M6 | M4 applied to multi-instrument sep. | Multi-instrument | python Training.py with cfg.full_multi_instrument |
| M7 | Wave-U-Net model to compare with SotA models U7,U7a | Vocals | python Training.py with cfg.baseline_comparison |
| U7 | U-Net replication from prior work, audio-based MSE loss | Vocals | python Training.py with cfg.unet_spectrogram |
| U7a | Like U7, but with L1 magnitude loss | Vocals | python Training.py with cfg.unet_spectrogram_l1 |
We provide a pretrained version of the stereo vocal separator (Model M4) so you can separate any of your songs into vocals and accompaniment right away.
Download our pretrained models here.
Unzip the archive into the checkpoints subfolder in this repository, so that you have one subfolder for each model (e.g. REPO/checkpoints/baseline_stereo)
For a quick demo on an example song with our pretrained model, one can simply execute
python Predict.py with cfg.baseline_stereo
to separate the song "Mallory" included in this repository's audio_examples subfolder into vocals and accompaniment. The output will be saved next to the input file.
To apply our pretrained model to any of your own songs, simply point to its audio file path using the input_path parameter:
python Predict.py with cfg.baseline_stereo input_path="/mnt/medien/Daniel/Music/Dark Passion Play/Nightwish - Bye Bye Beautiful.mp3"
If you want to save the predictions to a custom folder instead of where the input song is, just add the output_path parameter:
python Predict.py with cfg.baseline_stereo input_path="/mnt/medien/Daniel/Music/Dark Passion Play/Nightwish - Bye Bye Beautiful.mp3" output_path="/home/daniel"
If you want to use your own trained model, point to the location of the Tensorflow checkpoint file using the model_path parameter, making sure that the model configuration (here: baseline_stereo) matches with the model saved in the checkpoint:
python Predict.py with cfg.baseline_stereo model_path="checkpoints/baseline_stereo/baseline_stereo-186093" input_path="/mnt/medien/Daniel/Music/Dark Passion Play/Nightwish - Bye Bye Beautiful.mp3" output_path="/home/daniel"
During the preparation of the MUSDB dataset, conversion to WAV can sometimes halt because of an ffmpeg process freezing that is used within the musdb python package to identify the datasets mp4 audio streams. This seems to be an error occurring upon the subprocess.Popen() used deep within the stempeg library. Due to its random nature, it is not currently known how to fix this. I suggest regenerating the dataset again if this error occurs.