Polymath uses machine learning to convert any music library (e.g from Hard-Drive or YouTube) into a music production sample-library. The tool automatically separates songs into stems (beats, bass, etc.), quantizes them to the same tempo and beat-grid (e.g. 120bpm) and analyzes musical structure (e.g. verse, chorus, etc.), key (e.g C4, E3, etc.) and other infos (timbre, loudness, etc.). The result is a searchable sample library that streamlines the workflow for music producers, DJs, and ML audio developers.

Polymath makes it effortless to combine elements from different songs to create unique new compositions: Simply grab a beat from a Funkadelic track, a bassline from a Tito Puente piece, and fitting horns from a Fela Kuti song, and seamlessly integrate them into your DAW in record time. Using Polymath's search capability to discover related tracks, it is a breeze to create a polished, hour-long mash-up DJ set. For ML developers, Polymath simplifies the process of creating a large music dataset, for training generative models, etc.

• Music Source Separation is performed with the Demucs neural network
• Music Structure Segmentation/Labeling is performed with the sf_segmenter neural network
• Music Pitch Tracking and Key Detection are performed with Crepe neural network
• Music Quantization and Alignment are performed with pyrubberband
• Music Info retrieval and processing is performed with librosa

You will need Python 3.8.

If you're using pip on a Linux environment, you may need to install libsndfile manually. Please refer to the SoundFile installation documentation for details.

sudo apt-get install ffmpeg 
# or
sudo apt-get install gstreamer1.0-plugins-base gstreamer1.0-plugins-ugly

git clone [email protected]:samim23/polymath.git
cd polymath
pip install -r requirements.txt

python polymath.py -a n6DAqMFe97E

python polymath.py -a /path/to/audiolib/song.wav

python polymath.py -a n6DAqMFe97E,eaPzCHEQExs,RijB8wnJCN0
python polymath.py -a /path/to/audiolib/song1.wav,/path/to/audiolib/song2.wav
python polymath.py -a /path/to/audiolib/

Songs are automatically analyzed once which takes some time. Once in the database, they can be access rapidly. The database is stored in the folder "/library/database.p". To reset everything, simply delete it.

python polymath.py -q n6DAqMFe97E -t 120

python polymath.py -q all -t 120

python polymath.py -q n6DAqMFe97E -k

Songs are automatically quantized to the same tempo and beat-grid and saved to the folder “/processed”.

python polymath.py -s n6DAqMFe97E -sa 10

python polymath.py -s n6DAqMFe97E -sa 10 -q all -t 120

python polymath.py -s n6DAqMFe97E -sa 10 -q all -t 120 -st -k

Similar songs are automatically found and optionally quantized and saved to the folder "/processed". This makes it easy to create for example an hour long mix of songs that perfectly match one after the other.

The Demucs Neural Net has settings that can be adjusted in the python file

- bass
- drum
- guitare
- other
- piano
- vocals

The audio feature extractors have settings that can be adjusted in the python file

- tempo
- duration
- timbre
- timbre_frames
- pitch
- pitch_frames
- intensity
- intensity_frames
- volume
- avg_volume
- loudness
- beats
- segments_boundaries
- segments_labels
- frequency_frames
- frequency
- key

Polymath is released under the MIT license as found in the LICENSE file.