omnizart 0.1.0b1

Omniscient Mozart, being able to transcribe everything in the music.

omnizart

Omniscient Mozart, being able to transcribe everything in the music, including vocal, drum, chord, beat, instruments, and more. Combines all the hard works developed by everyone in MCTLab into a single command line tool, and plan to distribute as a python package in the future.

A quick-start example is as following:

# Install omnizart
pip install omnizart

# Download the checkpoints after installation
omnizart download-checkpoints

# Now it's ready for the transcription~
omnizart drum transcribe <path/to/audio.wav>
omnizart chord transcribe <path/to/audio.wav>
omnizart music transcribe <path/to/audio.wav>

Or use the docker image:

docker pull mctlab/omnizart:latest
docker run -it mctlab/omnziart:latest bash

Comprehensive usage and API references can be found in the official documentation site.

About

Music and Culture Technology Lab (MCTLab) aims to develop technology for music and relevant applications by leveraging cutting-edge AI techiniques.

Plan to support

Commands	transcribe	train	evaluate	Description
music	:heavy_check_mark:	:heavy_check_mark:		Transcribes notes of instruments.
drum	:heavy_check_mark:	:interrobang:		Transcribes drum tracks.
vocal				Transcribes pitch of vocal.
chord	:heavy_check_mark:	:heavy_check_mark:		Transcribes chord progression.
beat				Transcribes beat position.

NOTES Though the implementation of training the drum model is 90% complete, but there still exists some invisible bugs that cause the training fails to converge compared to the author's original implementation.

Example usage

omnizart music transcribe path/to/audio
omnizart chord transcribe path/to/audio
omnizart drum transcribe path/to/audio

For training a new model, download the dataset first and follow steps described below.

# The following command will default saving the extracted feature under the same folder,
# called train_feature and test_feature
omnizart music generate-featuer -d path/to/dataset

# Train a new model
omnizart music train-model -d path/to/dataset/train_feature --model-name My-Model

Development

Describes the neccessary background of how to develop this project.

Download and install

git clone https://github.com/Music-and-Culture-Technology-Lab/omnizart.git

# Install dependenies. For more different installation approaches, please refer to the official documentation page.
# The following command will download the checkpoints automatically.
cd omnizart
make install

# For developers, you have to install Dev dependencies as well, since they will not be installed by default.
poetry install

Package management

Uses poetry for package management, instead of writing requirements.txt and setup.py manually. We still provide the above two files for convenience. You can also generate them by executing make export.

Documentation

Automatically generate documents from inline docstrings of module, class, and function. Hosted document page

Documentation style: Follows numpy document flavor. Learn more from numpydoc.

Document builder: sphinx

To generate documents, cd docs/ and execute make html. To see the rendered results, run make serve and view from the browser. All documents and docstrings use reStructured Text format. More informations about this format can be found from Sphinx's Document.

Linters

Uses flake8 and pylint for coding style check.

To check with linters, execute make check.

You don't have to achieve a perfect score on pylint check, just pass 9.5 points still counted as a successful check.

Caution!

There is convenient make command for formating the code, but it should be used very carefully. Not only it could format the code for you, but also could mess up the code, and eventually you should still need to check the format manually after refacorting the code with tools.

To format the code with black and yapf, enter make format.

Unittest

Uses pytest for unittesting. Under construction...

CI/CD

Uses github actions for automatic linting, unittesting, document building, and package release. Currently supports two workflows:

• General check
• Documentation page publishing
• Publish PyPI package and docker image

General Check

Everytime you push to the master branch, file a pull request, and merge into master branch, will trigger this action. This will do checks like code format, and unittests by leveraging the above mentioned tools. If the check fails, you will not be able to merge the feature branch into master branch.

Documentation Page Publishing

We use github page to host our documentation, and is separated as an independent repository.

Please do not directly modify the content of the omnizart-doc repository!!

The only permitted way to update the documentation page is by updating the build_doc branch, and let the workflow do the rest of things.

Steps to update the documentation page:

• Clone this repo
• Create a new branch. DO NOT UPDATE THE build_doc BRANCH DIRECTLY!!
• File a pull request
• Merge into master (by admin)
• Merge into build_doc branch (by admin)
• Push to this repo (by admin)

Publish PyPI Package and Docker Image

Publish the python package to PyPI and also the docker image to dockerhub when push tags to the repository. The publish process will be automatically done by the github actions. There are several steps in the process:

1. Pack and publish the python package.
2. Build the docker image and publish to Docker Hub.
3. Create release -> this will also trigger the automation of documentation publishment.

Docker

We provide both the Dockerfile for local image build and also the pre-build image on Docker Hub.

To build the image, run the following:

docker build -t omnizart:my-image .

To use the pre-build image, follow below steps:

# Download from Docker Hub
docker pull mctlab/omnizart

# Execute the image
docker run -it mctlab/omnizart:latest

### For those who want to leverage the power of GPU for acceleration, make sure
### you have installed docker>=19.03 and the 'nvidia-container-toolkit' package.
# Execute the docker with GPU support
docker run --gpus all -it mctlab/omnizart:latest

Command Test

To actually install and test the omnizart command, execute make install. This will automatically create a virtual environment and install everything needed inside it. After installation, just follow the instruction showing on the screen to activate the environment, then type omnizart --help to check if it works. After testing the command, type deactivate to leave the virtual environment.