karaoke-prep 0.1.7

Prepare for karaoke video creation, by downloading audio and lyrics for a specified song.

Karaoke Prep 🎶

Prepare for karaoke video creation, by downloading audio from YouTube and lyrics from Genius for a given song.

This was created to make it easier for me to prepare the source audio, lyrics and instrumental audio so I can then do the actual karaoke creation in focus mode without internet.

Features

• Audio Fetching: Automatically downloads audio in WAV format for the YouTube URL provided (or searches for the song/artist name and fetches the top result).
• Lyrics Fetching: Automatically fetches song lyrics from Genius.com using LyricsGenius.
• Audio Separation: Separates the downloaded audio into instrumental and vocal tracks, using audio-separator.
• Multiple Audio Models: Runs audio separation with 2 different models (by default, UVR_MDXNET_KARA_2 and UVR-MDX-NET-Inst_HQ_3) to give you options for the backing track.
• Easy Configuration: Control the tool's behavior using command-line arguments.
• Organized Outputs: Creates structured output directories for easy access to generated tracks and lyrics.
• Internet First: Completes operations which require internet first, in case user is preparing last-minute before a period of being offline!

Installation 🛠️

You can install Karaoke Prep using pip:

pip install karaoke-prep

Usage 🚀

Command Line Interface (CLI)

You can use Karaoke Prep via the command line:

usage: karaoke-prep [-h] [-v] [--log_level LOG_LEVEL] [--model_name MODEL_NAME] [--model_file_dir MODEL_FILE_DIR] [--output_dir OUTPUT_DIR] [--use_cuda] [--use_coreml]
                    [--denoise DENOISE] [--normalize NORMALIZE] [--create_track_subfolders]
                    [artist] [title] [url]

Fetch audio and lyrics for a specified song, to prepare karaoke video creation.

positional arguments:
  artist                           Artist name for the song to prep.
  title                            Title of the song to prep.
  url                              YouTube URL of the song to prep.

options:
  -h, --help                       show this help message and exit
  -v, --version                    show program's version number and exit
  --log_level LOG_LEVEL            Optional: logging level, e.g. info, debug, warning (default: info). Example: --log_level=debug
  --model_name MODEL_NAME          Optional: model name to be used for separation (default: UVR_MDXNET_KARA_2). Example: --model_name=UVR-MDX-NET-Inst_HQ_3
  --model_file_dir MODEL_FILE_DIR  Optional: model files directory (default: /tmp/audio-separator-models/). Example: --model_file_dir=/app/models
  --output_dir OUTPUT_DIR          Optional: directory to write output files (default: <current dir>). Example: --output_dir=/app/karaoke
  --use_cuda                       Optional: use Nvidia GPU with CUDA for separation (default: False). Example: --use_cuda=true
  --use_coreml                     Optional: use Apple Silicon GPU with CoreML for separation (default: False). Example: --use_coreml=true
  --denoise DENOISE                Optional: enable or disable denoising during separation (default: True). Example: --denoise=False
  --normalize NORMALIZE            Optional: enable or disable normalization during separation (default: True). Example: --normalize=False
  --no_track_subfolders            Optional:do NOT create a named subfolder for each track. Example: --no_track_subfolders

Example:

karaoke-prep "The Fray" "Never Say Never"

This command will search YouTube for the specified song, download audio from the top result and lyrics from genius, then separate the audio to produce an instrumental track, ready for a karaoke video to be created by whatever process you prefer!

By default, you'll end up with files in the current folder, neatly organised e.g.

├── Artist - Title (Instrumental UVR-MDX-NET-Inst_HQ_3).flac
├── Artist - Title (Instrumental UVR_MDXNET_KARA_2).flac
├── Artist - Title (Lyrics).txt
├── Artist - Title (Vocals UVR-MDX-NET-Inst_HQ_3).flac
├── Artist - Title (Vocals UVR_MDXNET_KARA_2).flac
└── Artist - Title (YouTube CNUgemJBLTw).wav

Requirements 📋

Python >= 3.9

Libraries: onnx, onnxruntime, numpy, soundfile, librosa, torch, wget, six

Developing Locally

This project uses Poetry for dependency management and packaging. Follow these steps to setup a local development environment:

Prerequisites

• Make sure you have Python 3.9 or newer installed on your machine.
• Install Poetry by following the installation guide here.

Clone the Repository

Clone the repository to your local machine:

git clone https://github.com/YOUR_USERNAME/karaoke-prep.git
cd karaoke-prep

Replace YOUR_USERNAME with your GitHub username if you've forked the repository, or use the main repository URL if you have the permissions.

Install Dependencies

Run the following command to install the project dependencies:

poetry install

Activate the Virtual Environment

To activate the virtual environment, use the following command:

poetry shell

Running the Command-Line Interface Locally

You can run the CLI command directly within the virtual environment. For example:

karaoke-prep 1

Deactivate the Virtual Environment

Once you are done with your development work, you can exit the virtual environment by simply typing:

exit

Building the Package

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.

Contributing 🤝

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

License 📄

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!

Credits 🙏

• Anjok07 - Author of Ultimate Vocal Remover GUI, which was essential for the creation of audio-separator! Thank you!

Contact 💌

For questions or feedback, please raise an issue or reach out to @beveradb (Andrew Beveridge) directly.