karaoke-prep 0.6.0

Prepare for karaoke video creation, by downloading audio and lyrics for a specified song or youtube playlist and separatung audio stems.

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!
• Flexible Input: provide just a YouTube URL, just an artist and title, or both.
• Playlist Processing: Capable of processing an entire YouTube playlist, extracting audio and lyrics for each track.

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:
  args                             [YouTube video or playlist URL] [Artist] [Title] of song to prep. If URL is provided, Artist and Title are optional but increase chance of fetching the correct lyrics. If Artist and Title are provided with no URL, the top YouTube search result will be fetched.

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
  --intro_background_color INTRO_BACKGROUND_COLOR  Optional: Background color for intro video (default: black). Example: --intro_background_color=#123456
  --intro_background_image INTRO_BACKGROUND_IMAGE  Optional: Path to background image for intro video. Overrides background color if provided. Example: --intro_background_image=path/to/image.jpg
  --intro_font INTRO_FONT                          Optional: Font file for intro video (default: Avenir-Next-Bold). Example: --intro_font=AvenirNext-Bold.ttf
  --intro_artist_color INTRO_ARTIST_COLOR          Optional: Font color for intro video artist text (default: #ff7acc). Example: --intro_artist_color=#123456
  --intro_title_color INTRO_TITLE_COLOR            Optional: Font color for intro video title text (default: #ffdf6b). Example: --intro_title_color=#123456

Only YouTube URL

This will process the video at the given URL, guessing the artist and title from the YouTube title. ⚠️ Be aware the downloaded lyrics may be incorrect if the video title doesn't match the standard "Artist - Title" format.

karaoke-prep "https://www.youtube.com/watch?v=YOUR_VIDEO_ID"

Artist and Title Only

If you don't have a specific YouTube URL, just provide the artist and title. Karaoke Prep will search for and download the top YouTube result.

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

⭐ YouTube URL with Artist and Title

For more precise control (and most consistent results), provide the YouTube URL along with the artist and title to avoid any guesswork.

karaoke-prep "https://www.youtube.com/watch?v=YOUR_VIDEO_ID" "The Fray" "Never Say Never"

YouTube Playlist URL

To process a playlist, just provide the playlist URL. The script will process every video in the playlist. ⚠️ Be aware the downloaded lyrics for each track may be incorrect if the video titles don't match the standard "Artist - Title" format.

karaoke-prep "https://www.youtube.com/playlist?list=YOUR_PLAYLIST_ID"

By default, you'll end up with files in the current folder, neatly organised into folders for each track and files named consistently 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.