⚠️ This project is still under development!
UltraSinger is a tool to automatically create UltraStar.txt, midi and notes from music. It automatically pitches UltraStar files, adding text and tapping to UltraStar files and creates separate UltraStar karaoke files. It also can re-pitch current UltraStar files and calculates the possible in-game score.
Multiple AI models are used to extract text from the voice and to determine the pitch.
Please mention UltraSinger in your UltraStar.txt file if you use it. It helps others find this tool, and it helps this tool get improved and maintained. You should only use it on Creative Commons licensed songs.
There are many ways to support this project. Starring ⭐️ the repo is just one 🙏
You can also support this work on GitHub sponsors or Patreon or Buy Me a Coffee.
This will help me a lot to keep this project alive and improve it.
- Install Python 3.10 (older and newer versions has some breaking changes). Download
- Also install ffmpeg separately with PATH. Download
- Go to folder
installand run install script for your OS.- Choose
GPUif you have an nvidia CUDA GPU. - Choose
CPUif you don't have an nvidia CUDA GPU.
- Choose
- In root folder just run
run_on_windows.batorrun_on_linux.shto start the app. - Now you can use the UltraSinger source code with
py UltraSinger.py [opt] [mode] [transcription] [pitcher] [extra]. See How to use for more information.
Not all options working now!
UltraSinger.py [opt] [mode] [transcription] [pitcher] [extra]
[opt]
-h This help text.
-i Ultrastar.txt
audio like .mp3, .wav, youtube link
-o Output folder
[mode]
## if INPUT is audio ##
default Creates all
# Single file creation selection is in progress, you currently getting all!
(-u Create ultrastar txt file) # In Progress
(-m Create midi file) # In Progress
(-s Create sheet file) # In Progress
## if INPUT is ultrastar.txt ##
default Creates all
# Single selection is in progress, you currently getting all!
(-r repitch Ultrastar.txt (input has to be audio)) # In Progress
(-p Check pitch of Ultrastar.txt input) # In Progress
(-m Create midi file) # In Progress
[transcription]
# Default is whisper
--whisper Multilingual model > tiny|base|small|medium|large-v1|large-v2 >> ((default) is large-v2
English-only model > tiny.en|base.en|small.en|medium.en
--whisper_align_model Use other languages model for Whisper provided from huggingface.co
--language Override the language detected by whisper, does not affect transcription but steps after transcription
--whisper_batch_size Reduce if low on GPU mem >> ((default) is 16)
--whisper_compute_type Change to "int8" if low on GPU mem (may reduce accuracy) >> ((default) is "float16" for cuda devices, "int8" for cpu)
[pitcher]
# Default is crepe
--crepe tiny|full >> ((default) is full)
--crepe_step_size unit is miliseconds >> ((default) is 10)
[extra]
--hyphenation True|False >> ((default) is True)
--disable_separation True|False >> ((default) is False)
--disable_karaoke True|False >> ((default) is False)
--ignore_audio True|False >> ((default) is False)
--create_audio_chunks True|False >> ((default) is False)
--keep_cache True|False >> ((default) is False)
--plot True|False >> ((default) is False)
--format_version 0.3.0|1.0.0|1.1.0 >> ((default) is 1.0.0)
--musescore_path path to MuseScore executable
[device]
--force_cpu True|False >> ((default) is False) All steps will be forced to cpu
--force_whisper_cpu True|False >> ((default) is False) Only whisper will be forced to cpu
--force_crepe_cpu True|False >> ((default) is False) Only crepe will be forced to cpu
For standard use, you only need to use [opt]. All other options are optional.
-i "input/music.mp3"
-i https://www.youtube.com/watch?v=BaW_jenozKc
This re-pitch the audio and creates a new txt file.
-i "input/ultrastar.txt"
Keep in mind that while a larger model is more accurate, it also takes longer to transcribe.
For the first test run, use the tiny, to be accurate use the large-v2 model.
-i XYZ --whisper large-v2
Currently provided default language models are en, fr, de, es, it, ja, zh, nl, uk, pt.
If the language is not in this list, you need to find a phoneme-based ASR model from
🤗 huggingface model hub. It will download automatically.
Example for romanian:
-i XYZ --whisper_align_model "gigant/romanian-wav2vec2"
Is on by default. Can also be deactivated if hyphenation does not produce anything useful. Note that the word is simply split, without paying attention to whether the separated word really starts at the place or is heard.
-i XYZ --hyphenation True
Pitching is done with the crepe model.
Also consider that a bigger model is more accurate, but also takes longer to pitch.
For just testing you should use tiny.
If you want solid accurate, then use the full model.
-i XYZ --crepe full
The vocals are separated from the audio before they are passed to the models. If problems occur with this, you have the option to disable this function; in which case the original audio file is used instead.
-i XYZ --disable_separation True
For Sheet Music generation you need to have MuseScore installed on your system.
Or provide the path to the MuseScore executable.
-i XYZ --musescore_path "C:/Program Files/MuseScore 4/bin/MuseScore4.exe"
This defines the format version of the UltraStar.txt file. For more info see Official UltraStar format specification.
You can choose between 3 different format versions. The default is 1.0.0.
0.3.0is the old format version. Use this if you have problems with the new format.1.0.0is the current format version.1.1.0is the upcoming format version. It is not finished yet.
-i XYZ --format_version 1.0.0
The score that the singer in the audio would receive will be measured. You get 2 scores, simple and accurate. You wonder where the difference is? Ultrastar is not interested in pitch hights. As long as it is in the pitch range A-G you get one point. This makes sense for the game, because otherwise men don't get points for high female voices and women don't get points for low male voices. Accurate is the real tone specified in the txt. I had txt files where the pitch was in a range not singable by humans, but you could still reach the 10k points in the game. The accuracy is important here, because from this MIDI and sheet are created. And you also want to have accurate files
With a GPU you can speed up the process. Also the quality of the transcription and pitching is better.
You need a cuda device for this to work. Sorry, there is no cuda device for macOS.
It is optional (but recommended) to install the cuda driver for your gpu: see driver.
Install torch with cuda separately in your venv. See tourch+cuda.
Also check you GPU cuda support. See cuda support
Command for pip:
pip3 install torch==2.0.1+cu117 torchvision==0.15.2+cu117 torchaudio==2.0.2+cu117 --index-url https://download.pytorch.org/whl/cu117
When you want to use conda instead you need a different installation command.
The pitch tracker used by UltraSinger (crepe) uses TensorFlow as its backend. TensorFlow dropped GPU support for Windows for versions >2.10 as you can see in this release note and their installation instructions.
For now UltraSinger runs the latest version available that still supports GPUs on windows.
For running later versions of TensorFlow on windows while still taking advantage of GPU support the suggested solution is:
- install WSL2
- within the Ubuntu WSL2 installation
- run
sudo apt update && sudo apt install nvidia-cuda-toolkit - follow the setup instructions for UltraSinger at the top of this document
- run
If something crashes because of low VRAM then use a smaller model.
Whisper needs more than 8GB VRAM in the large model!
You can also force cpu usage with the extra option --force_cpu.
to run the docker run git clone https://github.com/rakuri255/UltraSinger.git
enter the UltraSinger folder.
run this command to build the docker
docker build -t ultrasinger . make sure to include the "." at the end
let this run till complete.
then run this command
docker run --gpus all -it --name UltraSinger -v $pwd/src/output:/app/src/output ultrasinger
Docker-Compose
there are two files that you can pick from.
cd into docker-compose folder and then cd into Nvidia or NonGPU
Run docker-compose up to download and setup
Nvidia is for if you have a nvidia gpu to use with UltraSinger. NonGPU is for if you wish to only use the CPU for UltraSinger.
Output
by default the docker-compose will setup the output folder as /output inside the docker.
on the host machine it will map to the folder with the docker-compose.yml file under output
you may chnage this by editing the docker-compose.yml
to edit the file.
use any text editor you wish. i would recoment nano.
run nano docker-compose.yml
then change this line
- ./output:/app/UltraSinger/src/output
to anything you line for on your host machine.
- /yourfolderpathhere:/app/UltraSinger/src/output
sample
- /mnt/user/appdata/UltraSinger:/output
note the blank space before the -
formating is important here in this file.
this will create and drop you into the docker.
now run this command.
python3 UltraSinger.py -i file
or
python3 UltraSinger.py -i youtube_url
to use mp3's in the folder you git cloned you must place all songs you like in UltraSinger/src/output.
this will be the place for youtube links aswell.
to quit the docker just type exit.
to reenter docker run this command
docker start UltraSinger && Docker exec -it UltraSinger /bin/bash
