A clip/sample auto tuner
Project description
Installation/Requirements
All requirements are in requirements.txt. They are numpy for array manipulations, librosa for audio processing, and scipy for it's wavefile export.
Installation, currently, is all via github. To download the cli tool run
pip3.6 install tuning-fork-cli
or, if you just want the Python Library Code, run
pip3.6 install tuning-fork
This project does require python3.6 since I use mypy annotations to help me out.
CLI Usage
You need two things to run this program effectively: a sampled wavefile and a .music file.
To sample the wavefile into the .music run
tuning-fork (wavfile) (musicfile) ((bpm))
The bpm is optional, and specifying it will set the samplefile's length to be consistent with the bpm. BPM is automatically 100
Python Usage
To import the entire package just include
import tuning_fork ((as tf))
in your imports and all code will be loaded!
tuning_fork
itself contains methods for pitch shifting, so, to shift a wav to a .music, you can run
tf.sampleWAVFileIntoMusic("wavfilename", "musicfilename", (bpm))
and that will return a librosa style ndarray that represents the WAV encoding of the autotuned song.
Along with normal functions, tuning_fork
also exposes analysis and parseMusic.
-
analysis
- Deals with the analysis of a wavfile.
- Most useful exports are
startingNote
andstartingNoteFromFile
- These methods take in some reference to a wavfile (depending on the function) and returns the approximate starting note frequency of the song.
-
parseMusic
- Deals with parsing .music files
- Fairly useful all around, take a look around the source or the
help(parseMusic)
to find what will fit you!
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