A pure-python implementation of MV2H metric, the original repo can be found here.
For a more easily use with python frameworks, like PyTorch and Tensorflow we implement this repo.
Using by shell:
pyMV2H
Usage:
pyMV2H midi_converter -i <input_dir> -o <output_dir>
pyMV2H compare_files -g <reference_file> -t <transcription_file> [-a] [-p]
pyMV2H -h | --help
pyMV2H --version
Options:
-h --help Show this screen.
--version Show version.
-o --output The output file
-t The transcription file
-g The reference file
-a Align both files
-p Set the DTW insertion and deletion penalties
Examples:
pyMV2H compare_files -g <reference_file> -t <transcription_file> -a -p 2.35
pyMV2H midi_converter -i <midi_file> -o <output_dir>
Help:
For help using this tool, please open an issue on the Github repository:
https://github.com/lucasmpaim/pyMV2HUsing by python code:
pip install pyMV2HConvert midi files:
from pyMV2H.converter.midi_converter import MidiConverter as Converter
reference_midi_file = '<midi dir>'
reference_file = '<reference txt output>'
converter = Converter(file=reference_midi_file, output=reference_file)
converter.convert_file()Compare files:
from pyMV2H.utils.music import Music
from pyMV2H.metrics.mv2h import mv2h
reference_file = Music.from_file('<reference_file_dir>')
transcription_file = Music.from_file('<transcription_file_dir>')
print(mv2h(reference_file, transcription_file))Please, cite the original article:
@inproceedings{McLeod:18a,
title={Evaluating automatic polyphonic music transcription},
author={McLeod, Andrew and Steedman, Mark},
booktitle={International Society for Music Information Retrieval Conference (ISMIR)},
year={2018},
pages={42--49}
}For convenience at this point, python version doesn't include support for multi-tempo or chords, this repo is implemented for MIDI AMT's algorithms research, and the original repo doesn't extract this info from MIDI files. ref.
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Support for multi-tempo
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Reduce the number of dependencies
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Add support to chords
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MusicXML parser
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Write unit tests
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Increase the number of Unit Tests
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Refactor code to be more pythonic
