symbtrdataextractor Python tools for extracting relevant (meta)data from SymbTr-scores Introduction This repository contains to algorithms to extract metadata, related music knowledge and section information from SymbTr-scores and related information sources. Currently you can: Obtain the makam, usul, form, name and composer of the given SymbTr score Extract section boundaries from both the implicit and explicit section information given in the SymbTr scores Extract annotated phrase boundaries in the SymbTr scores Add user provided segment boundaries in the SymbTr scores Analyse the melody and the lyrics of the sections, phrase annotations and user provided segments, and apply semiotic labels Query relevant metadata from MusicBrainz, if the MBID is supplied. Read the metadata stored in the header of the mu2 file. If you are using this code for academic purposes please cite the software as: Sertan Senturk. (2016). symbtrdataextractor: symbtrdataextractor v2.0.0. Zenodo. 10.5281/zenodo.50033 Usage Extracting (meta)data from the txt-score: from symbtrdataextractor.dataextractor import DataExtractor extractor = DataExtractor( melody_sim_thres=0.7, lyrics_sim_thres=0.7, save_structure_sim=True, extract_all_labels=False, crop_consec_bounds=True, get_recording_rels=False, print_warnings=True) """ Inputs ---------- melody_sim_thres : (optional) the maximum similarity threshold for two melodic stuctures to be considered as variant of each other. The default is 0.75. lyrics_sim_thres : (optional) the maximum similarity threshold for two lyric stuctures to be considered as variant of each other. The default is 0.75. save_structure_sim : (optional) boolean to add the melodic and lyrics similarity between each section and segment to the output or not extract_all_labels: (optional) boolean to treat all (explicit) annotations in the lyrics as a section or not (e.g. INSTRUMENTATION labels). The default is False. crop_consec_bounds: (optional) remove the first of the two consecutive boundaries inside the user given segmentation boundaries get_recording_rels: (optional) boolean to extract the relevant recording relations from MusicBrainz. The default is False. print_warnings : (optional) boolean to print possible warnings during reading the scores. Note that errors will always be printed. The default is True """ txt_data, is_data_valid = extractor.extract(txt_filename, symbtr_name=scorename, segment_note_bound_idx=auto_seg_bounds, mbid=mbid) """ Inputs ---------- txt_filename : the filepath of the SymbTr-txt score symbtr_name : (optional) the SymbTr-name in the "makam--form--usul--name--composer" format. mbid : (optional) the work or recording mbid of the composition/performance related to the score segment_note_bound_idx : (optional) user provided segment boundaries. NOTE - the indexing should start from 1, as this is the convention in the SymbTr-txt scores. TIP - makam-symbolic-phrase-segmentation package (https://github.com/MTG/makam-symbolic-phrase-segmentation) can be used to segment the SymbTr-txt scores automatically. """ Important note: For the sake of consistency with the indexing in the SymbTr-txt scores, the output note indices start from 1, not 0! Likewise, segment_note_bound_idx input should also obey to the 1 indexing convention, otherwise the semiotic labels will be wrong. Extracting metadata stored in the mu2 headers: from symbtrdataextractor.reader.mu2 import Mu2Reader mu2_header, header_row, is_header_valid = Mu2Reader.read( mu2_filename, symbtr_name=scorename) """ Inputs ---------- mu2filename : the filepath of the mu2 score symbtr_name : (optional) the SymbTr-name in the "makam--form--usul--name--composer" format. """ For an interactive demo please refer to extractsymbtrdata.ipynb Installation If you want to install symbtrmetadataextractor, it is recommended to install symbtrmetadataextractor and dependencies into a virtualenv. In the terminal, do the following: virtualenv env source env/bin/activate python setup.py install If you want to be able to edit files and have the changes be reflected, then install the repository like this instead: pip install -e . Now you can install the rest of the dependencies: pip install -r requirements Changelog Melodic organization is not replaced by the instrumental section names anymore Added melodic and lyrics similarity to output Default similarity threshold is lowered to 0.7 from 0.75 Fixed typos (lyric -> lyrics) in variable names and output dictionary keys. Improved warnings Authors Sertan Senturk contact@sertansenturk.com Reference Thesis