ORI_FOLDER = '' # Replace with the path to your folder containing standard ABC notation files INTERLEAVED_FOLDER = '' # Output interleaved ABC notation files to this folder AUGMENTED_FOLDER = '' # Output key-augmented and rest-omitted ABC notation files to this folder EVAL_SPLIT = 0.1 # The ratio of eval data import os import re import json import shutil import random from tqdm import tqdm from abctoolkit.utils import ( remove_information_field, remove_bar_no_annotations, Quote_re, Barlines, extract_metadata_and_parts, extract_global_and_local_metadata, extract_barline_and_bartext_dict) from abctoolkit.convert import unidecode_abc_lines from abctoolkit.rotate import rotate_abc from abctoolkit.check import check_alignment_unrotated from abctoolkit.transpose import Key2index, transpose_an_abc_text os.makedirs(INTERLEAVED_FOLDER, exist_ok=True) os.makedirs(AUGMENTED_FOLDER, exist_ok=True) for key in Key2index.keys(): key_folder = os.path.join(AUGMENTED_FOLDER, key) os.makedirs(key_folder, exist_ok=True) def abc_preprocess_pipeline(abc_path): with open(abc_path, 'r', encoding='utf-8') as f: abc_lines = f.readlines() # delete blank lines abc_lines = [line for line in abc_lines if line.strip() != ''] # unidecode abc_lines = unidecode_abc_lines(abc_lines) # clean information field abc_lines = remove_information_field(abc_lines=abc_lines, info_fields=['X:', 'T:', 'C:', 'W:', 'w:', 'Z:', '%%MIDI']) # delete bar number annotations abc_lines = remove_bar_no_annotations(abc_lines) # delete \" for i, line in enumerate(abc_lines): if re.search(r'^[A-Za-z]:', line) or line.startswith('%'): continue else: if r'\"' in line: abc_lines[i] = abc_lines[i].replace(r'\"', '') # delete text annotations with quotes for i, line in enumerate(abc_lines): quote_contents = re.findall(Quote_re, line) for quote_content in quote_contents: for barline in Barlines: if barline in quote_content: line = line.replace(quote_content, '') abc_lines[i] = line # check bar alignment try: _, bar_no_equal_flag, _ = check_alignment_unrotated(abc_lines) if not bar_no_equal_flag: print(abc_path, 'Unequal bar number') raise Exception except: raise Exception # deal with text annotations: remove too long text annotations; remove consecutive non-alphabet/number characters for i, line in enumerate(abc_lines): quote_matches = re.findall(r'"[^"]*"', line) for match in quote_matches: if match == '""': line = line.replace(match, '') if match[1] in ['^', '_']: sub_string = match pattern = r'([^a-zA-Z0-9])\1+' sub_string = re.sub(pattern, r'\1', sub_string) if len(sub_string) <= 40: line = line.replace(match, sub_string) else: line = line.replace(match, '') abc_lines[i] = line abc_name = os.path.splitext(os.path.split(abc_path)[-1])[0] # transpose metadata_lines, part_text_dict = extract_metadata_and_parts(abc_lines) global_metadata_dict, local_metadata_dict = extract_global_and_local_metadata(metadata_lines) if global_metadata_dict['K'][0] == 'none': global_metadata_dict['K'][0] = 'C' ori_key = global_metadata_dict['K'][0] interleaved_abc = rotate_abc(abc_lines) interleaved_path = os.path.join(INTERLEAVED_FOLDER, abc_name + '.abc') with open(interleaved_path, 'w') as w: w.writelines(interleaved_abc) for key in Key2index.keys(): transposed_abc_text = transpose_an_abc_text(abc_lines, key) transposed_abc_lines = transposed_abc_text.split('\n') transposed_abc_lines = list(filter(None, transposed_abc_lines)) transposed_abc_lines = [line + '\n' for line in transposed_abc_lines] # rest reduction metadata_lines, prefix_dict, left_barline_dict, bar_text_dict, right_barline_dict = \ extract_barline_and_bartext_dict(transposed_abc_lines) reduced_abc_lines = metadata_lines for i in range(len(bar_text_dict['V:1'])): line = '' for symbol in prefix_dict.keys(): valid_flag = False for char in bar_text_dict[symbol][i]: if char.isalpha() and not char in ['Z', 'z', 'X', 'x']: valid_flag = True break if valid_flag: if i == 0: part_patch = '[' + symbol + ']' + prefix_dict[symbol] + left_barline_dict[symbol][0] + bar_text_dict[symbol][0] + right_barline_dict[symbol][0] else: part_patch = '[' + symbol + ']' + bar_text_dict[symbol][i] + right_barline_dict[symbol][i] line += part_patch line += '\n' reduced_abc_lines.append(line) reduced_abc_name = abc_name + '_' + key reduced_abc_path = os.path.join(AUGMENTED_FOLDER, key, reduced_abc_name + '.abc') with open(reduced_abc_path, 'w', encoding='utf-8') as w: w.writelines(reduced_abc_lines) return abc_name, ori_key if __name__ == '__main__': data = [] file_list = os.listdir(ORI_FOLDER) for file in tqdm(file_list): ori_abc_path = os.path.join(ORI_FOLDER, file) try: abc_name, ori_key = abc_preprocess_pipeline(ori_abc_path) except: print(ori_abc_path, 'failed to pre-process.') continue data.append({ 'path': os.path.join(AUGMENTED_FOLDER, abc_name), 'key': ori_key }) random.shuffle(data) eval_data = data[ : int(EVAL_SPLIT * len(data))] train_data = data[int(EVAL_SPLIT * len(data)) : ] data_index_path = AUGMENTED_FOLDER + '.jsonl' eval_index_path = AUGMENTED_FOLDER + '_eval.jsonl' train_index_path = AUGMENTED_FOLDER + '_train.jsonl' with open(data_index_path, 'w', encoding='utf-8') as w: for d in data: w.write(json.dumps(d) + '\n') with open(eval_index_path, 'w', encoding='utf-8') as w: for d in eval_data: w.write(json.dumps(d) + '\n') with open(train_index_path, 'w', encoding='utf-8') as w: for d in train_data: w.write(json.dumps(d) + '\n')