wehub-resource-sync
74 lines
2.7 KiB
Python
74 lines
2.7 KiB
Python
# Env - chords_extraction on devfair
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import pickle
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import argparse
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from chord_extractor.extractors import Chordino # type: ignore
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from chord_extractor import clear_conversion_cache, LabelledChordSequence # type: ignore
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import os
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from tqdm import tqdm
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def parse_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--src_jsonl_file', type=str, required=True,
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help='abs path to .jsonl file containing list of absolute file paths seperated by new line')
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parser.add_argument('--target_output_dir', type=str, required=True,
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help='target directory to save parsed chord files to, individual files will be saved inside')
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parser.add_argument("--override", action="store_true")
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args = parser.parse_args()
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return args
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def save_to_db_cb(tgt_dir: str):
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# Every time one of the files has had chords extracted, receive the chords here
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# along with the name of the original file and then run some logic here, e.g. to
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# save the latest data to DB
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def inner(results: LabelledChordSequence):
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path = results.id.split(".wav")
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sequence = [(item.chord, item.timestamp) for item in results.sequence]
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if len(path) != 2:
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print("Something")
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print(path)
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else:
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file_idx = path[0].split("/")[-1]
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with open(f"{tgt_dir}/{file_idx}.chords", "wb") as f:
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# dump the object to the file
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pickle.dump(sequence, f)
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return inner
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if __name__ == "__main__":
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'''This script extracts chord data from a list of audio files using the Chordino extractor,
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and saves the extracted chords to individual files in a target directory.'''
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print("parsed args")
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args = parse_args()
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files_to_extract_from = list()
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with open(args.src_jsonl_file, "r") as json_file:
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for line in tqdm(json_file.readlines()):
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# fpath = json.loads(line.replace("\n", ""))['path']
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fpath = line.replace("\n", "")
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if not args.override:
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fname = fpath.split("/")[-1].replace(".wav", ".chords")
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if os.path.exists(f"{args.target_output_dir}/{fname}"):
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continue
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files_to_extract_from.append(line.replace("\n", ""))
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print(f"num files to parse: {len(files_to_extract_from)}")
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chordino = Chordino()
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# Optionally clear cache of file conversions (e.g. wav files that have been converted from midi)
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clear_conversion_cache()
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# Run bulk extraction
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res = chordino.extract_many(
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files_to_extract_from,
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callback=save_to_db_cb(args.target_output_dir),
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num_extractors=80,
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num_preprocessors=80,
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max_files_in_cache=400,
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stop_on_error=False,
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)
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