# Load in hash sequences (and metadata) for all MSD entries
msd_data = experiment_utils.load_precomputed_data(
msd_list, os.path.join(RESULTS_PATH, 'tpaa_msd_hash_sequences'))
# Ignore very short sequences
msd_data = [d for d in msd_data
if len(d['hash_sequence']) > MIN_SEQUENCE_LENGTH]
# Get a list of valid MIDI-MSD match pairs
midi_msd_mapping = experiment_utils.get_valid_matches(
os.path.join(RESULTS_PATH, '{}_pairs.csv'.format(SPLIT)),
SCORE_THRESHOLD,
os.path.join(RESULTS_PATH, 'clean_midi_aligned', 'h5'))
midi_datas, midi_index_mapping = experiment_utils.load_valid_midi_datas(
midi_msd_mapping, msd_data, midi_list,
os.path.join(RESULTS_PATH, 'tpaa_clean_midi_hash_sequences'))
# Run match_one_midi for each MIDI data and MSD index list
results = joblib.Parallel(n_jobs=11, verbose=51)(
joblib.delayed(experiment_utils.match_sequence)(
midi_datas[md5], msd_data, midi_index_mapping[md5], GULLY, PENALTY)
for md5 in midi_datas)
# Create DHS match results output path if it doesn't exist
output_path = os.path.join(RESULTS_PATH, 'tpaa_match_results')
if not os.path.exists(output_path):
os.makedirs(output_path)
# Save list of all matching results
results_file = os.path.join(output_path, '{}_results.h5'.format(SPLIT))
deepdish.io.save(results_file, results)
# Load in hash sequences (and metadata) for all MSD entries
msd_data = experiment_utils.load_precomputed_data(
msd_list, os.path.join(RESULTS_PATH, 'dhs_piano_msd_hash_sequences'))
# Ignore very short sequences
msd_data = [
d for d in msd_data if len(d['hash_sequence']) > MIN_SEQUENCE_LENGTH
]
# Get a list of valid MIDI-MSD match pairs
midi_msd_mapping = experiment_utils.get_valid_matches(
os.path.join(RESULTS_PATH,
'{}_pairs.csv'.format(SPLIT)), SCORE_THRESHOLD,
os.path.join(RESULTS_PATH, 'clean_midi_aligned', 'h5'))
midi_datas, midi_index_mapping = experiment_utils.load_valid_midi_datas(
midi_msd_mapping, msd_data, midi_list,
os.path.join(RESULTS_PATH, 'dhs_piano_clean_midi_hash_sequences'))
# Delete variables which are no longer used to conserve memory
del msd_index
del midi_index
del msd_list
del midi_list
del midi_msd_mapping
# Run match_one_midi for each MIDI data and MSD index list
results = joblib.Parallel(n_jobs=8, verbose=51)(
joblib.delayed(experiment_utils.match_sequence)(
midi_datas[md5], msd_data, midi_index_mapping[md5], GULLY, PENALTY)
for md5 in midi_datas)
os.path.join(DATA_PATH, 'clean_midi', 'index'))
with midi_index.searcher() as searcher:
midi_list = list(searcher.documents())
# Load in hash sequences (and metadata) for all MSD entries
msd_data = experiment_utils.load_precomputed_data(
msd_list, os.path.join(RESULTS_PATH, 'tdftm_msd_embeddings'))
# Get a list of valid MIDI-MSD match pairs
midi_msd_mapping = experiment_utils.get_valid_matches(
os.path.join(RESULTS_PATH, '{}_pairs.csv'.format(SPLIT)),
SCORE_THRESHOLD,
os.path.join(RESULTS_PATH, 'clean_midi_aligned', 'h5'))
midi_datas, midi_index_mapping = experiment_utils.load_valid_midi_datas(
midi_msd_mapping, msd_data, midi_list,
os.path.join(RESULTS_PATH, 'tdftm_clean_midi_embeddings'))
# Run match_one_midi for each MIDI data and MSD index list
results = [experiment_utils.match_embedding(
midi_datas[md5], msd_data, midi_index_mapping[md5])
for md5 in midi_datas]
# Create DHS match results output path if it doesn't exist
output_path = os.path.join(RESULTS_PATH, 'tdftm_match_results')
if not os.path.exists(output_path):
os.makedirs(output_path)
# Save list of all matching results
results_file = os.path.join(output_path, '{}_results.h5'.format(SPLIT))
deepdish.io.save(results_file, results)
os.path.join(DATA_PATH, 'clean_midi', 'index'))
with midi_index.searcher() as searcher:
midi_list = list(searcher.documents())
# Load in hash sequences (and metadata) for all MSD entries
msd_data = experiment_utils.load_precomputed_data(
msd_list, os.path.join(RESULTS_PATH, 'stats_msd_embeddings'))
# Get a list of valid MIDI-MSD match pairs
midi_msd_mapping = experiment_utils.get_valid_matches(
os.path.join(RESULTS_PATH, '{}_pairs.csv'.format(SPLIT)),
SCORE_THRESHOLD,
os.path.join(RESULTS_PATH, 'clean_midi_aligned', 'h5'))
midi_datas, midi_index_mapping = experiment_utils.load_valid_midi_datas(
midi_msd_mapping, msd_data, midi_list,
os.path.join(RESULTS_PATH, 'stats_clean_midi_embeddings'))
# Run match_one_midi for each MIDI data and MSD index list
results = [
experiment_utils.match_embedding(
midi_datas[md5], msd_data, midi_index_mapping[md5])
for md5 in midi_datas]
# Create DHS match results output path if it doesn't exist
output_path = os.path.join(RESULTS_PATH, 'stats_match_results')
if not os.path.exists(output_path):
os.makedirs(output_path)
# Save list of all matching results
results_file = os.path.join(output_path, '{}_results.h5'.format(SPLIT))
deepdish.io.save(results_file, results)