if len(d['hash_sequence']) > MIN_SEQUENCE_LENGTH]
# Create a big matrix of the embeddings
msd_embeddings = np.concatenate(
[d['embedding'] for d in msd_embedding_datas], axis=0)
# Construct paths to each feature file
msd_feature_paths = [
os.path.join(DATA_PATH, 'msd', 'h5', d['path']) + '.h5'
for d in msd_sequence_datas]
# Extract all sequences
msd_sequences = [d['hash_sequence'] for d in msd_sequence_datas]
# Extract all IDs
msd_ids = [d['id'] for d in msd_sequence_datas]
# 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'))
# Collect a list of valid MIDI entries in the provided mapping
valid_midi_list = []
for midi_md5 in midi_msd_mapping:
midi_entry = [entry for entry in midi_list if entry['id'] == midi_md5]
# Edge case - no entry in the MIDI list for this md5
if len(midi_entry) == 0:
continue
else:
valid_midi_list.append(midi_entry[0])
# Load all pre-computed MIDI CQTs
midi_grams = [deepdish.io.load(os.path.join(DATA_PATH, 'clean_midi', 'h5',
entry['path'] + '.h5'))['gram']
# Create a big matrix of the embeddings
msd_embeddings = np.concatenate(
[d['embedding'] for d in msd_embedding_datas], axis=0)
# Construct paths to each feature file
msd_feature_paths = [
os.path.join(DATA_PATH, 'msd', 'h5', d['path']) + '.h5'
for d in msd_sequence_datas
]
# Extract all sequences
msd_sequences = [d['hash_sequence'] for d in msd_sequence_datas]
# Extract all IDs
msd_ids = [d['id'] for d in msd_sequence_datas]
# 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'))
# Collect a list of valid MIDI entries in the provided mapping
valid_midi_list = []
for midi_md5 in midi_msd_mapping:
midi_entry = [entry for entry in midi_list if entry['id'] == midi_md5]
# Edge case - no entry in the MIDI list for this md5
if len(midi_entry) == 0:
continue
else:
valid_midi_list.append(midi_entry[0])
# Load all pre-computed MIDI CQTs
midi_grams = [
deepdish.io.load(