def load_files(self):
self.buf = []
done = 0
bad_files = []
for filename in self.filenames:
# update the progress bar to show it's working on the current file
bothoven_globals.progress_bar(done, self.filenames.size,
"Buffering " + filename + "...")
# for progress tracking
done += 1
try:
file_buf = self.array_builder_type(filename).mid_to_array()
except Exception as e:
bad_files.append(done - 1) # done - 1 is the index
print("\nError!", e)
print()
continue
# slap this file's buffer onto the back of our running buffer
self.buf.append(file_buf)
# finish off the progress bar
bothoven_globals.progress_bar(done, self.filenames.size,
"Buffering complete!")
self.filenames = np.delete(self.filenames, bad_files)
def build_csv(cache_dir):
files = get_filenames(cache_dir, [".pkl"])
done = 0
csv_path = os.path.join(cache_dir, "key_signatures.csv")
if os.path.exists(csv_path) and os.path.getsize(csv_path) > 0:
df = pd.read_csv(csv_path, index_col="path")
else:
df = pd.DataFrame(columns=["tonic", "mode"])
df.index.name = "path"
csv_f = open(csv_path, "a")
for file in files:
progress_bar(done, len(files), file)
done += 1
if file in df.index.values:
continue
score = pickle_load(file, verbose=False)
key = score.analyze('key')
df.loc[file] = [key.tonic.name, key.mode]
s = ",".join([f'"{file}"', key.tonic.name, key.mode]) + "\n"
csv_f.write(s)
csv_f.flush()
csv_f.close()
progress_bar(done, len(files), "Done!")
return df
def cache(in_dir, out_dir):
files = get_filenames(in_dir, [".mid", ".midi", ".smf"])
done = 0
csv_path = os.path.join(out_dir, "key_signatures.csv")
os.makedirs(out_dir, exist_ok=True)
if os.path.exists(csv_path) and os.path.getsize(csv_path) > 0:
df = pd.read_csv(csv_path, index_col="path")
needs_column_header = False
else:
df = pd.DataFrame(columns=["tonic", "mode"])
df.index.name = "path"
needs_column_header = True
csv_f = open(csv_path, "a")
if needs_column_header:
csv_f.write("path,tonic,mode\n")
for file in files:
progress_bar(done, len(files), "Loading:".ljust(11) + file + "...")
done += 1
relative_path = file[len(in_dir):]
if relative_path[0] == "/":
relative_path = relative_path[1:]
new_file = os.path.join(out_dir, relative_path) + ".pkl"
if new_file in df.index.values:
continue
new_dir = os.path.dirname(new_file)
os.makedirs(new_dir, exist_ok=True)
try:
score = m21.converter.parse(file)
except Exception as e:
continue
progress_bar(done - 1, len(files),
"Analyzing:".ljust(12) + file + "...")
key = score.analyze('key')
csv_f.write(",".join([f'"{new_file}"', key.tonic.name, key.mode]) +
"\n")
csv_f.flush()
m21.converter.freeze(score, fp=new_file)
csv_f.close()
progress_bar(done, len(files), "Done!")
def doit(seed_file):
print(" -> Loading seed song...")
seed = song_buf_to_onehot(
Music21ArrayBuilder("midi/format0_maj/" + seed_file).mid_to_array(),
dataset)
note_temp = .1
while note_temp <= 1.5:
duration_temp = .1
while duration_temp <= 1.5:
output_file = f"output/{seed_file[:-12]}/note_temp-{float(round(note_temp, 1))}__duration_temp-{float(round(duration_temp, 1))}.mid"
print(f"Saving '{output_file}'...")
new_song = np.vstack([
seed[start_i:start_i + NUM_STEPS],
np.zeros((num_predictions, dataset.num_features))
])
score = music21.stream.Score()
part = music21.stream.Part()
part.id = 'part1'
current_list = part
notes_in_chord = 0
for i in range(num_predictions):
p = model.predict(
np.array([seed[i + start_i:i + start_i + NUM_STEPS]]))
p_note = sample(p[0][0], temperature=note_temp)
p_duration = sample(p[1][0], temperature=duration_temp)
p_offset = np.argmax(p[2][0])
new_song[i + NUM_STEPS, p_note] = 1
new_song[i + NUM_STEPS, p_duration + p[0].shape[1]] = 1
new_song[i + NUM_STEPS,
p_offset + p[0].shape[1] + p[1].shape[1]] = 1
note = dataset.one_hot_to_note[p_note]
duration = dataset.one_hot_to_duration[p_duration]
offset = dataset.one_hot_to_offset[p_offset]
if offset != -1 and offset != -2:
if note == -3 or duration == -3 or offset == -3:
if notes_in_chord:
part.append(music21.chord.Chord(current_list))
notes_in_chord = 0
current_list = []
elif note == -4 or duration == -4 or offset == -4:
if notes_in_chord:
part.append(music21.chord.Chord(current_list))
notes_in_chord = 0
current_list = part
elif note >= 0 and duration >= 0:
if notes_in_chord >= 4:
part.append(music21.chord.Chord(current_list))
notes_in_chord = 0
current_list = part
current_list.append(
music21.note.Note(note, quarterLength=duration))
if not current_list is part:
notes_in_chord += 1
progress_bar(i + 1,
num_predictions,
output_file,
clear_when_done=True)
score.insert(0, part)
os.makedirs(os.path.dirname(output_file), exist_ok=True)
score.write('midi', fp=output_file)
# upload_file(output_file)
duration_temp += 0.1
note_temp += 0.1
import os
from bothoven_globals import progress_bar
from functions.file_functions import get_filenames
in_dir = "/home/mark/Documents/raw_midi"
out_dir = "/media/mark/Data/Documents/python/bothoven/midi/monophonic"
good_programs = set(range(56, 80))
filenames = get_filenames(in_dir, [".mid", ".midi", ".smf"])
total = len(filenames)
done = 0
for filename in filenames:
progress_bar(done, total, filename)
done += 1
mid = None
try:
mid = mido.MidiFile(filename)
except KeyboardInterrupt as e:
exit(1)
except Exception as e:
print("There was an error reading", filename)
print(e)
continue
good_tracks = []
for track in mid.tracks: