def run(mp3_file_path):
os.makedirs('static', exist_ok=True)
logger.info(
'run function mp3_file_path argument : {}'.format(mp3_file_path))
voca = False # True means large vocabulary label type
if voca == True:
config.feature['large_voca'] = True
config.model['num_chords'] = 170
model_file = 'test/btc_model_large_voca.pt'
idx_to_chord = idx2voca_chord()
logger.info("label type: large voca")
else:
model_file = 'test/btc_model.pt'
idx_to_chord = idx2chord
logger.info("label type: Major and minor")
model = BTC_model(config=config.model).to(device)
# Load model
if os.path.isfile(model_file):
checkpoint = torch.load(model_file, map_location='cpu')
mean = checkpoint['mean']
std = checkpoint['std']
model.load_state_dict(checkpoint['model'])
logger.info("restore model")
# clean mp3 filename
base_path, song_name = os.path.split(mp3_file_path)
new_name = "".join(x for x in song_name[:-4] if x.isalnum())
new_path = os.path.join(base_path, new_name + '.mp3')
shutil.move(mp3_file_path, new_path)
filename = new_path[:-4]
logger.info('cleaned filename : {}'.format(filename))
# load mp3 and get features
feature, feature_per_second, song_length_second = audio_file_to_features(
new_path, config)
logger.info("audio file loaded and feature computation success")
# Majmin type chord recognition
feature = feature.T
feature = (feature - mean) / std
time_unit = feature_per_second
n_timestep = config.model['timestep']
num_pad = n_timestep - (feature.shape[0] % n_timestep)
feature = np.pad(feature, ((0, num_pad), (0, 0)),
mode="constant",
constant_values=0)
num_instance = feature.shape[0] // n_timestep
start_time = 0.0
lines = []
with torch.no_grad():
model.eval()
feature = torch.tensor(feature,
dtype=torch.float32).unsqueeze(0).to(device)
for t in range(num_instance):
self_attn_output, _ = model.self_attn_layers(
feature[:, n_timestep * t:n_timestep * (t + 1), :])
prediction, _ = model.output_layer(self_attn_output)
prediction = prediction.squeeze()
for i in range(n_timestep):
if t == 0 and i == 0:
prev_chord = prediction[i].item()
continue
if prediction[i].item() != prev_chord:
lines.append(
'%.6f %.6f %s\n' %
(start_time, time_unit *
(n_timestep * t + i), idx_to_chord[prev_chord]))
start_time = time_unit * (n_timestep * t + i)
prev_chord = prediction[i].item()
if t == num_instance - 1 and i + num_pad == n_timestep:
if start_time != time_unit * (n_timestep * t + i):
lines.append(
'%.6f %.6f %s\n' %
(start_time, time_unit *
(n_timestep * t + i), idx_to_chord[prev_chord]))
break
# lab file write
# test_result_path = 'test/{}.lab'.format(filename)
test_result_path = './{}.lab'.format(filename)
with open(test_result_path, 'w') as f:
for line in lines:
f.write(line)
logger.info("label file saved")
# read in lab file into dataframe
logger.info('read in label file to pandas dataframe')
df = pd.read_csv('./{}.lab'.format(filename), header=None, delimiter=' ')
df.columns = ['start', 'stop', 'chord']
# calculate chord duration
df['duration'] = df.stop - df.start
# discard the first and last non chords
df = df.iloc[1:-2].copy(deep=True)
# set any non-chords to the previous chord
for index in df.index.values:
chord_ = df.at[index, 'chord']
if chord_ == 'N':
timestamp = df.at[index, 'stop']
df.at[index - 1, 'stop'] = timestamp
df.drop(index, inplace=True)
logger.info('start processing the chords into midi')
try:
s1 = stream.Stream()
s1.append(chord.Chord(["C4", "G4", "E-5"]))
for index in df.index.values[1:20]: #[1:-2]:
chord_ = df.at[index, 'chord']
kind = 'major'
if ':min' in chord_:
kind = 'minor'
chord_ = chord_.split(':min')[0]
# multiply duration by 2. Don't know why this works but it does
duration_ = 2 * df.at[index, 'duration']
chord21 = harmony.ChordSymbol(root=chord_,
kind=kind,
duration=duration_)
chord21.writeAsChord = True
s1.append(chord21)
except Exception as e:
logger.info(e)
logger.info('complete')
logger.info('save midi to disk')
fp = s1.write('midi', fp='{}.mid'.format(filename))
# read in midi
sheet = midi.Midi('{}.mid'.format(filename))
# get the video representation
clip = video.midi_videoclip(sheet)
# save the video without audio
clip.write_videofile('{}.webm'.format(filename), codec='libvpx', fps=20)
os.makedirs('sf2', exist_ok=True)
# download the libsynth soundfont if it doesn't exist
if not os.path.exists('sf2/FluidR3_GM.sf2'):
cmd = 'wget -O sf2/FluidR3_GM.sf2 https://github.com/urish/cinto/raw/master/media/FluidR3%20GM.sf2'
subprocess.call(cmd, shell=True)
# load the soundfont
fs = FluidSynth('sf2/FluidR3_GM.sf2') # arch
# convert the midi to audio
fs.midi_to_audio('{}.mid'.format(filename), '{}.wav'.format(filename))
# combine the audio and video
cmd = 'ffmpeg -y -i {}.wav -r 30 -i {}.webm -filter:a aresample=async=1 -c:a flac -c:v copy {}.mkv'.format(
filename, filename, filename)
subprocess.call(cmd, shell=True) # "Muxing Done
# delay the audio by 4% (this aligns the audio to the video)
cmd = 'ffmpeg -i {}.mkv -filter:a "atempo=0.96" -vn -y {}.wav'.format(
filename, filename)
subprocess.call(cmd, shell=True)
# strip the path to get the filename
filename_only = os.path.splitext(os.path.basename(filename))[0]
# combine the video and the delayed audio
cmd = 'ffmpeg -y -i {}.wav -r 30 -i {}.webm -filter:a aresample=async=1 -c:a flac -c:v copy static/{}.mkv'.format(
filename, filename, filename_only)
subprocess.call(cmd, shell=True) # "Muxing Done
logger.info('Muxing Done')
return 'static/{}.mkv'.format(filename_only)
# hyperparameters
parser = argparse.ArgumentParser()
parser.add_argument('--voca',
default=True,
type=lambda x: (str(x).lower() == 'true'))
parser.add_argument('--audio_dir', type=str, default='./test')
parser.add_argument('--save_dir', type=str, default='./test')
args = parser.parse_args()
config = HParams.load("run_config.yaml")
if args.voca is True:
config.feature['large_voca'] = True
config.model['num_chords'] = 170
model_file = './test/btc_model_large_voca.pt'
idx_to_chord = idx2voca_chord()
logger.info("label type: large voca")
else:
model_file = './test/btc_model.pt'
idx_to_chord = idx2chord
logger.info("label type: Major and minor")
model = BTC_model(config=config.model).to(device)
# Load model
if os.path.isfile(model_file):
checkpoint = torch.load(model_file, map_location=torch.device('cpu'))
mean = checkpoint['mean']
std = checkpoint['std']
model.load_state_dict(checkpoint['model'])
logger.info("restore model")