fragment = audio[start_idx : end_idx]
theme_descr = calculateThemeDescriptor(fragment)
print theme_descr
pool.add('theme_descriptors', theme_descr.tolist()[0])
# --------------------- Load all audio files ---------------------------
from songcollection import SongCollection
sc = SongCollection()
sc.load_directory('../music')
sc.load_directory('../moremusic')
sc.load_directory('../evenmoremusic')
sc.load_directory('../music/test')
songs = []
for song in sc.get_annotated():
song.open()
pool.add('song.themes', song.song_theme_descriptor.tolist()[0])
songs.append(song.title)
song.close()
# --------------------- Make nice plots ---------------------------
Y = pool['song.themes'] # All songs in "/music" and "/moremusic" libraries
X = pool['theme_descriptors'] # Evolution of mix theme descriptors
from scipy.spatial.distance import euclidean as euclidean_distance
for P1,P2 in zip(X[:-1,:],X[1:,:]):
distance_path = euclidean_distance(P1,P2)
num_songs_closer = 0
for P in Y:
cmd_split = str.split(raw_input('> : '), ' ')
except KeyboardInterrupt:
logger.info('Goodbye!')
break
cmd = cmd_split[0]
if cmd == 'loaddir':
if len(cmd_split) == 1:
logger.warning('Please provide a directory name to load!')
continue
elif not os.path.isdir(cmd_split[1]):
logger.warning(cmd_split[1] + ' is not a valid directory!')
continue
sc.load_directory(cmd_split[1])
logger.info(
str(len(sc.songs)) + ' songs loaded [annotated: ' +
str(len(sc.get_annotated())) + ']')
elif cmd == 'play':
if len(sc.get_annotated()) == 0:
logger.warning(
'Use the loaddir command to load some songs before playing!'
)
continue
if len(cmd_split) > 1 and cmd_split[1] == 'save':
logger.info('Saving this new mix to disk!')
save_mix = True
else:
save_mix = False
logger.info('Starting playback!')
try:
reader = csv.reader(csvfile)
for line in reader:
time = float(line[0])
annot_cur.append(time)
annotations.append(annot_cur)
return songs, annotations
#TODO make sure that these arrays are constructed and saved during training CV loop
y_val_all = np.load('singingvoice_y_val.bin.npy')
t_val_all = np.load('singingvoice_t_val.bin.npy')
y_true_all = np.load('singingvoice_y_train.bin.npy')
t_train_all = np.load('singingvoice_t_train.bin.npy')
masks = {}
for s in sc.get_annotated():
if s.title in t_val_all:
cur_song_mask = np.array([t == s.title for t in t_val_all])
y_val = y_val_all[cur_song_mask]
cur_song_mask_train = np.array([t == s.title for t in t_train_all])
y_true = y_true_all[cur_song_mask_train]
masks[s.title] = (y_val, y_true)
for title_1, y_tuple_1 in masks.iteritems():
song_1 = [s for s in sc.get_annotated() if s.title == title_1][0]
song_1.open()
# Combine all songs and report the accuracy on detecting overlaps in voice
def index_dj(request):
template = get_template("Index.html")
LOG_LEVEL = logging.DEBUG
LOGFORMAT = "%(log_color)s%(message)s%(reset)s"
logging.root.setLevel(LOG_LEVEL)
formatter = ColoredFormatter(LOGFORMAT)
stream = logging.StreamHandler()
stream.setLevel(LOG_LEVEL)
stream.setFormatter(formatter)
logger = logging.getLogger('colorlogger')
logger.setLevel(LOG_LEVEL)
logger.addHandler(stream)
sc = SongCollection()
tl = TrackLister(sc)
dj = DjController(tl)
essentia.log.infoActive = False
essentia.log.warningActive = False
if request.method == "POST":
cmd = request.POST.get("cmd", None)
cmd = str(cmd)
# cmd_split = str(cmd).split
# cmd = cmd_split[0]
while (True):
# try:
# cmd_split = str.split(input('> : '), ' ')
# except KeyboardInterrupt:
# logger.info('Goodbye!')
# break
# cmd = cmd_split[0]
# if cmd == 'loaddir':
# if len(cmd_split) == 1:
# return HttpResponse('Please provide a directory name to load!')
# continue
# elif not os.path.isdir(cmd_split[1]):
# return HttpResponse(cmd_split[1] + ' is not a valid directory!')
# continue
message = "abc"
sc.load_directory("/home/ddman/音樂/upload")
message = str(len(sc.songs)) + ' songs loaded [annotated: ' + str(
len(sc.get_annotated())) + ']'
if cmd == 'play':
if len(sc.get_annotated()) == 0:
message = 'Use the loaddir command to load some songs before playing!'
continue
# if len(cmd_split) > 1 and cmd_split[1] == 'save':
# message = 'Saving this new mix to disk!'
# save_mix = True
# else:
# save_mix = False
message = 'Starting playback!'
try:
dj.play(save_mix=False)
except Exception as e:
logger.error(e)
return render(request, "Index.html", locals())
elif cmd == 'pause':
message = 'Pausing playback!'
try:
dj.pause()
except Exception as e:
logger.error(e)
return render(request, "Index.html", locals())
elif cmd == 'skip' or cmd == 's':
message = 'Skipping to next segment...'
try:
dj.skipToNextSegment()
except Exception as e:
logger.error(e)
return render(request, "Index.html", locals())
elif cmd == 'stop':
message = 'Stopping playback!'
dj.stop()
return render(request, "Index.html", locals())
elif cmd == 'save':
message = 'Saving the next new mix!'
return render(request, "Index.html", locals())
elif cmd == 'showannotated':
message = 'Number of annotated songs ' + str(
len(sc.get_annotated()))
message = 'Number of unannotated songs ' + str(
len(sc.get_unannotated()))
return render(request, "Index.html", locals())
elif cmd == 'annotate':
message = 'Started annotating!'
sc.annotate()
message = 'Done annotating!'
return render(request, "Index.html", locals())
elif cmd == 'debug':
LOG_LEVEL = logging.DEBUG
logging.root.setLevel(LOG_LEVEL)
stream.setLevel(LOG_LEVEL)
logger.setLevel(LOG_LEVEL)
message = 'Enabled debug info. Use this command before playing, or it will have no effect.'
return render(request, "Index.html", locals())
elif cmd == 'mark':
dj.markCurrentMaster()
return render(request, "Index.html", locals())
elif cmd == "quit":
break
return render(request, "Index.html", locals())
else:
message = 'The command ' + str(cmd) + ' does not exist!'
return render(request, "Index.html", locals())
return render(request, "Index.html", locals())
pyAudio = pyaudio.PyAudio()
stream = pyAudio.open(format=pyaudio.paFloat32,
channels=1,
rate=44100,
output=True)
try:
title = sys.argv[1][len('singingvoice_predicted_'):-4]
y_train_all = np.load('singingvoice_y_train.bin.npy')
t_train_all = np.load('singingvoice_t_train.bin.npy')
print title
cur_song_mask = np.array([t == title for t in t_train_all])
y_train = y_train_all[cur_song_mask]
y_train = y_train[np.array([i % 4 != 0 for i in range(len(y_train))])]
y_pred = np.load('singingvoice_predicted_{}.npy'.format(title))
song = [s for s in sc.get_annotated() if s.title == title][0]
song.open()
plt.figure()
plt.fill_between(range(len(y_train[::3])),
np.logical_and(y_pred[0::3], y_pred[1::3],
y_pred[2::3]),
alpha=0.5,
color='blue')
plt.fill_between(range(len(y_train[::3])),
y_train[::3],
alpha=0.5,
color='red')
plt.show()
for y_true, y, t in zip(y_train[::4], y_pred[::4], song.downbeats):
print '{} {} {}'.format(y_true, y, t)
song.close()
