def plot_toniness():
with open('assets/phonetics/model.json') as f:
model = json.loads(f.read())
plot = dpc.Plot()
i = 0
for phonetic, info in model.items():
for j, frame in enumerate(info['frames']):
plot.text(f'{phonetic}{j}', i, frame['toniness'])
i += 1
plot.show()
os.environ['PHONETIC_ENCODER_RECORDING_PATH'] = args.recording_path
import phonetic_encoder as pe
dlal.comm_set(None)
run_size = pe.audio.run_size()
duration = pe.filea.duration()
samples = 0
features = []
while samples < duration:
pe.audio.run()
sample = pe.sample_system()
params = pe.parameterize(*sample)
features.append(dlal.speech.get_features(params))
samples += run_size
print(f'{samples} / {duration}', end='\r')
print()
plot = dpc.Plot(
transform=dpc.transforms.Grid(duration // 64, 1, 1),
primitive=dpc.primitives.Line(),
)
for i, feature in enumerate(dlal.speech.FEATURES):
plot.text(feature,
**plot.transform(0, 0, None, plot.series),
r=1.0,
g=0,
b=0)
plot.plot([j[i] for j in features])
plot.show()
def random_color_for_position(x, y):
r = random.randint(0, 255) % (x - 512 + 1)
g = random.randint(0, 255) % (y - 768 + 1)
b = random.randint(0, 255)
a = random.randint(0, 255)
return (r, g, b, a)
#===== general =====#
if args.case in ['1', 'general', 'all']:
print(
'plotting random dots, lines, text, in (x=512..767, y=768..1023), reddish on right, greenish on top'
)
plot = dpc.Plot('test')
for i in range(10000):
x, y = random_position()
r, g, b, a = random_color_for_position(x, y)
choice = random.choice(['point', 'line', 'text'])
if choice == 'point':
plot.point(x, y, r, g, b, a)
elif choice == 'line':
xf, yf = random_position()
plot.line(x, y, xf, yf, r, g, b, a // 4)
elif choice == 'text' and not random.randint(0, 100):
s = ''.join(
random.choice(string.ascii_letters + string.digits)
for i in range(random.randint(1, 8)))
plot.text(s, x, y, r, g, b, a)
def __init__(self):
self.plot = dpc.Plot()
self.xy_prev = None
4.48,
4.58,
4.65,
4.84,
4.88,
4.99,
5.05,
]
sample_rate = pe.audio.sample_rate()
run_size = pe.audio.run_size()
duration = pe.filea.duration()
samples = 0
if args.plot:
plot = dpc.Plot()
#===== helpers =====#
def serialize_features(features):
parts = ['{:02x}'.format(math.floor(i * 255)) for i in features]
return ''.join([''.join(i) for i in zip(*parts)])
def bucketize(features):
if features[0] < 0.3:
return serialize_features(features[:5])
else:
return serialize_features((features[0], ) + features[5:])