# ======================
parser = get_base_parser(
'Efficiently Trainable Text-to-Speech System Based on' +
'Deep Convolutional Networks with Guided Attention', SENTENCE,
SAVE_WAV_PATH)
# overwrite
parser.add_argument('--input',
'-i',
metavar='TEXT',
default=SENTENCE,
help='input text')
parser.add_argument('--ailia_audio',
action='store_true',
help='use ailia audio library')
args = update_parser(parser, check_input_type=False)
if args.ailia_audio:
from pytorch_dc_tts_utils_ailia import get_test_data, save_to_wav
else:
from pytorch_dc_tts_utils import get_test_data, save_to_wav
# ======================
# Main function
# ======================
def preprocess(SENTENCE):
L = get_test_data([SENTENCE], len(SENTENCE))
zeros = np.zeros((1, 80, 1), np.float32)
Y = zeros
A = None
MODEL_LISTS = ['small', 'large']
SLEEP_TIME = 0
# ======================
# Arguemnt Parser Config
# ======================
parser = get_base_parser('ImageNet classification Model', IMAGE_PATH, None)
parser.add_argument('-a',
'--arch',
metavar='ARCH',
default='small',
choices=MODEL_LISTS,
help='model lists: ' + ' | '.join(MODEL_LISTS) +
' (default: small)')
args = update_parser(parser)
# ======================
# Parameters 2
# ======================
WEIGHT_PATH = f'mobilenetv3_{args.arch}.onnx'
MODEL_PATH = WEIGHT_PATH + '.prototxt'
REMOTE_PATH = 'https://storage.googleapis.com/ailia-models/mobilenetv3/'
# ======================
# Main functions
# ======================
def recognize_from_image():
# prepare input data
input_data = load_image(args.input, (IMAGE_HEIGHT, IMAGE_WIDTH),
# ======================
# Arguemnt Parser Config
# ======================
parser = get_base_parser('CAIN', IMAGE_PATH, SAVE_IMAGE_PATH)
parser.add_argument('-i2',
'--input2',
metavar='IMAGE2',
default=None,
help='The second input image path.')
parser.add_argument('-hw',
metavar='HEIGHT,WIDTH',
default="256,448",
help='Specify the size to resize on video mode.')
args = update_parser(parser, large_model=True)
# ======================
# Main functions
# ======================
def preprocess(img):
im_h, im_w, _ = img.shape
ow, oh = im_w, im_h
if im_w % (1 << 7) != 0:
ow = (((im_w >> 7) + 1) << 7)
if im_h % (1 << 7) != 0:
oh = (((im_h >> 7) + 1) << 7)
