def predict(self, data):
kurfile = Kurfile(self.get_model_path(), JinjaEngine())
kurfile.parse()
model = kurfile.get_model()
with DisableLogging(logging.WARNING):
model.backend.compile(model)
model.restore(self.get_path('weights'))
pdf, metrics = model.backend.evaluate(model,
data={'in': np.array([data])})
prediction = pdf['out'][0][0]
return prediction
def load():
spec_file = 'speech.yml'
w_file = 'weights'
spec = Kurfile(spec_file, JinjaEngine())
spec.parse()
model = spec.get_model()
model.backend.compile(model)
model.restore(w_file)
norm = Normalize(center=True, scale=True, rotate=True)
norm.restore('norm.yml')
trans = TranscriptHook()
rev = {0: ' ', 1: "'", 2: 'a', 3: 'b', 4: 'c', 5: 'd', 6: 'e', 7: 'f', 8: 'g', 9: 'h', 10: 'i', 11: 'j', 12: 'k', 13: 'l', 14: 'm', 15: 'n', 16: 'o',
17: 'p', 18: 'q', 19: 'r', 20: 's', 21: 't', 22: 'u', 23: 'v', 24: 'w', 25: 'x', 26: 'y', 27: 'z'}
blank = 28
return model, norm, trans, rev, blank
def passthrough_engine():
""" Returns a Jinja2 engine.
"""
return JinjaEngine()
def jinja_engine():
""" Returns a Jinja2 engine.
"""
return JinjaEngine()