def execute(self, context, edge):
text = Text()
while len(text.text) == 0:
text = listen_and_transcribe(**self.listen_settings)
res = text.interpret(model=self.model) if self.interpret else text
context.set_step_data(self.step_name, res)
return edge.next()
def listen_and_transcribe(length=0, silence_len=0.5): """ Listen with the given parameters, but simulaneously stream the audio to the Aurora API, transcribe, and return a Text object. This reduces latency if you already know you want to convert the speech to text. :param length the length of time (seconds) to record for. If 0, it will record indefinitely, until the specified amount of silence :type length float :param silence_len the amount of silence (seconds) to allow before stoping (ignored if length != 0) :type silence_len float """ from auroraapi.text import Text return Text(get_stt(functools.partial(stream, length, silence_len), stream=True)["transcript"])
def playCaptcha(phrase): speech = Text(phrase).speech() speech.audio.play()
def test_create_no_argument(self): with pytest.raises(TypeError): t = Text()
def test_speech_empty_string(self):
with pytest.raises(APIException):
Text("").speech()
def test_speech(self):
t = Text("hello")
s = t.speech()
assert isinstance(s, Speech)
assert isinstance(s.audio, AudioFile)
assert len(s.audio.audio) > 0
def test_interpret_empty_string(self):
with pytest.raises(APIException):
Text("").interpret()
def test_interpret(self):
t = Text("hello")
i = t.interpret()
assert isinstance(i, Interpret)
assert i.intent == "greeting"
def test_speech(self):
with pytest.raises(APIException):
Text("test").speech()
def test_interpret(self):
with pytest.raises(APIException):
Text("test").interpret()
def test_create(self):
t = Text("test")
assert isinstance(t, Text)
assert t.text == "test"
def text(self):
""" Convert speech to text and get the prediction """
from auroraapi.text import Text
return Text(get_stt(self.audio)["transcript"])
def play_text_callback(text, *largs):
Text(text).speech().audio.play()