def write_to_KB(fact):
"""
Post a tuple to the KB
"""
kb.addFact(myID, TAG_USER_EMOTION, 1, 100, False, fact)
return
def __main__():
myID = kb.register()
obj_from_stt = {
"TAG": TAG_USER_TRANSCRIPT,
"text": "We will build a great wall"
}
kb.addFact(myID, TAG_USER_TRANSCRIPT, 1, 100, False, obj_from_stt)
def __main__():
myID = kb.register()
obj_from_erasmus = {
TAG_ANSWER:
"Professor Gervasi is giving classes of the Smart Application course in room L on Wednesday at 14:00"
}
kb.addFact(myID, "GNLP_ANSWER", 1, 100, False, obj_from_erasmus)
from kb import addFact import sys import json idSource = sys.argv[1] infoSum = sys.argv[2] TTL = 1 reliability = 100 revisioning = True jsonFact = sys.argv[3] print(addFact(idSource, infoSum, 1, 100, True, json.loads(jsonFact)))
def write_to_KB(fact, tag):
"""
Post a tuple to the KB
"""
kb.addFact(myID, tag, 1, 100, False, fact)
return
def speech_to_text(queue):
"""
This function implements the translation from speech to text with online and offline services, and compute the
emotion related to the speech
:param queue: process shared queue
"""
myID = kb.register()
# TODO handle error of registration to KB
r = sr.Recognizer()
queue_transc = Queue()
while True:
# Data stored in the queue contain all the information needed to create AudioData object
timestamp, channels, sampleRate, bitPerSample, data = queue.get()
audio = sr.AudioData(data, sampleRate, bitPerSample/8)
#TODO detect language
#TODO add emotion from speech
emotion = None
# Transcribe audio with google speech recognition and sphinx
sphinx_rec = Process(target=recognize, args=("sphinx", audio, queue_transc, timestamp, r))
sphinx_rec.start()
google_rec = Process(target=recognize, args=("google", audio, queue_transc, timestamp, r))
google_rec.start()
req_err = False
res = queue_transc.get()
models_res = {"google": None, "sphinx": None}
while not res["timestamp"] == timestamp:
# this case ensure that the results is not related to old queries
res = queue_transc.get()
models_res[res["model"]] = res
if models_res["google"] is None:
res = queue_transc.get()
while not res["timestamp"] == timestamp:
# this case ensure that the results is not related to old queries
res = queue_transc.get()
models_res["google"] = res
if models_res["google"]["error"] == None:
# Add to KB Google result with timestamp and ID
print("Insert into KB only Google result")
print(kb.addFact(myID, "text_f_audio", 2, 50, 'false', {"TAG": "text_f_audio",
"ID": timestamp,
"timestamp": timestamp,
"text": models_res["google"]["text"],
"language":language,
"emotion": emotion})) #TODO adjust "text_f_audio", 2, 50, 'false'
else:
if models_res["sphinx"] == None:
res = queue_transc.get()
while not res["timestamp"] == timestamp:
# this case ensure that the results is not related to old queries
res = queue_transc.get()
models_res["sphinx"] = res
if models_res["sphinx"]["error"] == None:
# Add to KB Sphinx result with timestamp and ID
print("Insert into KB only Sphinx result")
print(kb.addFact(myID, "text_f_audio", 2, 50, 'false', {"TAG": "text_f_audio",
"ID": timestamp,
"timestamp": timestamp,
"text": models_res["sphinx"]["text"],
"language":language,
"emotion": emotion}))#TODO adjust "text_f_audio", 2, 50, 'false'
else:
# Add to KB that none of google and sphinx retrieved a result
print("Insert into KB that no Google or Sphinx result")
print(kb.addFact(myID, "text_f_audio", 2, 50, 'false', {"TAG": "text_f_audio",
"ID": timestamp,
"timestamp": timestamp,
"text": "",
"language":language,
"emotion": emotion})) #TODO adjust "text_f_audio", 2, 50, 'false' #TODO probably better way to define the error for other module