def __init__(self, language = "en_GB"):
dialog_context_xml_path=os.path.join(os.path.dirname(__file__), '../../homodeus_external/xml_folder/dialog_context.xml')
self.dialog_context = ET.parse(dialog_context_xml_path).getroot()
self.language = language
# initialisation of global class variables
self.selected_context = ""
self.repeat = 0
self.relevant_info = ""
self.reach_deadline = False
self.timer = None
self.dialoguing_now = False
self.dialog_state = Dialog_state.begin
# the code works differently if it's run on the robot or not
param_name = rospy.search_param('on_robot')
self.on_robot = rospy.get_param(param_name,False)
# Goal input
self.input_bhvr_goal_context = rospy.Subscriber("/bhvr_input_goal_dialContext",data_class=String, callback=self.set_context_Cb,queue_size=10)
# Output
self.output_bhvr_result = rospy.Publisher("/bhvr_output_res_dialBool", Bool, queue_size=10)
self.output_bhvr_relevant = rospy.Publisher("/bhvr_output_res_dialRelevant", String, queue_size=10)
# dialog only use tts_server when it's run on the robot
if self.on_robot:
self.connect_to_tts_server()
self.speech_recognizer = sr.SpeechRecognizer()
def listen_commands(self):
text = "How I can help You."
speech = Speech()
speech.create_voice(text)
r = SpeechRecognizer()
command = (r.recognize()).lower()
print 'Recognized command: {0}'.format(command)
if 'weatcher' in command:
self.command_weatcher()
if 'autopilot' in command:
self.command_autopilot()
if 'dance' in command:
self.command_dance()
if 'exterminate' in command:
self.command_exterminate()
def __init__(self, language='en-US'):
"""
This method initializes the perception module by initializing the output topic and
the Speech_recognition object
Arguments
---------
language : string
The language used for the speech recognition. By default its
value is set to american english.
"""
#The output of the module
self.output_perc = rospy.Publisher("/proc_output_listenText",
String,
queue_size=10)
self.language = language
self.speech_recognizer = sr.SpeechRecognizer()
def listen_text(self):
r = SpeechRecognizer()
text = (r.recognize()).lower()
print 'Recognized text: {0}'.format(text)
if 'shadow' in text:
self.listen_commands()
import os
import subprocess
import time
import logging
import uuid
from speech_recognizer import SpeechRecognizer
from punctuator import Punctuator
from number_utils.text2numbers import TextToNumbers
speech_recognizer = SpeechRecognizer()
punctuator = Punctuator(model_path="data/punctuator")
text2numbers = TextToNumbers()
class FileHandler:
@staticmethod
def get_recognized_text(blob):
try:
filename = str(uuid.uuid4())
os.makedirs('./records', exist_ok=True)
new_record_path = os.path.join('./records', filename + '.webm')
blob.save(new_record_path)
new_filename = filename + '.wav'
converted_record_path = FileHandler.convert_to_wav(
new_record_path, new_filename)
response_models_result = FileHandler.get_models_result(
converted_record_path)
return 0, new_filename, response_models_result
except Exception as e:
logging.exception(e)
return 1, None, str(e)
def __init__(self, audio_stream):
self.speech_recognizer = SpeechRecognizer(audio_stream)
self.natural_language_processor = NaturalLanguageProcessor()