def __init__(self):
self.input_engine = STTEngine(
pause_threshold=SPEECH_RECOGNITION['pause_threshold'],
energy_theshold=SPEECH_RECOGNITION['energy_threshold'],
ambient_duration=SPEECH_RECOGNITION['ambient_duration'],
dynamic_energy_threshold=SPEECH_RECOGNITION[
'dynamic_energy_threshold'],
sr=sr) if GENERAL_SETTINGS['user_voice_input'] else TTTEngine()
self.console_manager = ConsoleManager(log_settings=ROOT_LOG_CONF, )
self.output_engine = TTSEngine(
console_manager=self.console_manager,
speech_response_enabled=GENERAL_SETTINGS['response_in_speech'])
self.response_creator = ResponseCreator()
self.skill_analyzer = SkillAnalyzer(
weight_measure=TfidfVectorizer,
similarity_measure=cosine_similarity,
args=ANALYZER['args'],
skills_=SKILLS,
sensitivity=ANALYZER['sensitivity'])
self.skill_controller = SkillController(
settings_=GENERAL_SETTINGS,
input_engine=self.input_engine,
analyzer=self.skill_analyzer,
control_skills=CONTROL_SKILLS,
)
def __init__(self, settings_):
self.settings = settings_
self.input_engine = STTEngine(
pause_threshold=self.settings.SPEECH_RECOGNITION.get(
'pause_threshold'),
energy_theshold=self.settings.SPEECH_RECOGNITION.get(
'energy_threshold'),
ambient_duration=self.settings.SPEECH_RECOGNITION.get(
'ambient_duration'),
dynamic_energy_threshold=self.settings.SPEECH_RECOGNITION.get(
'dynamic_energy_threshold'),
sr=sr) if self.settings.GENERAL_SETTINGS.get(
'input_mode') == InputMode.VOICE.value else TTTEngine()
self.console_manager = ConsoleManager(
log_settings=self.settings.ROOT_LOG_CONF, )
self.output_engine = TTSEngine(
console_manager=self.console_manager,
speech_response_enabled=self.settings.GENERAL_SETTINGS.get(
'response_in_speech'))
self.response_creator = ResponseCreator()
self.skill_analyzer = SkillAnalyzer(
weight_measure=TfidfVectorizer,
similarity_measure=cosine_similarity,
args=self.settings.SKILL_ANALYZER.get('args'),
skills_=SKILLS,
sensitivity=self.settings.SKILL_ANALYZER.get('sensitivity'))
class Processor:
def __init__(self):
self.input_engine = STTEngine(
pause_threshold=SPEECH_RECOGNITION['pause_threshold'],
energy_theshold=SPEECH_RECOGNITION['energy_threshold'],
ambient_duration=SPEECH_RECOGNITION['ambient_duration'],
dynamic_energy_threshold=SPEECH_RECOGNITION[
'dynamic_energy_threshold'],
sr=sr) if GENERAL_SETTINGS['user_voice_input'] else TTTEngine()
self.console_manager = ConsoleManager(log_settings=ROOT_LOG_CONF, )
self.output_engine = TTSEngine(
console_manager=self.console_manager,
speech_response_enabled=GENERAL_SETTINGS['response_in_speech'])
self.response_creator = ResponseCreator()
self.skill_analyzer = SkillAnalyzer(
weight_measure=TfidfVectorizer,
similarity_measure=cosine_similarity,
args=ANALYZER['args'],
skills_=SKILLS,
sensitivity=ANALYZER['sensitivity'])
self.skill_controller = SkillController(
settings_=GENERAL_SETTINGS,
input_engine=self.input_engine,
analyzer=self.skill_analyzer,
control_skills=CONTROL_SKILLS,
)
def run(self):
start_up()
while True:
self.skill_controller.wake_up_check()
if self.skill_controller.is_assistant_enabled: # Check if the assistant is waked up
self._process()
def _process(self):
self.skill_controller.get_transcript()
self.skill_controller.get_skills()
if self.skill_controller.to_execute:
response = self.response_creator.create_positive_response(
self.skill_controller.latest_voice_transcript)
else:
response = self.response_creator.create_negative_response(
self.skill_controller.latest_voice_transcript)
self.output_engine.assistant_response(response)
self.skill_controller.execute()
class Processor:
def __init__(self):
self.input_engine = SPEECH_ENGINES[SPEECH_RECOGNITION['recognizer']]()
self.console_manager = ConsoleManager(log_settings=ROOT_LOG_CONF, )
self.output_engine = TTSEngine(
console_manager=self.console_manager,
speech_response_enabled=GENERAL_SETTINGS['response_in_speech'])
self.response_creator = ResponseCreator()
self.skill_analyzer = SkillAnalyzer(
weight_measure=TfidfVectorizer,
similarity_measure=cosine_similarity,
args=ANALYZER['args'],
skills_=SKILLS,
sensitivity=ANALYZER['sensitivity'])
self.skill_controller = SkillController(
settings_=GENERAL_SETTINGS,
input_engine=self.input_engine,
analyzer=self.skill_analyzer,
control_skills=CONTROL_SKILLS,
)
def run(self):
start_up()
keyboard.add_hotkey(GENERAL_SETTINGS['wake_up_hotkey'], self._process)
keyboard.wait()
def _process(self):
print('Assistant has woken up')
self.skill_controller.get_transcript()
self.skill_controller.get_skills()
if self.skill_controller.to_execute:
response = self.response_creator.create_positive_response(
self.skill_controller.latest_voice_transcript)
else:
response = self.response_creator.create_negative_response(
self.skill_controller.latest_voice_transcript)
self.output_engine.assistant_response(response)
self.skill_controller.execute()
def __init__(self):
self.input_engine = SPEECH_ENGINES[SPEECH_RECOGNITION['recognizer']]()
self.console_manager = ConsoleManager(log_settings=ROOT_LOG_CONF, )
self.output_engine = TTSEngine(
console_manager=self.console_manager,
speech_response_enabled=GENERAL_SETTINGS['response_in_speech'])
self.response_creator = ResponseCreator()
self.skill_analyzer = SkillAnalyzer(
weight_measure=TfidfVectorizer,
similarity_measure=cosine_similarity,
args=ANALYZER['args'],
skills_=SKILLS,
sensitivity=ANALYZER['sensitivity'])
self.skill_controller = SkillController(
settings_=GENERAL_SETTINGS,
input_engine=self.input_engine,
analyzer=self.skill_analyzer,
control_skills=CONTROL_SKILLS,
)
class AssistantSkill:
first_activation = True
console_manager = ConsoleManager(
log_settings=ROOT_LOG_CONF,
)
tts_engine = TTSEngine(
console_manager=console_manager,
speech_response_enabled=GENERAL_SETTINGS['response_in_speech']
)
@classmethod
def response(cls, text):
cls.tts_engine.assistant_response(text)
@classmethod
def _extract_tags(cls, voice_transcript, skill_tags):
transcript_words = voice_transcript.split()
return set(transcript_words).intersection(skill_tags)
class Processor:
def __init__(self, settings_):
self.settings = settings_
self.input_engine = STTEngine(
pause_threshold=self.settings.SPEECH_RECOGNITION.get(
'pause_threshold'),
energy_theshold=self.settings.SPEECH_RECOGNITION.get(
'energy_threshold'),
ambient_duration=self.settings.SPEECH_RECOGNITION.get(
'ambient_duration'),
dynamic_energy_threshold=self.settings.SPEECH_RECOGNITION.get(
'dynamic_energy_threshold'),
sr=sr) if self.settings.GENERAL_SETTINGS.get(
'input_mode') == InputMode.VOICE.value else TTTEngine()
self.console_manager = ConsoleManager(
log_settings=self.settings.ROOT_LOG_CONF, )
self.output_engine = TTSEngine(
console_manager=self.console_manager,
speech_response_enabled=self.settings.GENERAL_SETTINGS.get(
'response_in_speech'))
self.response_creator = ResponseCreator()
self.skill_analyzer = SkillAnalyzer(
weight_measure=TfidfVectorizer,
similarity_measure=cosine_similarity,
args=self.settings.SKILL_ANALYZER.get('args'),
skills_=SKILLS,
sensitivity=self.settings.SKILL_ANALYZER.get('sensitivity'))
def run(self):
self._traped_until_assistant_is_enabled()
transcript = self.input_engine.recognize_input()
skill_to_execute = self._extract_skill(transcript)
response = self.response_creator.create_positive_response(transcript) if skill_to_execute \
else self.response_creator.create_negative_response(transcript)
self.output_engine.assistant_response(response)
self._execute_skill(skill_to_execute)
def _execute_skill(self, skill):
if skill:
try:
skill_method = skill.get('skill').get('skill')
logging.debug('Executing skill {0}'.format(skill))
skill_method(**skill)
except Exception as e:
logging.debug(
"Error with the execution of skill with message {0}".
format(e))
def _traped_until_assistant_is_enabled(self):
if self.settings.GENERAL_SETTINGS.get(
'input_mode') == InputMode.VOICE.value:
while not ExecutionState.is_ready_to_execute():
voice_transcript = self.input_engine.recognize_input()
transcript_words = voice_transcript.split()
enable_tag = set(transcript_words).intersection(
CONTROL_SKILLS.get('enable_assistant').get('tags'))
if bool(enable_tag):
CONTROL_SKILLS.get('enable_assistant').get('skill')()
ExecutionState.update()
def _extract_skill(self, transcript):
skill = self.skill_analyzer.extract(transcript)
if skill:
return {'voice_transcript': transcript, 'skill': skill}