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# SPDX-License-Identifier: Apache-2.0
#
# Copyright (c) 2023 Malik Talha
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import grpc
import time
import threading
from generated import voice_agent_pb2
from generated import voice_agent_pb2_grpc
from agl_service_voiceagent.utils.audio_recorder import AudioRecorder
from agl_service_voiceagent.utils.wake_word import WakeWordDetector
from agl_service_voiceagent.utils.stt_model import STTModel
from agl_service_voiceagent.utils.config import get_config_value
from agl_service_voiceagent.nlu.snips_interface import SnipsInterface
from agl_service_voiceagent.nlu.rasa_interface import RASAInterface
from agl_service_voiceagent.utils.common import generate_unique_uuid, delete_file
class VoiceAgentServicer(voice_agent_pb2_grpc.VoiceAgentServiceServicer):
def __init__(self):
# Get the config values
self.service_version = get_config_value('SERVICE_VERSION')
self.wake_word = get_config_value('WAKE_WORD')
self.base_audio_dir = get_config_value('BASE_AUDIO_DIR')
self.channels = int(get_config_value('CHANNELS'))
self.sample_rate = int(get_config_value('SAMPLE_RATE'))
self.bits_per_sample = int(get_config_value('BITS_PER_SAMPLE'))
self.stt_model_path = get_config_value('STT_MODEL_PATH')
self.snips_model_path = get_config_value('SNIPS_MODEL_PATH')
self.rasa_model_path = get_config_value('RASA_MODEL_PATH')
self.rasa_server_port = int(get_config_value('RASA_SERVER_PORT'))
self.base_log_dir = get_config_value('BASE_LOG_DIR')
self.store_voice_command = bool(int(get_config_value('STORE_VOICE_COMMANDS')))
# Initialize class methods
self.stt_model = STTModel(self.stt_model_path, self.sample_rate)
self.snips_interface = SnipsInterface(self.snips_model_path)
self.rasa_interface = RASAInterface(self.rasa_server_port, self.rasa_model_path, self.base_log_dir)
self.rasa_interface.start_server()
self.rvc_stream_uuids = {}
def CheckServiceStatus(self, request, context):
response = voice_agent_pb2.ServiceStatus(
version=self.service_version,
status=True
)
return response
def DetectWakeWord(self, request, context):
wake_word_detector = WakeWordDetector(self.wake_word, self.stt_model, self.channels, self.sample_rate, self.bits_per_sample)
wake_word_detector.create_pipeline()
detection_thread = threading.Thread(target=wake_word_detector.start_listening)
detection_thread.start()
while True:
status = wake_word_detector.get_wake_word_status()
time.sleep(1)
if not context.is_active():
wake_word_detector.send_eos()
break
yield voice_agent_pb2.WakeWordStatus(status=status)
if status:
break
detection_thread.join()
def RecognizeVoiceCommand(self, requests, context):
stt = ""
intent = ""
intent_slots = []
for request in requests:
if request.record_mode == voice_agent_pb2.MANUAL:
if request.action == voice_agent_pb2.START:
status = voice_agent_pb2.REC_PROCESSING
stream_uuid = generate_unique_uuid(8)
recorder = AudioRecorder(self.stt_model, self.base_audio_dir, self.channels, self.sample_rate, self.bits_per_sample)
recorder.set_pipeline_mode("manual")
audio_file = recorder.create_pipeline()
self.rvc_stream_uuids[stream_uuid] = {
"recorder": recorder,
"audio_file": audio_file
}
recorder.start_recording()
elif request.action == voice_agent_pb2.STOP:
stream_uuid = request.stream_id
status = voice_agent_pb2.REC_SUCCESS
recorder = self.rvc_stream_uuids[stream_uuid]["recorder"]
audio_file = self.rvc_stream_uuids[stream_uuid]["audio_file"]
del self.rvc_stream_uuids[stream_uuid]
recorder.stop_recording()
recognizer_uuid = self.stt_model.setup_recognizer()
stt = self.stt_model.recognize_from_file(recognizer_uuid, audio_file)
if stt not in ["FILE_NOT_FOUND", "FILE_FORMAT_INVALID", "VOICE_NOT_RECOGNIZED", ""]:
if request.nlu_model == voice_agent_pb2.SNIPS:
extracted_intent = self.snips_interface.extract_intent(stt)
intent, intent_actions = self.snips_interface.process_intent(extracted_intent)
if not intent or intent == "":
status = voice_agent_pb2.INTENT_NOT_RECOGNIZED
for action, value in intent_actions.items():
intent_slots.append(voice_agent_pb2.IntentSlot(name=action, value=value))
elif request.nlu_model == voice_agent_pb2.RASA:
extracted_intent = self.rasa_interface.extract_intent(stt)
intent, intent_actions = self.rasa_interface.process_intent(extracted_intent)
if not intent or intent == "":
status = voice_agent_pb2.INTENT_NOT_RECOGNIZED
for action, value in intent_actions.items():
intent_slots.append(voice_agent_pb2.IntentSlot(name=action, value=value))
else:
stt = ""
status = voice_agent_pb2.VOICE_NOT_RECOGNIZED
# cleanup the kaldi recognizer
self.stt_model.cleanup_recognizer(recognizer_uuid)
# delete the audio file
if not self.store_voice_command:
delete_file(audio_file)
# Process the request and generate a RecognizeResult
response = voice_agent_pb2.RecognizeResult(
command=stt,
intent=intent,
intent_slots=intent_slots,
stream_id=stream_uuid,
status=status
)
return response
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