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Build voice-enabled AI applications with speech recognition, text-to-speech, and voice-based interactions. Supports multiple voice providers and real-time processing. Use when creating voice assistants, voice-controlled applications, audio interfaces, or hands-free AI systems.
npx skill4agent add qodex-ai/ai-agent-skills voice-ai-integrationVoiceAssistantRealTimeVoiceProcessorclass SmartHomeVoiceAgent:
def __init__(self):
self.voice_assistant = VoiceAssistant()
self.devices = {
"lights": SmartLights(),
"temperature": SmartThermostat(),
"security": SecuritySystem()
}
async def handle_voice_command(self, audio_input):
# Get text from voice
command_text = await self.voice_assistant.process_voice_input(audio_input)
# Parse intent
intent = parse_smart_home_intent(command_text)
# Execute command
if intent.action == "turn_on_lights":
self.devices["lights"].turn_on(intent.room)
elif intent.action == "set_temperature":
self.devices["temperature"].set(intent.value)
# Confirm with voice
response = f"I've {intent.action_description}"
audio_output = await self.voice_assistant.synthesize_response(response)
return audio_outputclass VoiceMeetingRecorder:
def __init__(self):
self.processor = RealTimeVoiceProcessor()
self.transcripts = []
async def record_and_transcribe_meeting(self, duration_seconds=3600):
audio_stream = self.processor.stream_audio_input()
buffer = []
chunk_duration = 30 # Transcribe every 30 seconds
for audio_chunk in audio_stream:
buffer.append(audio_chunk)
if sum(len(chunk) for chunk in buffer) >= chunk_duration * 16000:
# Transcribe chunk
transcript = transcribe_audio_whisper(buffer)
self.transcripts.append({
"timestamp": datetime.now(),
"text": transcript
})
buffer = []
return self.transcripts