theoretical sample: basic voice chat

src/khk-working/theoretical/06-llm-voice-chat.py

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+from dailyai.services.transport.DailyTransport import DailyTransportService
+from dailyai.services.llm.AzureLLMService import AzureLLMService
+from dailyai.services.tts.AzureTTSService import AzureTTSService
+from dailyai.services.utils import Tee
+from dailyai.services.utils import ReadySoundWav
+
+initial_prompt = "You are a helpful assistant. Introduce yourself and ask how you can be helpful."
+
+llm_messages = [{
+    "role": "system",
+    "content": initial_prompt
+}]
+
+
+transport = None
+llm = None
+tts = None
+mic = None
+transcription = None
+
+
+def main():
+    global transport
+    global llm
+    global tts
+    global mic
+    global transcription
+
+    transport = DailyTransportService()
+    llm = AzureLLMService()
+    tts = AzureTTSService()
+
+    # using Moishe's combined output queue rather than an audio-only queue
+    mic = transport.create_output_queue(audio=True, video=False)
+
+    llm.set_output(Tee(tts, accumulate_assistant_messages))
+    tts.set_output(mic)
+
+    # DailyTransport implements transcription internally. we'll grab a handle to this
+    # Transcription service, configure it to use silence-based endpointing, and
+    # set the silence interval to 1.5 seconds
+    transcription = transport.transcription_service()
+    transcription.configure(endpointing_pause=1.5)
+
+    transport.on("error", lambda e: print(e))
+    transport.on("joined-meeting", llm_prompt)
+    transport.start()
+
+
+def llm_prompt():
+    llm.run_llm(
+        """You are a friendly assistant. Introduce yourself and ask how you can be helpful""")
+    mic.once("audio-queue-empty", listen)
+
+
+def listen():
+    mic.queue(ReadySoundWav)
+    # ignore any transcription results that come in before we're ready
+    _ = transcription.read()
+    user_text_input = transcription.read_until_silence()
+    llm_messages.push({
+        "role": "user",
+        "content": user_text_input
+    })
+    llm_prompt()
+
+
+def accumulate_assistant_messages(completed_inference_text):
+    llm_messages.push({
+        "role": "assistant",
+        "content": completed_inference_text
+    })