renamed samples to examples

src/examples/foundational/11-sound-effects.py

@@ -0,0 +1,127 @@
+import aiohttp
+import asyncio
+import logging
+import os
+import wave
+
+from dailyai.services.daily_transport_service import DailyTransportService
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
+from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from dailyai.queue_aggregators import LLMContextAggregator, LLMUserContextAggregator, LLMAssistantContextAggregator
+from dailyai.services.ai_services import AIService, FrameLogger
+from dailyai.queue_frame import QueueFrame, AudioQueueFrame, LLMResponseEndQueueFrame, LLMMessagesQueueFrame
+from typing import AsyncGenerator
+
+from examples.foundational.support.runner import configure
+
+logging.basicConfig(format=f"%(levelno)s %(asctime)s %(message)s") # or whatever
+logger = logging.getLogger("dailyai")
+logger.setLevel(logging.DEBUG)
+
+sounds = {}
+sound_files = [
+    'ding1.wav',
+    'ding2.wav'
+]
+
+script_dir = os.path.dirname(__file__)
+
+for file in sound_files:
+    # Build the full path to the image file
+    full_path = os.path.join(script_dir, "assets", file)
+    # Get the filename without the extension to use as the dictionary key
+    filename = os.path.splitext(os.path.basename(full_path))[0]
+    # Open the image and convert it to bytes
+    with wave.open(full_path) as audio_file:
+        sounds[file] = audio_file.readframes(-1)
+
+
+
+
+class OutboundSoundEffectWrapper(AIService):
+    def __init__(self):
+        pass
+
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        if isinstance(frame, LLMResponseEndQueueFrame):
+            yield AudioQueueFrame(sounds["ding1.wav"])
+            # In case anything else up the stack needs it
+            yield frame
+        else:
+            yield frame
+
+class InboundSoundEffectWrapper(AIService):
+    def __init__(self):
+        pass
+
+    async def process_frame(self, frame: QueueFrame) -> AsyncGenerator[QueueFrame, None]:
+        if isinstance(frame, LLMMessagesQueueFrame):
+            yield AudioQueueFrame(sounds["ding2.wav"])
+            # In case anything else up the stack needs it
+            yield frame
+        else:
+            yield frame
+
+
+async def main(room_url: str, token):
+    async with aiohttp.ClientSession() as session:
+        transport = DailyTransportService(
+            room_url,
+            token,
+            "Respond bot",
+            duration_minutes=5,
+            mic_enabled=True,
+            mic_sample_rate=16000,
+            camera_enabled=False
+        )
+
+        llm = AzureLLMService(api_key=os.getenv("AZURE_CHATGPT_API_KEY"), endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"), model=os.getenv("AZURE_CHATGPT_MODEL"))
+        tts = ElevenLabsTTSService(aiohttp_session=session, api_key=os.getenv("ELEVENLABS_API_KEY"), voice_id="ErXwobaYiN019PkySvjV")
+
+
+        @transport.event_handler("on_first_other_participant_joined")
+        async def on_first_other_participant_joined(transport):
+            await tts.say("Hi, I'm listening!", transport.send_queue)
+            await transport.send_queue.put(AudioQueueFrame(sounds["ding1.wav"]))
+        async def handle_transcriptions():
+            messages = [
+                {"role": "system", "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio. Respond to what the user said in a creative and helpful way."},
+            ]
+
+            tma_in = LLMUserContextAggregator(
+                messages, transport._my_participant_id
+            )
+            tma_out = LLMAssistantContextAggregator(
+                messages, transport._my_participant_id
+            )
+            out_sound = OutboundSoundEffectWrapper()
+            in_sound = InboundSoundEffectWrapper()
+            fl = FrameLogger("LLM Out")
+            fl2 = FrameLogger("Transcription In")
+            await out_sound.run_to_queue(
+                transport.send_queue,
+                tts.run(
+                    fl.run(
+                        tma_out.run(
+                            llm.run(
+                                fl2.run(
+                                    in_sound.run(
+                                        tma_in.run(
+                                            transport.get_receive_frames()
+                                        )
+                                    )
+                                )
+                            )
+                        )
+                    )
+                )
+            )
+
+
+        transport.transcription_settings["extra"]["punctuate"] = True
+        await asyncio.gather(transport.run(), handle_transcriptions())
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))