Add sample 04-

src/samples/theoretical-to-real/04-utterance-and-speech.py

@@ -1,33 +1,52 @@
 import argparse
 import asyncio
+import re
 
-from dailyai.output_queue import OutputQueueFrame, FrameType
 from dailyai.services.daily_transport_service import DailyTransportService
-from dailyai.services.open_ai_services import OpenAIImageGenService
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
+from dailyai.output_queue import OutputQueueFrame, FrameType
 
-local_joined = False
+async def main(room_url:str):
-participant_joined = False
+    global transport
+    global llm
+    global tts
 
-async def main(room_url):
-    meeting_duration_minutes = 1
     transport = DailyTransportService(
         room_url,
         None,
-        "Show a still frame image",
+        "Say Two Things Bot",
-        meeting_duration_minutes,
+        1,
     )
-    transport.mic_enabled = False
+    transport.mic_enabled = True
-    transport.camera_enabled = True
+    transport.mic_sample_rate = 16000
-    transport.camera_width = 1024
+    transport.camera_enabled = False
-    transport.camera_height = 1024
 
-    imagegen = OpenAIImageGenService()
+    llm = AzureLLMService()
-    image_task = asyncio.create_task(imagegen.run_image_gen("a cat in the style of picasso", "1024x1024"))
+    tts = AzureTTSService()
 
     @transport.event_handler("on_participant_joined")
-    async def on_participant_joined(transport, participant):
+    async def on_joined(transport, participant):
-        (_, image_bytes) = await image_task
+        if participant["id"] == transport.my_participant_id:
-        transport.output_queue.put(OutputQueueFrame(FrameType.IMAGE_FRAME, image_bytes))
+            return
+
+        # queue two pieces of speech: one specified as a text literal,
+        # and one generated by an llm. We'll kick off the llm first, and let
+        # it generate a response while we're speaking the literal string.
+        llm_response_task = asyncio.create_task(llm.run_llm(
+            [{"role": "system", "content": "tell the user a joke about llamas"}]
+        ))
+
+        async for audio_chunk in tts.run_tts("My friend the LLM is now going to tell a joke about llamas."):
+            transport.output_queue.put(OutputQueueFrame(FrameType.AUDIO_FRAME, audio_chunk))
+
+        llm_response = await llm_response_task
+        async for audio_chunk in tts.run_tts(llm_response):
+            transport.output_queue.put(OutputQueueFrame(FrameType.AUDIO_FRAME, audio_chunk))
+
+
+        # wait for the output queue to be empty, then leave the meeting
+        transport.output_queue.join()
+        transport.stop()
 
     await transport.run()
 

src/samples/theoretical-to-real/05-queued.py

@@ -0,0 +1,119 @@
+import argparse
+import asyncio
+
+from asyncio.queues import Queue
+import re
+
+from dailyai.output_queue import OutputQueueFrame, FrameType
+from dailyai.services.azure_ai_services import AzureLLMService, AzureTTSService
+from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from dailyai.services.open_ai_services import OpenAILLMService, OpenAIImageGenService
+from dailyai.services.elevenlabs_ai_service import ElevenLabsTTSService
+from dailyai.services.daily_transport_service import DailyTransportService
+
+async def main(room_url):
+    meeting_duration_minutes = 5
+    transport = DailyTransportService(
+        room_url,
+        None,
+        "Month Narration Bot",
+        meeting_duration_minutes,
+    )
+    transport.mic_enabled = True
+    transport.camera_enabled = True
+    transport.mic_sample_rate = 16000
+    transport.camera_width = 1024
+    transport.camera_height = 1024
+
+    llm = AzureLLMService()
+    tts = ElevenLabsTTSService()
+    dalle = OpenAIImageGenService()
+
+    # Get a complete audio chunk from the given text. Splitting this into its own
+    # coroutine lets us ensure proper ordering of the audio chunks on the output queue.
+    async def get_all_audio(text):
+        all_audio = bytearray()
+        async for audio in tts.run_tts(text):
+            all_audio.extend(audio)
+
+        return all_audio
+
+    async def get_month_data(month):
+        image_text = ""
+        current_clause = ""
+        tts_tasks = []
+        async for text in llm.run_llm_async(
+            [
+                {
+                    "role": "system",
+                    "content": f"Describe a nature photograph suitable for use in a calendar, for the month of {month}. Include only the image description with no preamble. Limit the description to one sentence, please."
+                }
+            ]
+        ):
+            image_text += text
+            current_clause += text
+            if re.match(r"^.*[.!?]$", text):
+                tts_tasks.append(get_all_audio(current_clause))
+                current_clause = ""
+
+        tts_tasks.insert(0, dalle.run_image_gen(image_text, "1024x1024"))
+
+        data = await asyncio.gather(
+            *tts_tasks
+        )
+
+        return {
+            "month": month,
+            "text": image_text,
+            "image": data[0][1],
+            "audio": data[1:],
+        }
+
+    months: list[str] = [
+        "January",
+        "February",
+        "March",
+        "April",
+        "May",
+        "June",
+        "July",
+        "August",
+        "September",
+        "October",
+        "November",
+        "December",
+    ]
+
+    @transport.event_handler("on_participant_joined")
+    async def on_participant_joined(transport, participant):
+        if participant["id"] == transport.my_participant_id:
+            return
+
+        for month_data_task in asyncio.as_completed(month_tasks):
+            data = await month_data_task
+            transport.output_queue.put(
+                [
+                    OutputQueueFrame(FrameType.IMAGE_FRAME, data["image"]),
+                    OutputQueueFrame(FrameType.AUDIO_FRAME, data["audio"][0]),
+                ]
+            )
+            for audio in data["audio"][1:]:
+                transport.output_queue.put(OutputQueueFrame(FrameType.AUDIO_FRAME, audio))
+
+        # wait for the output queue to be empty, then leave the meeting
+        transport.output_queue.join()
+        transport.stop()
+
+    month_tasks = [asyncio.create_task(get_month_data(month)) for month in months]
+
+    await transport.run()
+
+if __name__=="__main__":
+    parser = argparse.ArgumentParser(description="Simple Daily Bot Sample")
+    parser.add_argument(
+        "-u", "--url", type=str, required=True, help="URL of the Daily room to join"
+    )
+
+    args: argparse.Namespace = parser.parse_args()
+
+    asyncio.run(main(args.url))

src/samples/theoretical-to-real/05-sync-speech-and-text.py

@@ -1,4 +1,5 @@
 import argparse
+
 import asyncio
 
 from asyncio.queues import Queue