DailyRESTHelper now receives an aiohttp client session

examples/foundational/15-switch-voices.py

@@ -4,6 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
+import aiohttp
 import asyncio
 import os
 import sys
@@ -58,96 +59,97 @@ async def barbershop_man_filter(frame) -> bool:
 
 
 async def main():
-    (room_url, token) = await configure()
+    async with aiohttp.ClientSession() as session:
+        (room_url, token) = await configure(session)
 
-    transport = DailyTransport(
-        room_url,
-        token,
-        "Pipecat",
-        DailyParams(
-            audio_out_enabled=True,
-            transcription_enabled=True,
-            vad_enabled=True,
-            vad_analyzer=SileroVADAnalyzer()
+        transport = DailyTransport(
+            room_url,
+            token,
+            "Pipecat",
+            DailyParams(
+                audio_out_enabled=True,
+                transcription_enabled=True,
+                vad_enabled=True,
+                vad_analyzer=SileroVADAnalyzer()
+            )
         )
-    )
 
-    news_lady = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="bf991597-6c13-47e4-8411-91ec2de5c466",  # Newslady
-    )
+        news_lady = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="bf991597-6c13-47e4-8411-91ec2de5c466",  # Newslady
+        )
 
-    british_lady = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
-    )
+        british_lady = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="79a125e8-cd45-4c13-8a67-188112f4dd22",  # British Lady
+        )
 
-    barbershop_man = CartesiaTTSService(
-        api_key=os.getenv("CARTESIA_API_KEY"),
-        voice_id="a0e99841-438c-4a64-b679-ae501e7d6091",  # Barbershop Man
-    )
+        barbershop_man = CartesiaTTSService(
+            api_key=os.getenv("CARTESIA_API_KEY"),
+            voice_id="a0e99841-438c-4a64-b679-ae501e7d6091",  # Barbershop Man
+        )
 
-    llm = OpenAILLMService(
-        api_key=os.getenv("OPENAI_API_KEY"),
-        model="gpt-4o")
-    llm.register_function("switch_voice", switch_voice)
+        llm = OpenAILLMService(
+            api_key=os.getenv("OPENAI_API_KEY"),
+            model="gpt-4o")
+        llm.register_function("switch_voice", switch_voice)
 
-    tools = [
-        ChatCompletionToolParam(
-            type="function",
-            function={
-                "name": "switch_voice",
-                "description": "Switch your voice only when the user asks you to",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "voice": {
-                            "type": "string",
-                            "description": "The voice the user wants you to use",
+        tools = [
+            ChatCompletionToolParam(
+                type="function",
+                function={
+                    "name": "switch_voice",
+                    "description": "Switch your voice only when the user asks you to",
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "voice": {
+                                "type": "string",
+                                "description": "The voice the user wants you to use",
+                            },
                         },
+                        "required": ["voice"],
                     },
-                    "required": ["voice"],
-                },
-            })]
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities. Respond to what the user said in a creative and helpful way. Your output should not include non-alphanumeric characters. You can do the following voices: 'News Lady', 'British Lady' and 'Barbershop Man'.",
-        },
-    ]
-
-    context = OpenAILLMContext(messages, tools)
-    tma_in = LLMUserContextAggregator(context)
-    tma_out = LLMAssistantContextAggregator(context)
-
-    pipeline = Pipeline([
-        transport.input(),   # Transport user input
-        tma_in,              # User responses
-        llm,                 # LLM
-        ParallelPipeline(    # TTS (one of the following vocies)
-            [FunctionFilter(news_lady_filter), news_lady],            # News Lady voice
-            [FunctionFilter(british_lady_filter), british_lady],      # British Lady voice
-            [FunctionFilter(barbershop_man_filter), barbershop_man],  # Barbershop Man voice
-        ),
-        transport.output(),  # Transport bot output
-        tma_out              # Assistant spoken responses
-    ])
-
-    task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
-
-    @transport.event_handler("on_first_participant_joined")
-    async def on_first_participant_joined(transport, participant):
-        transport.capture_participant_transcription(participant["id"])
-        # Kick off the conversation.
-        messages.append(
+                })]
+        messages = [
             {
                 "role": "system",
-                "content": f"Please introduce yourself to the user and let them know the voices you can do. Your initial responses should be as if you were a {current_voice}."})
-        await task.queue_frames([LLMMessagesFrame(messages)])
+                "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities. Respond to what the user said in a creative and helpful way. Your output should not include non-alphanumeric characters. You can do the following voices: 'News Lady', 'British Lady' and 'Barbershop Man'.",
+            },
+        ]
 
-    runner = PipelineRunner()
+        context = OpenAILLMContext(messages, tools)
+        tma_in = LLMUserContextAggregator(context)
+        tma_out = LLMAssistantContextAggregator(context)
 
-    await runner.run(task)
+        pipeline = Pipeline([
+            transport.input(),   # Transport user input
+            tma_in,              # User responses
+            llm,                 # LLM
+            ParallelPipeline(    # TTS (one of the following vocies)
+                [FunctionFilter(news_lady_filter), news_lady],            # News Lady voice
+                [FunctionFilter(british_lady_filter), british_lady],      # British Lady voice
+                [FunctionFilter(barbershop_man_filter), barbershop_man],  # Barbershop Man voice
+            ),
+            transport.output(),  # Transport bot output
+            tma_out              # Assistant spoken responses
+        ])
+
+        task = PipelineTask(pipeline, PipelineParams(allow_interruptions=True))
+
+        @transport.event_handler("on_first_participant_joined")
+        async def on_first_participant_joined(transport, participant):
+            transport.capture_participant_transcription(participant["id"])
+            # Kick off the conversation.
+            messages.append(
+                {
+                    "role": "system",
+                    "content": f"Please introduce yourself to the user and let them know the voices you can do. Your initial responses should be as if you were a {current_voice}."})
+            await task.queue_frames([LLMMessagesFrame(messages)])
+
+        runner = PipelineRunner()
+
+        await runner.run(task)
 
 
 if __name__ == "__main__":