examples: use new AzureSTTService in 07f-interruptible-azure

examples/foundational/07f-interruptible-azure-tts.py

@@ -1,95 +0,0 @@
-#
-# Copyright (c) 2024, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import asyncio
-import aiohttp
-import os
-import sys
-
-from pipecat.frames.frames import LLMMessagesFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_response import (
-    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
-from pipecat.services.azure import AzureTTSService
-from pipecat.services.openai import OpenAILLMService
-from pipecat.transports.services.daily import DailyParams, DailyTransport
-from pipecat.vad.silero import SileroVADAnalyzer
-
-
-from runner import configure
-
-from loguru import logger
-
-from dotenv import load_dotenv
-load_dotenv(override=True)
-
-logger.remove(0)
-logger.add(sys.stderr, level="DEBUG")
-
-
-async def main(room_url: str, token):
-    async with aiohttp.ClientSession() as session:
-        transport = DailyTransport(
-            room_url,
-            token,
-            "Respond bot",
-            DailyParams(
-                audio_out_enabled=True,
-                audio_out_sample_rate=16000,
-                transcription_enabled=True,
-                vad_enabled=True,
-                vad_analyzer=SileroVADAnalyzer()
-            )
-        )
-
-        tts = AzureTTSService(
-            api_key=os.getenv("AZURE_SPEECH_API_KEY"),
-            region=os.getenv("AZURE_SPEECH_REGION"),
-        )
-
-        llm = OpenAILLMService(
-            api_key=os.getenv("OPENAI_API_KEY"),
-            model="gpt-4o")
-
-        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 so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
-            },
-        ]
-
-        tma_in = LLMUserResponseAggregator(messages)
-        tma_out = LLMAssistantResponseAggregator(messages)
-
-        pipeline = Pipeline([
-            transport.input(),   # Transport user input
-            tma_in,              # User responses
-            llm,                 # LLM
-            tts,                 # TTS
-            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": "Please introduce yourself to the user."})
-            await task.queue_frames([LLMMessagesFrame(messages)])
-
-        runner = PipelineRunner()
-
-        await runner.run(task)
-
-
-if __name__ == "__main__":
-    (url, token) = configure()
-    asyncio.run(main(url, token))

examples/foundational/07f-interruptible-azure.py

@@ -0,0 +1,100 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import os
+import sys
+
+from pipecat.frames.frames import LLMMessagesFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
+from pipecat.services.azure import AzureLLMService, AzureSTTService, AzureTTSService
+from pipecat.transports.services.daily import DailyParams, DailyTransport
+from pipecat.vad.silero import SileroVADAnalyzer
+
+
+from runner import configure
+
+from loguru import logger
+
+from dotenv import load_dotenv
+load_dotenv(override=True)
+
+logger.remove(0)
+logger.add(sys.stderr, level="DEBUG")
+
+
+async def main(room_url: str, token):
+    transport = DailyTransport(
+        room_url,
+        token,
+        "Respond bot",
+        DailyParams(
+            audio_out_enabled=True,
+            audio_out_sample_rate=16000,
+            vad_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+            vad_audio_passthrough=True,
+        )
+    )
+
+    stt = AzureSTTService(
+        api_key=os.getenv("AZURE_SPEECH_API_KEY"),
+        region=os.getenv("AZURE_SPEECH_REGION"),
+    )
+
+    tts = AzureTTSService(
+        api_key=os.getenv("AZURE_SPEECH_API_KEY"),
+        region=os.getenv("AZURE_SPEECH_REGION"),
+    )
+
+    llm = AzureLLMService(
+        api_key=os.getenv("AZURE_CHATGPT_API_KEY"),
+        endpoint=os.getenv("AZURE_CHATGPT_ENDPOINT"),
+        model=os.getenv("AZURE_CHATGPT_MODEL"),
+    )
+
+    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 so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+        },
+    ]
+
+    tma_in = LLMUserResponseAggregator(messages)
+    tma_out = LLMAssistantResponseAggregator(messages)
+
+    pipeline = Pipeline([
+        transport.input(),   # Transport user input
+        stt,                 # STT
+        tma_in,              # User responses
+        llm,                 # LLM
+        tts,                 # TTS
+        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": "Please introduce yourself to the user."})
+        await task.queue_frames([LLMMessagesFrame(messages)])
+
+    runner = PipelineRunner()
+
+    await runner.run(task)
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))