Update release evals for OpenAI Realtime, Gemini Live Vertex; shorten 26 foundational names

examples/foundational/26a-gemini-live-transcription.py

@@ -0,0 +1,152 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.audio.vad.vad_analyzer import VADParams
+from pipecat.frames.frames import LLMRunFrame, TranscriptionMessage
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.processors.transcript_processor import TranscriptProcessor
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import create_transport
+from pipecat.services.google.gemini_live.llm import GeminiLiveLLMService
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.daily.transport import DailyParams
+from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
+
+load_dotenv(override=True)
+
+
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        # set stop_secs to something roughly similar to the internal setting
+        # of the Multimodal Live api, just to align events. This doesn't really
+        # matter because we can only use the Multimodal Live API's phrase
+        # endpointing, for now.
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
+    ),
+    "twilio": lambda: FastAPIWebsocketParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        # set stop_secs to something roughly similar to the internal setting
+        # of the Multimodal Live api, just to align events. This doesn't really
+        # matter because we can only use the Multimodal Live API's phrase
+        # endpointing, for now.
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        # set stop_secs to something roughly similar to the internal setting
+        # of the Multimodal Live api, just to align events. This doesn't really
+        # matter because we can only use the Multimodal Live API's phrase
+        # endpointing, for now.
+        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.5)),
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    llm = GeminiLiveLLMService(
+        api_key=os.getenv("GOOGLE_API_KEY"),
+        voice_id="Aoede",  # Puck, Charon, Kore, Fenrir, Aoede
+        # system_instruction="Talk like a pirate."
+        # inference_on_context_initialization=False,
+    )
+
+    context = OpenAILLMContext(
+        [
+            {
+                "role": "user",
+                "content": "Say hello. Then ask if I want to hear a joke.",
+            },
+            #     {"role": "assistant", "content": "Hello! Why don't scientists trust atoms?"},
+            #     {
+            #         "role": "user",
+            #         "content": [
+            #             {
+            #                 "type": "text",
+            #                 "text": "Oh, I know this one: because they make up everything.",
+            #             }
+            #         ],
+            #     },
+        ],
+    )
+    context_aggregator = llm.create_context_aggregator(context)
+
+    transcript = TranscriptProcessor()
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            context_aggregator.user(),
+            transcript.user(),
+            llm,
+            transport.output(),
+            transcript.assistant(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        await task.queue_frames([LLMRunFrame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    # Register event handler for transcript updates
+    @transcript.event_handler("on_transcript_update")
+    async def on_transcript_update(processor, frame):
+        for msg in frame.messages:
+            if isinstance(msg, TranscriptionMessage):
+                timestamp = f"[{msg.timestamp}] " if msg.timestamp else ""
+                line = f"{timestamp}{msg.role}: {msg.content}"
+                logger.info(f"Transcript: {line}")
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point compatible with Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()