initial commit for new pipecat architecture

examples/foundational/09-mirror.py

@@ -0,0 +1,62 @@
+#
+# Copyright (c) 2024, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import asyncio
+import sys
+
+from pipecat.frames.frames import AudioRawFrame, ImageRawFrame
+from pipecat.processors.filter import Filter
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.pipeline.parallel_pipeline import ParallelPipeline
+from pipecat.transports.services.daily import DailyTransport, DailyParams
+
+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, token):
+    transport = DailyTransport(
+        room_url, token, "Test",
+        DailyParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            camera_out_enabled=True,
+            camera_out_width=1280,
+            camera_out_height=720
+        )
+    )
+
+    @transport.event_handler("on_first_participant_joined")
+    async def on_first_participant_joined(transport, participant):
+        transport.capture_participant_video(participant["id"])
+
+    # The ParallelPipeline is not really necessary here but it shows how you
+    # would process audio and video concurrently in parallel pipelines.
+    pipeline = Pipeline([transport.input(),
+                         ParallelPipeline(
+                             [Filter([AudioRawFrame])],
+                             [Filter([ImageRawFrame])]),
+                         transport.output()])
+
+    runner = PipelineRunner()
+
+    task = PipelineTask(pipeline)
+
+    await runner.run(task)
+
+
+if __name__ == "__main__":
+    (url, token) = configure()
+    asyncio.run(main(url, token))