processors: split processors into their own files

server/reflector/tools/process.py

@@ -0,0 +1,61 @@
+from pathlib import Path
+import av
+from reflector.logger import logger
+from reflector.processors import (
+    Pipeline,
+    AudioChunkerProcessor,
+    AudioMergeProcessor,
+    AudioTranscriptAutoProcessor,
+    TranscriptLinerProcessor,
+    TranscriptTopicDetectorProcessor,
+    TranscriptSummarizerProcessor,
+)
+import asyncio
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("source", help="Source file (mp3, wav, mp4...)")
+    args = parser.parse_args()
+
+    async def main():
+        async def on_transcript(transcript):
+            print(f"Transcript: [{transcript.human_timestamp}]: {transcript.text}")
+
+        async def on_summary(summary):
+            print(f"Summary: {summary.title} - {summary.summary}")
+
+        async def on_final_summary(path):
+            print(f"Final Summary: {path}")
+
+        # transcription output
+        result_fn = Path(args.source).with_suffix(".jsonl")
+
+        pipeline = Pipeline(
+            AudioChunkerProcessor(),
+            AudioMergeProcessor(),
+            AudioTranscriptAutoProcessor.as_threaded(),
+            TranscriptLinerProcessor(callback=on_transcript),
+            TranscriptTopicDetectorProcessor.as_threaded(callback=on_summary),
+            TranscriptSummarizerProcessor.as_threaded(
+                filename=result_fn, callback=on_final_summary
+            ),
+        )
+        pipeline.describe()
+
+        # start processing audio
+        logger.info(f"Opening {args.source}")
+        container = av.open(args.source)
+        try:
+            logger.info("Start pushing audio into the pipeline")
+            for frame in container.decode(audio=0):
+                await pipeline.push(frame)
+        finally:
+            logger.info("Flushing the pipeline")
+            await pipeline.flush()
+
+        logger.info("All done !")
+
+    asyncio.run(main())