mirror of
https://github.com/Monadical-SAS/reflector.git
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125 lines
4.5 KiB
Python
125 lines
4.5 KiB
Python
#!/usr/bin/env python
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"""
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Test script to trigger multitrack recording processing with ffmpeg padding fix
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"""
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import asyncio
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from reflector.pipelines.main_multitrack_pipeline import PipelineMainMultitrack
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async def test_processing():
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"""Manually trigger multitrack processing for the test recording"""
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# Initialize database connection
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from reflector.db import get_database
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db = get_database()
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await db.connect()
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try:
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# The test recording with known speaker timeline
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bucket_name = "monadical"
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track_keys = [
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"daily-20251020193458/1760988935484-52f7f48b-fbab-431f-9a50-87b9abfc8255-cam-audio-1760988935922.webm",
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"daily-20251020193458/1760988935484-a37c35e3-6f8e-4274-a482-e9d0f102a732-cam-audio-1760988943823.webm",
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]
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# Create a new transcript ID
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import uuid
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transcript_id = str(uuid.uuid4())
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# Create transcript directly with SQL
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from reflector.db.transcripts import (
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Transcript,
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transcripts,
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transcripts_controller,
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)
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pipeline = PipelineMainMultitrack(transcript_id=transcript_id)
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# Create transcript model
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transcript = Transcript(
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id=transcript_id,
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name="FFMPEG Test - Daily Multitrack Recording",
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source_kind="file",
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source_language="en",
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target_language="en",
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status="idle",
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events=[],
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title="",
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)
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# Insert into database
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query = transcripts.insert().values(**transcript.model_dump())
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await db.execute(query)
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print(f"Created transcript: {transcript_id}")
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# Process the tracks using the pipeline
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print(f"Processing multitrack recording with ffmpeg padding...")
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print(f"Track 0: ...935922.webm (expected to start at ~2s)")
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print(f"Track 1: ...943823.webm (expected to start at ~51s)")
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try:
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await pipeline.set_status(transcript_id, "processing")
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await pipeline.process(bucket_name, track_keys)
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print(f"Processing complete!")
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except Exception as e:
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await pipeline.set_status(transcript_id, "error")
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print(f"Error during processing: {e}")
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import traceback
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traceback.print_exc()
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raise
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# Check the results
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final_transcript = await transcripts_controller.get(transcript_id)
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print(f"\nTranscript status: {final_transcript.status}")
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print(f"Transcript title: {final_transcript.title}")
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# Extract timeline from events
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if final_transcript.events:
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for event in final_transcript.events:
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if event.get("event") == "TRANSCRIPT":
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text = event.get("data", {}).get("text", "")
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# Show first 500 chars to check if speakers are properly separated
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print(f"\nTranscript text (first 500 chars):")
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print(text[:500])
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# Show last 500 chars too to see if second speaker is at the end
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print(f"\nTranscript text (last 500 chars):")
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print(text[-500:])
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# Count words per speaker
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words = text.split()
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print(f"\nTotal words in transcript: {len(words)}")
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# Check if text has proper speaker separation
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# Expected: First ~45% from speaker 0, then ~35% from speaker 1, then ~20% from speaker 0
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first_third = " ".join(words[: len(words) // 3])
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middle_third = " ".join(
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words[len(words) // 3 : 2 * len(words) // 3]
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)
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last_third = " ".join(words[2 * len(words) // 3 :])
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print(f"\nFirst third preview: {first_third[:100]}...")
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print(f"Middle third preview: {middle_third[:100]}...")
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print(f"Last third preview: {last_third[:100]}...")
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break
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return transcript_id
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finally:
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await db.disconnect()
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if __name__ == "__main__":
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transcript_id = asyncio.run(test_processing())
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print(f"\n✅ Test complete! Transcript ID: {transcript_id}")
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print(f"\nExpected timeline:")
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print(f" Speaker 0: ~2s to ~49s (first participant speaks)")
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print(f" Speaker 1: ~51s to ~70s (second participant speaks)")
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print(f" Speaker 0: ~73s to end (first participant speaks again)")
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print(
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f"\nIf the text shows proper chronological order (not interleaved), the fix worked!"
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)
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