feat: daily.co support as alternative to whereby (#691)

* llm instructions

* vibe dailyco

* vibe dailyco

* doc update (vibe)

* dont show recording ui on call

* stub processor (vibe)

* stub processor (vibe) self-review

* stub processor (vibe) self-review

* chore(main): release 0.14.0 (#670)

* Add multitrack pipeline

* Mixdown audio tracks

* Mixdown with pyav filter graph

* Trigger multitrack processing for daily recordings

* apply platform from envs in priority: non-dry

* Use explicit track keys for processing

* Align tracks of a multitrack recording

* Generate waveforms for the mixed audio

* Emit multriack pipeline events

* Fix multitrack pipeline track alignment

* dailico docs

* Enable multitrack reprocessing

* modal temp files uniform names, cleanup. remove llm temporary docs

* docs cleanup

* dont proceed with raw recordings if any of the downloads fail

* dry transcription pipelines

* remove is_miltitrack

* comments

* explicit dailyco room name

* docs

* remove stub data/method

* frontend daily/whereby code self-review (no-mistake)

* frontend daily/whereby code self-review (no-mistakes)

* frontend daily/whereby code self-review (no-mistakes)

* consent cleanup for multitrack (no-mistakes)

* llm fun

* remove extra comments

* fix tests

* merge migrations

* Store participant names

* Get participants by meeting session id

* pop back main branch migration

* s3 paddington (no-mistakes)

* comment

* pr comments

* pr comments

* pr comments

* platform / meeting cleanup

* Use participant names in summary generation

* platform assignment to meeting at controller level

* pr comment

* room playform properly default none

* room playform properly default none

* restore migration lost

* streaming WIP

* extract storage / use common storage / proper env vars for storage

* fix mocks tests

* remove fall back

* streaming for multifile

* cenrtal storage abstraction (no-mistakes)

* remove dead code / vars

* Set participant user id for authenticated users

* whereby recording name parsing fix

* whereby recording name parsing fix

* more file stream

* storage dry + tests

* remove homemade boto3 streaming and use proper boto

* update migration guide

* webhook creation script - print uuid

---------

Co-authored-by: Igor Loskutov <igor.loskutoff@gmail.com>
Co-authored-by: Mathieu Virbel <mat@meltingrocks.com>
Co-authored-by: Sergey Mankovsky <sergey@monadical.com>

server/reflector/pipelines/__init__.py

@@ -0,0 +1 @@
+"""Pipeline modules for audio processing."""

server/reflector/pipelines/main_file_pipeline.py

@@ -23,23 +23,18 @@ from reflector.db.transcripts import (
     transcripts_controller,
 )
 from reflector.logger import logger
+from reflector.pipelines import topic_processing
 from reflector.pipelines.main_live_pipeline import (
     PipelineMainBase,
     broadcast_to_sockets,
     task_cleanup_consent,
     task_pipeline_post_to_zulip,
 )
-from reflector.processors import (
-    AudioFileWriterProcessor,
-    TranscriptFinalSummaryProcessor,
-    TranscriptFinalTitleProcessor,
-    TranscriptTopicDetectorProcessor,
-)
+from reflector.pipelines.transcription_helpers import transcribe_file_with_processor
+from reflector.processors import AudioFileWriterProcessor
 from reflector.processors.audio_waveform_processor import AudioWaveformProcessor
 from reflector.processors.file_diarization import FileDiarizationInput
 from reflector.processors.file_diarization_auto import FileDiarizationAutoProcessor
-from reflector.processors.file_transcript import FileTranscriptInput
-from reflector.processors.file_transcript_auto import FileTranscriptAutoProcessor
 from reflector.processors.transcript_diarization_assembler import (
     TranscriptDiarizationAssemblerInput,
     TranscriptDiarizationAssemblerProcessor,
@@ -56,19 +51,6 @@ from reflector.storage import get_transcripts_storage
 from reflector.worker.webhook import send_transcript_webhook
 
 
-class EmptyPipeline:
-    """Empty pipeline for processors that need a pipeline reference"""
-
-    def __init__(self, logger: structlog.BoundLogger):
-        self.logger = logger
-
-    def get_pref(self, k, d=None):
-        return d
-
-    async def emit(self, event):
-        pass
-
-
 class PipelineMainFile(PipelineMainBase):
     """
     Optimized file processing pipeline.
@@ -81,7 +63,7 @@ class PipelineMainFile(PipelineMainBase):
     def __init__(self, transcript_id: str):
         super().__init__(transcript_id=transcript_id)
         self.logger = logger.bind(transcript_id=self.transcript_id)
-        self.empty_pipeline = EmptyPipeline(logger=self.logger)
+        self.empty_pipeline = topic_processing.EmptyPipeline(logger=self.logger)
 
     def _handle_gather_exceptions(self, results: list, operation: str) -> None:
         """Handle exceptions from asyncio.gather with return_exceptions=True"""
@@ -262,24 +244,7 @@ class PipelineMainFile(PipelineMainBase):
 
     async def transcribe_file(self, audio_url: str, language: str) -> TranscriptType:
         """Transcribe complete file"""
-        processor = FileTranscriptAutoProcessor()
-        input_data = FileTranscriptInput(audio_url=audio_url, language=language)
-
-        # Store result for retrieval
-        result: TranscriptType | None = None
-
-        async def capture_result(transcript):
-            nonlocal result
-            result = transcript
-
-        processor.on(capture_result)
-        await processor.push(input_data)
-        await processor.flush()
-
-        if not result:
-            raise ValueError("No transcript captured")
-
-        return result
+        return await transcribe_file_with_processor(audio_url, language)
 
     async def diarize_file(self, audio_url: str) -> list[DiarizationSegment] | None:
         """Get diarization for file"""
@@ -322,63 +287,31 @@ class PipelineMainFile(PipelineMainBase):
     async def detect_topics(
         self, transcript: TranscriptType, target_language: str
     ) -> list[TitleSummary]:
-        """Detect topics from complete transcript"""
-        chunk_size = 300
-        topics: list[TitleSummary] = []
-
-        async def on_topic(topic: TitleSummary):
-            topics.append(topic)
-            return await self.on_topic(topic)
-
-        topic_detector = TranscriptTopicDetectorProcessor(callback=on_topic)
-        topic_detector.set_pipeline(self.empty_pipeline)
-
-        for i in range(0, len(transcript.words), chunk_size):
-            chunk_words = transcript.words[i : i + chunk_size]
-            if not chunk_words:
-                continue
-
-            chunk_transcript = TranscriptType(
-                words=chunk_words, translation=transcript.translation
-            )
-
-            await topic_detector.push(chunk_transcript)
-
-        await topic_detector.flush()
-        return topics
+        return await topic_processing.detect_topics(
+            transcript,
+            target_language,
+            on_topic_callback=self.on_topic,
+            empty_pipeline=self.empty_pipeline,
+        )
 
     async def generate_title(self, topics: list[TitleSummary]):
-        """Generate title from topics"""
-        if not topics:
-            self.logger.warning("No topics for title generation")
-            return
-
-        processor = TranscriptFinalTitleProcessor(callback=self.on_title)
-        processor.set_pipeline(self.empty_pipeline)
-
-        for topic in topics:
-            await processor.push(topic)
-
-        await processor.flush()
+        return await topic_processing.generate_title(
+            topics,
+            on_title_callback=self.on_title,
+            empty_pipeline=self.empty_pipeline,
+            logger=self.logger,
+        )
 
     async def generate_summaries(self, topics: list[TitleSummary]):
-        """Generate long and short summaries from topics"""
-        if not topics:
-            self.logger.warning("No topics for summary generation")
-            return
-
         transcript = await self.get_transcript()
-        processor = TranscriptFinalSummaryProcessor(
-            transcript=transcript,
-            callback=self.on_long_summary,
-            on_short_summary=self.on_short_summary,
+        return await topic_processing.generate_summaries(
+            topics,
+            transcript,
+            on_long_summary_callback=self.on_long_summary,
+            on_short_summary_callback=self.on_short_summary,
+            empty_pipeline=self.empty_pipeline,
+            logger=self.logger,
         )
-        processor.set_pipeline(self.empty_pipeline)
-
-        for topic in topics:
-            await processor.push(topic)
-
-        await processor.flush()
 
 
 @shared_task

server/reflector/pipelines/main_live_pipeline.py

@@ -17,7 +17,6 @@ from contextlib import asynccontextmanager
 from typing import Generic
 
 import av
-import boto3
 from celery import chord, current_task, group, shared_task
 from pydantic import BaseModel
 from structlog import BoundLogger as Logger
@@ -584,6 +583,7 @@ async def cleanup_consent(transcript: Transcript, logger: Logger):
 
     consent_denied = False
     recording = None
+    meeting = None
     try:
         if transcript.recording_id:
             recording = await recordings_controller.get_by_id(transcript.recording_id)
@@ -594,8 +594,8 @@ async def cleanup_consent(transcript: Transcript, logger: Logger):
                         meeting.id
                     )
     except Exception as e:
-        logger.error(f"Failed to get fetch consent: {e}", exc_info=e)
-        consent_denied = True
+        logger.error(f"Failed to fetch consent: {e}", exc_info=e)
+        raise
 
     if not consent_denied:
         logger.info("Consent approved, keeping all files")
@@ -603,25 +603,24 @@ async def cleanup_consent(transcript: Transcript, logger: Logger):
 
     logger.info("Consent denied, cleaning up all related audio files")
 
-    if recording and recording.bucket_name and recording.object_key:
-        s3_whereby = boto3.client(
-            "s3",
-            aws_access_key_id=settings.AWS_WHEREBY_ACCESS_KEY_ID,
-            aws_secret_access_key=settings.AWS_WHEREBY_ACCESS_KEY_SECRET,
-        )
-        try:
-            s3_whereby.delete_object(
-                Bucket=recording.bucket_name, Key=recording.object_key
-            )
-            logger.info(
-                f"Deleted original Whereby recording: {recording.bucket_name}/{recording.object_key}"
-            )
-        except Exception as e:
-            logger.error(f"Failed to delete Whereby recording: {e}", exc_info=e)
+    deletion_errors = []
+    if recording and recording.bucket_name:
+        keys_to_delete = []
+        if recording.track_keys:
+            keys_to_delete = recording.track_keys
+        elif recording.object_key:
+            keys_to_delete = [recording.object_key]
+
+        master_storage = get_transcripts_storage()
+        for key in keys_to_delete:
+            try:
+                await master_storage.delete_file(key, bucket=recording.bucket_name)
+                logger.info(f"Deleted recording file: {recording.bucket_name}/{key}")
+            except Exception as e:
+                error_msg = f"Failed to delete {key}: {e}"
+                logger.error(error_msg, exc_info=e)
+                deletion_errors.append(error_msg)
 
-    # non-transactional, files marked for deletion not actually deleted is possible
-    await transcripts_controller.update(transcript, {"audio_deleted": True})
-    # 2. Delete processed audio from transcript storage S3 bucket
     if transcript.audio_location == "storage":
         storage = get_transcripts_storage()
         try:
@@ -630,18 +629,28 @@ async def cleanup_consent(transcript: Transcript, logger: Logger):
                 f"Deleted processed audio from storage: {transcript.storage_audio_path}"
             )
         except Exception as e:
-            logger.error(f"Failed to delete processed audio: {e}", exc_info=e)
+            error_msg = f"Failed to delete processed audio: {e}"
+            logger.error(error_msg, exc_info=e)
+            deletion_errors.append(error_msg)
 
-    # 3. Delete local audio files
     try:
         if hasattr(transcript, "audio_mp3_filename") and transcript.audio_mp3_filename:
             transcript.audio_mp3_filename.unlink(missing_ok=True)
         if hasattr(transcript, "audio_wav_filename") and transcript.audio_wav_filename:
             transcript.audio_wav_filename.unlink(missing_ok=True)
     except Exception as e:
-        logger.error(f"Failed to delete local audio files: {e}", exc_info=e)
+        error_msg = f"Failed to delete local audio files: {e}"
+        logger.error(error_msg, exc_info=e)
+        deletion_errors.append(error_msg)
 
-    logger.info("Consent cleanup done")
+    if deletion_errors:
+        logger.warning(
+            f"Consent cleanup completed with {len(deletion_errors)} errors",
+            errors=deletion_errors,
+        )
+    else:
+        await transcripts_controller.update(transcript, {"audio_deleted": True})
+        logger.info("Consent cleanup done - all audio deleted")
 
 
 @get_transcript

server/reflector/pipelines/main_multitrack_pipeline.py

@@ -0,0 +1,694 @@
+import asyncio
+import math
+import tempfile
+from fractions import Fraction
+from pathlib import Path
+
+import av
+from av.audio.resampler import AudioResampler
+from celery import chain, shared_task
+
+from reflector.asynctask import asynctask
+from reflector.db.transcripts import (
+    TranscriptStatus,
+    TranscriptWaveform,
+    transcripts_controller,
+)
+from reflector.logger import logger
+from reflector.pipelines import topic_processing
+from reflector.pipelines.main_file_pipeline import task_send_webhook_if_needed
+from reflector.pipelines.main_live_pipeline import (
+    PipelineMainBase,
+    broadcast_to_sockets,
+    task_cleanup_consent,
+    task_pipeline_post_to_zulip,
+)
+from reflector.pipelines.transcription_helpers import transcribe_file_with_processor
+from reflector.processors import AudioFileWriterProcessor
+from reflector.processors.audio_waveform_processor import AudioWaveformProcessor
+from reflector.processors.types import TitleSummary
+from reflector.processors.types import Transcript as TranscriptType
+from reflector.storage import Storage, get_transcripts_storage
+from reflector.utils.string import NonEmptyString
+
+# Audio encoding constants
+OPUS_STANDARD_SAMPLE_RATE = 48000
+OPUS_DEFAULT_BIT_RATE = 128000
+
+# Storage operation constants
+PRESIGNED_URL_EXPIRATION_SECONDS = 7200  # 2 hours
+
+
+class PipelineMainMultitrack(PipelineMainBase):
+    def __init__(self, transcript_id: str):
+        super().__init__(transcript_id=transcript_id)
+        self.logger = logger.bind(transcript_id=self.transcript_id)
+        self.empty_pipeline = topic_processing.EmptyPipeline(logger=self.logger)
+
+    async def pad_track_for_transcription(
+        self,
+        track_url: NonEmptyString,
+        track_idx: int,
+        storage: Storage,
+    ) -> NonEmptyString:
+        """
+        Pad a single track with silence based on stream metadata start_time.
+        Downloads from S3 presigned URL, processes via PyAV using tempfile, uploads to S3.
+        Returns presigned URL of padded track (or original URL if no padding needed).
+
+        Memory usage:
+        - Pattern: fixed_overhead(2-5MB) for PyAV codec/filters
+        - PyAV streams input efficiently (no full download, verified)
+        - Output written to tempfile (disk-based, not memory)
+        - Upload streams from file handle (boto3 chunks, typically 5-10MB)
+
+        Daily.co raw-tracks timing - Two approaches:
+
+            CURRENT APPROACH (PyAV metadata):
+            The WebM stream.start_time field encodes MEETING-RELATIVE timing:
+            - t=0: When Daily.co recording started (first participant joined)
+            - start_time=8.13s: This participant's track began 8.13s after recording started
+            - Purpose: Enables track alignment without external manifest files
+
+            This is NOT:
+            - Stream-internal offset (first packet timestamp relative to stream start)
+            - Absolute/wall-clock time
+            - Recording duration
+
+            ALTERNATIVE APPROACH (filename parsing):
+            Daily.co filenames contain Unix timestamps (milliseconds):
+            Format: {recording_start_ts}-{participant_id}-cam-audio-{track_start_ts}.webm
+            Example: 1760988935484-52f7f48b-fbab-431f-9a50-87b9abfc8255-cam-audio-1760988935922.webm
+
+            Can calculate offset: (track_start_ts - recording_start_ts) / 1000
+            - Track 0: (1760988935922 - 1760988935484) / 1000 = 0.438s
+            - Track 1: (1760988943823 - 1760988935484) / 1000 = 8.339s
+
+            TIME DIFFERENCE: PyAV metadata vs filename timestamps differ by ~209ms:
+            - Track 0: filename=438ms, metadata=229ms (diff: 209ms)
+            - Track 1: filename=8339ms, metadata=8130ms (diff: 209ms)
+
+            Consistent delta suggests network/encoding delay. PyAV metadata is ground truth
+            (represents when audio stream actually started vs when file upload initiated).
+
+            Example with 2 participants:
+                Track A: start_time=0.2s → Joined 200ms after recording began
+                Track B: start_time=8.1s → Joined 8.1 seconds later
+
+                After padding:
+                    Track A: [0.2s silence] + [speech...]
+                    Track B: [8.1s silence] + [speech...]
+
+                Whisper transcription timestamps are now synchronized:
+                    Track A word at 5.0s → happened at meeting t=5.0s
+                    Track B word at 10.0s → happened at meeting t=10.0s
+
+                Merging just sorts by timestamp - no offset calculation needed.
+
+        Padding coincidentally involves re-encoding. It's important when we work with Daily.co + Whisper.
+        This is because Daily.co returns recordings with skipped frames e.g. when microphone muted.
+        Daily.co doesn't understand those frames and ignores them, causing timestamp issues in transcription.
+        Re-encoding restores those frames. We do padding and re-encoding together just because it's convenient and more performant:
+        we need padded values for mix mp3 anyways
+        """
+
+        transcript = await self.get_transcript()
+
+        try:
+            # PyAV streams input from S3 URL efficiently (2-5MB fixed overhead for codec/filters)
+            with av.open(track_url) as in_container:
+                start_time_seconds = self._extract_stream_start_time_from_container(
+                    in_container, track_idx
+                )
+
+                if start_time_seconds <= 0:
+                    self.logger.info(
+                        f"Track {track_idx} requires no padding (start_time={start_time_seconds}s)",
+                        track_idx=track_idx,
+                    )
+                    return track_url
+
+                # Use tempfile instead of BytesIO for better memory efficiency
+                # Reduces peak memory usage during encoding/upload
+                with tempfile.NamedTemporaryFile(
+                    suffix=".webm", delete=False
+                ) as temp_file:
+                    temp_path = temp_file.name
+
+                try:
+                    self._apply_audio_padding_to_file(
+                        in_container, temp_path, start_time_seconds, track_idx
+                    )
+
+                    storage_path = (
+                        f"file_pipeline/{transcript.id}/tracks/padded_{track_idx}.webm"
+                    )
+
+                    # Upload using file handle for streaming
+                    with open(temp_path, "rb") as padded_file:
+                        await storage.put_file(storage_path, padded_file)
+                finally:
+                    # Clean up temp file
+                    Path(temp_path).unlink(missing_ok=True)
+
+                padded_url = await storage.get_file_url(
+                    storage_path,
+                    operation="get_object",
+                    expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
+                )
+
+                self.logger.info(
+                    f"Successfully padded track {track_idx}",
+                    track_idx=track_idx,
+                    start_time_seconds=start_time_seconds,
+                    padded_url=padded_url,
+                )
+
+                return padded_url
+
+        except Exception as e:
+            self.logger.error(
+                f"Failed to process track {track_idx}",
+                track_idx=track_idx,
+                url=track_url,
+                error=str(e),
+                exc_info=True,
+            )
+            raise Exception(
+                f"Track {track_idx} padding failed - transcript would have incorrect timestamps"
+            ) from e
+
+    def _extract_stream_start_time_from_container(
+        self, container, track_idx: int
+    ) -> float:
+        """
+        Extract meeting-relative start time from WebM stream metadata.
+        Uses PyAV to read stream.start_time from WebM container.
+        More accurate than filename timestamps by ~209ms due to network/encoding delays.
+        """
+        start_time_seconds = 0.0
+        try:
+            audio_streams = [s for s in container.streams if s.type == "audio"]
+            stream = audio_streams[0] if audio_streams else container.streams[0]
+
+            # 1) Try stream-level start_time (most reliable for Daily.co tracks)
+            if stream.start_time is not None and stream.time_base is not None:
+                start_time_seconds = float(stream.start_time * stream.time_base)
+
+            # 2) Fallback to container-level start_time (in av.time_base units)
+            if (start_time_seconds <= 0) and (container.start_time is not None):
+                start_time_seconds = float(container.start_time * av.time_base)
+
+            # 3) Fallback to first packet DTS in stream.time_base
+            if start_time_seconds <= 0:
+                for packet in container.demux(stream):
+                    if packet.dts is not None:
+                        start_time_seconds = float(packet.dts * stream.time_base)
+                        break
+        except Exception as e:
+            self.logger.warning(
+                "PyAV metadata read failed; assuming 0 start_time",
+                track_idx=track_idx,
+                error=str(e),
+            )
+            start_time_seconds = 0.0
+
+        self.logger.info(
+            f"Track {track_idx} stream metadata: start_time={start_time_seconds:.3f}s",
+            track_idx=track_idx,
+        )
+        return start_time_seconds
+
+    def _apply_audio_padding_to_file(
+        self,
+        in_container,
+        output_path: str,
+        start_time_seconds: float,
+        track_idx: int,
+    ) -> None:
+        """Apply silence padding to audio track using PyAV filter graph, writing to file"""
+        delay_ms = math.floor(start_time_seconds * 1000)
+
+        self.logger.info(
+            f"Padding track {track_idx} with {delay_ms}ms delay using PyAV",
+            track_idx=track_idx,
+            delay_ms=delay_ms,
+        )
+
+        try:
+            with av.open(output_path, "w", format="webm") as out_container:
+                in_stream = next(
+                    (s for s in in_container.streams if s.type == "audio"), None
+                )
+                if in_stream is None:
+                    raise Exception("No audio stream in input")
+
+                out_stream = out_container.add_stream(
+                    "libopus", rate=OPUS_STANDARD_SAMPLE_RATE
+                )
+                out_stream.bit_rate = OPUS_DEFAULT_BIT_RATE
+                graph = av.filter.Graph()
+
+                abuf_args = (
+                    f"time_base=1/{OPUS_STANDARD_SAMPLE_RATE}:"
+                    f"sample_rate={OPUS_STANDARD_SAMPLE_RATE}:"
+                    f"sample_fmt=s16:"
+                    f"channel_layout=stereo"
+                )
+                src = graph.add("abuffer", args=abuf_args, name="src")
+                aresample_f = graph.add("aresample", args="async=1", name="ares")
+                # adelay requires one delay value per channel separated by '|'
+                delays_arg = f"{delay_ms}|{delay_ms}"
+                adelay_f = graph.add(
+                    "adelay", args=f"delays={delays_arg}:all=1", name="delay"
+                )
+                sink = graph.add("abuffersink", name="sink")
+
+                src.link_to(aresample_f)
+                aresample_f.link_to(adelay_f)
+                adelay_f.link_to(sink)
+                graph.configure()
+
+                resampler = AudioResampler(
+                    format="s16", layout="stereo", rate=OPUS_STANDARD_SAMPLE_RATE
+                )
+                # Decode -> resample -> push through graph -> encode Opus
+                for frame in in_container.decode(in_stream):
+                    out_frames = resampler.resample(frame) or []
+                    for rframe in out_frames:
+                        rframe.sample_rate = OPUS_STANDARD_SAMPLE_RATE
+                        rframe.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
+                        src.push(rframe)
+
+                        while True:
+                            try:
+                                f_out = sink.pull()
+                            except Exception:
+                                break
+                            f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
+                            f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
+                            for packet in out_stream.encode(f_out):
+                                out_container.mux(packet)
+
+                src.push(None)
+                while True:
+                    try:
+                        f_out = sink.pull()
+                    except Exception:
+                        break
+                    f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
+                    f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
+                    for packet in out_stream.encode(f_out):
+                        out_container.mux(packet)
+
+                for packet in out_stream.encode(None):
+                    out_container.mux(packet)
+        except Exception as e:
+            self.logger.error(
+                "PyAV padding failed for track",
+                track_idx=track_idx,
+                delay_ms=delay_ms,
+                error=str(e),
+                exc_info=True,
+            )
+            raise
+
+    async def mixdown_tracks(
+        self,
+        track_urls: list[str],
+        writer: AudioFileWriterProcessor,
+        offsets_seconds: list[float] | None = None,
+    ) -> None:
+        """Multi-track mixdown using PyAV filter graph (amix), reading from S3 presigned URLs"""
+
+        target_sample_rate: int | None = None
+        for url in track_urls:
+            if not url:
+                continue
+            container = None
+            try:
+                container = av.open(url)
+                for frame in container.decode(audio=0):
+                    target_sample_rate = frame.sample_rate
+                    break
+            except Exception:
+                continue
+            finally:
+                if container is not None:
+                    container.close()
+            if target_sample_rate:
+                break
+
+        if not target_sample_rate:
+            self.logger.error("Mixdown failed - no decodable audio frames found")
+            raise Exception("Mixdown failed: No decodable audio frames in any track")
+        # Build PyAV filter graph:
+        # N abuffer (s32/stereo)
+        #   -> optional adelay per input (for alignment)
+        #   -> amix (s32)
+        #   -> aformat(s16)
+        #   -> sink
+        graph = av.filter.Graph()
+        inputs = []
+        valid_track_urls = [url for url in track_urls if url]
+        input_offsets_seconds = None
+        if offsets_seconds is not None:
+            input_offsets_seconds = [
+                offsets_seconds[i] for i, url in enumerate(track_urls) if url
+            ]
+        for idx, url in enumerate(valid_track_urls):
+            args = (
+                f"time_base=1/{target_sample_rate}:"
+                f"sample_rate={target_sample_rate}:"
+                f"sample_fmt=s32:"
+                f"channel_layout=stereo"
+            )
+            in_ctx = graph.add("abuffer", args=args, name=f"in{idx}")
+            inputs.append(in_ctx)
+
+        if not inputs:
+            self.logger.error("Mixdown failed - no valid inputs for graph")
+            raise Exception("Mixdown failed: No valid inputs for filter graph")
+
+        mixer = graph.add("amix", args=f"inputs={len(inputs)}:normalize=0", name="mix")
+
+        fmt = graph.add(
+            "aformat",
+            args=(
+                f"sample_fmts=s32:channel_layouts=stereo:sample_rates={target_sample_rate}"
+            ),
+            name="fmt",
+        )
+
+        sink = graph.add("abuffersink", name="out")
+
+        # Optional per-input delay before mixing
+        delays_ms: list[int] = []
+        if input_offsets_seconds is not None:
+            base = min(input_offsets_seconds) if input_offsets_seconds else 0.0
+            delays_ms = [
+                max(0, int(round((o - base) * 1000))) for o in input_offsets_seconds
+            ]
+        else:
+            delays_ms = [0 for _ in inputs]
+
+        for idx, in_ctx in enumerate(inputs):
+            delay_ms = delays_ms[idx] if idx < len(delays_ms) else 0
+            if delay_ms > 0:
+                # adelay requires one value per channel; use same for stereo
+                adelay = graph.add(
+                    "adelay",
+                    args=f"delays={delay_ms}|{delay_ms}:all=1",
+                    name=f"delay{idx}",
+                )
+                in_ctx.link_to(adelay)
+                adelay.link_to(mixer, 0, idx)
+            else:
+                in_ctx.link_to(mixer, 0, idx)
+        mixer.link_to(fmt)
+        fmt.link_to(sink)
+        graph.configure()
+
+        containers = []
+        try:
+            # Open all containers with cleanup guaranteed
+            for i, url in enumerate(valid_track_urls):
+                try:
+                    c = av.open(url)
+                    containers.append(c)
+                except Exception as e:
+                    self.logger.warning(
+                        "Mixdown: failed to open container from URL",
+                        input=i,
+                        url=url,
+                        error=str(e),
+                    )
+
+            if not containers:
+                self.logger.error("Mixdown failed - no valid containers opened")
+                raise Exception("Mixdown failed: Could not open any track containers")
+
+            decoders = [c.decode(audio=0) for c in containers]
+            active = [True] * len(decoders)
+            resamplers = [
+                AudioResampler(format="s32", layout="stereo", rate=target_sample_rate)
+                for _ in decoders
+            ]
+
+            while any(active):
+                for i, (dec, is_active) in enumerate(zip(decoders, active)):
+                    if not is_active:
+                        continue
+                    try:
+                        frame = next(dec)
+                    except StopIteration:
+                        active[i] = False
+                        continue
+
+                    if frame.sample_rate != target_sample_rate:
+                        continue
+                    out_frames = resamplers[i].resample(frame) or []
+                    for rf in out_frames:
+                        rf.sample_rate = target_sample_rate
+                        rf.time_base = Fraction(1, target_sample_rate)
+                        inputs[i].push(rf)
+
+                    while True:
+                        try:
+                            mixed = sink.pull()
+                        except Exception:
+                            break
+                        mixed.sample_rate = target_sample_rate
+                        mixed.time_base = Fraction(1, target_sample_rate)
+                        await writer.push(mixed)
+
+            for in_ctx in inputs:
+                in_ctx.push(None)
+            while True:
+                try:
+                    mixed = sink.pull()
+                except Exception:
+                    break
+                mixed.sample_rate = target_sample_rate
+                mixed.time_base = Fraction(1, target_sample_rate)
+                await writer.push(mixed)
+        finally:
+            # Cleanup all containers, even if processing failed
+            for c in containers:
+                if c is not None:
+                    try:
+                        c.close()
+                    except Exception:
+                        pass  # Best effort cleanup
+
+    @broadcast_to_sockets
+    async def set_status(self, transcript_id: str, status: TranscriptStatus):
+        async with self.lock_transaction():
+            return await transcripts_controller.set_status(transcript_id, status)
+
+    async def on_waveform(self, data):
+        async with self.transaction():
+            waveform = TranscriptWaveform(waveform=data)
+            transcript = await self.get_transcript()
+            return await transcripts_controller.append_event(
+                transcript=transcript, event="WAVEFORM", data=waveform
+            )
+
+    async def process(self, bucket_name: str, track_keys: list[str]):
+        transcript = await self.get_transcript()
+        async with self.transaction():
+            await transcripts_controller.update(
+                transcript,
+                {
+                    "events": [],
+                    "topics": [],
+                },
+            )
+
+        source_storage = get_transcripts_storage()
+        transcript_storage = source_storage
+
+        track_urls: list[str] = []
+        for key in track_keys:
+            url = await source_storage.get_file_url(
+                key,
+                operation="get_object",
+                expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
+                bucket=bucket_name,
+            )
+            track_urls.append(url)
+            self.logger.info(
+                f"Generated presigned URL for track from {bucket_name}",
+                key=key,
+            )
+
+        created_padded_files = set()
+        padded_track_urls: list[str] = []
+        for idx, url in enumerate(track_urls):
+            padded_url = await self.pad_track_for_transcription(
+                url, idx, transcript_storage
+            )
+            padded_track_urls.append(padded_url)
+            if padded_url != url:
+                storage_path = f"file_pipeline/{transcript.id}/tracks/padded_{idx}.webm"
+                created_padded_files.add(storage_path)
+            self.logger.info(f"Track {idx} processed, padded URL: {padded_url}")
+
+        transcript.data_path.mkdir(parents=True, exist_ok=True)
+
+        mp3_writer = AudioFileWriterProcessor(
+            path=str(transcript.audio_mp3_filename),
+            on_duration=self.on_duration,
+        )
+        await self.mixdown_tracks(padded_track_urls, mp3_writer, offsets_seconds=None)
+        await mp3_writer.flush()
+
+        if not transcript.audio_mp3_filename.exists():
+            raise Exception(
+                "Mixdown failed - no MP3 file generated. Cannot proceed without playable audio."
+            )
+
+        storage_path = f"{transcript.id}/audio.mp3"
+        # Use file handle streaming to avoid loading entire MP3 into memory
+        mp3_size = transcript.audio_mp3_filename.stat().st_size
+        with open(transcript.audio_mp3_filename, "rb") as mp3_file:
+            await transcript_storage.put_file(storage_path, mp3_file)
+        mp3_url = await transcript_storage.get_file_url(storage_path)
+
+        await transcripts_controller.update(transcript, {"audio_location": "storage"})
+
+        self.logger.info(
+            f"Uploaded mixed audio to storage",
+            storage_path=storage_path,
+            size=mp3_size,
+            url=mp3_url,
+        )
+
+        self.logger.info("Generating waveform from mixed audio")
+        waveform_processor = AudioWaveformProcessor(
+            audio_path=transcript.audio_mp3_filename,
+            waveform_path=transcript.audio_waveform_filename,
+            on_waveform=self.on_waveform,
+        )
+        waveform_processor.set_pipeline(self.empty_pipeline)
+        await waveform_processor.flush()
+        self.logger.info("Waveform generated successfully")
+
+        speaker_transcripts: list[TranscriptType] = []
+        for idx, padded_url in enumerate(padded_track_urls):
+            if not padded_url:
+                continue
+
+            t = await self.transcribe_file(padded_url, transcript.source_language)
+
+            if not t.words:
+                continue
+
+            for w in t.words:
+                w.speaker = idx
+
+            speaker_transcripts.append(t)
+            self.logger.info(
+                f"Track {idx} transcribed successfully with {len(t.words)} words",
+                track_idx=idx,
+            )
+
+        valid_track_count = len([url for url in padded_track_urls if url])
+        if valid_track_count > 0 and len(speaker_transcripts) != valid_track_count:
+            raise Exception(
+                f"Only {len(speaker_transcripts)}/{valid_track_count} tracks transcribed successfully. "
+                f"All tracks must succeed to avoid incomplete transcripts."
+            )
+
+        if not speaker_transcripts:
+            raise Exception("No valid track transcriptions")
+
+        self.logger.info(f"Cleaning up {len(created_padded_files)} temporary S3 files")
+        cleanup_tasks = []
+        for storage_path in created_padded_files:
+            cleanup_tasks.append(transcript_storage.delete_file(storage_path))
+
+        if cleanup_tasks:
+            cleanup_results = await asyncio.gather(
+                *cleanup_tasks, return_exceptions=True
+            )
+            for storage_path, result in zip(created_padded_files, cleanup_results):
+                if isinstance(result, Exception):
+                    self.logger.warning(
+                        "Failed to cleanup temporary padded track",
+                        storage_path=storage_path,
+                        error=str(result),
+                    )
+
+        merged_words = []
+        for t in speaker_transcripts:
+            merged_words.extend(t.words)
+        merged_words.sort(
+            key=lambda w: w.start if hasattr(w, "start") and w.start is not None else 0
+        )
+
+        merged_transcript = TranscriptType(words=merged_words, translation=None)
+
+        await self.on_transcript(merged_transcript)
+
+        topics = await self.detect_topics(merged_transcript, transcript.target_language)
+        await asyncio.gather(
+            self.generate_title(topics),
+            self.generate_summaries(topics),
+            return_exceptions=False,
+        )
+
+        await self.set_status(transcript.id, "ended")
+
+    async def transcribe_file(self, audio_url: str, language: str) -> TranscriptType:
+        return await transcribe_file_with_processor(audio_url, language)
+
+    async def detect_topics(
+        self, transcript: TranscriptType, target_language: str
+    ) -> list[TitleSummary]:
+        return await topic_processing.detect_topics(
+            transcript,
+            target_language,
+            on_topic_callback=self.on_topic,
+            empty_pipeline=self.empty_pipeline,
+        )
+
+    async def generate_title(self, topics: list[TitleSummary]):
+        return await topic_processing.generate_title(
+            topics,
+            on_title_callback=self.on_title,
+            empty_pipeline=self.empty_pipeline,
+            logger=self.logger,
+        )
+
+    async def generate_summaries(self, topics: list[TitleSummary]):
+        transcript = await self.get_transcript()
+        return await topic_processing.generate_summaries(
+            topics,
+            transcript,
+            on_long_summary_callback=self.on_long_summary,
+            on_short_summary_callback=self.on_short_summary,
+            empty_pipeline=self.empty_pipeline,
+            logger=self.logger,
+        )
+
+
+@shared_task
+@asynctask
+async def task_pipeline_multitrack_process(
+    *, transcript_id: str, bucket_name: str, track_keys: list[str]
+):
+    pipeline = PipelineMainMultitrack(transcript_id=transcript_id)
+    try:
+        await pipeline.set_status(transcript_id, "processing")
+        await pipeline.process(bucket_name, track_keys)
+    except Exception:
+        await pipeline.set_status(transcript_id, "error")
+        raise
+
+    post_chain = chain(
+        task_cleanup_consent.si(transcript_id=transcript_id),
+        task_pipeline_post_to_zulip.si(transcript_id=transcript_id),
+        task_send_webhook_if_needed.si(transcript_id=transcript_id),
+    )
+    post_chain.delay()

server/reflector/pipelines/topic_processing.py

@@ -0,0 +1,109 @@
+"""
+Topic processing utilities
+==========================
+
+Shared topic detection, title generation, and summarization logic
+used across file and multitrack pipelines.
+"""
+
+from typing import Callable
+
+import structlog
+
+from reflector.db.transcripts import Transcript
+from reflector.processors import (
+    TranscriptFinalSummaryProcessor,
+    TranscriptFinalTitleProcessor,
+    TranscriptTopicDetectorProcessor,
+)
+from reflector.processors.types import TitleSummary
+from reflector.processors.types import Transcript as TranscriptType
+
+
+class EmptyPipeline:
+    def __init__(self, logger: structlog.BoundLogger):
+        self.logger = logger
+
+    def get_pref(self, k, d=None):
+        return d
+
+    async def emit(self, event):
+        pass
+
+
+async def detect_topics(
+    transcript: TranscriptType,
+    target_language: str,
+    *,
+    on_topic_callback: Callable,
+    empty_pipeline: EmptyPipeline,
+) -> list[TitleSummary]:
+    chunk_size = 300
+    topics: list[TitleSummary] = []
+
+    async def on_topic(topic: TitleSummary):
+        topics.append(topic)
+        return await on_topic_callback(topic)
+
+    topic_detector = TranscriptTopicDetectorProcessor(callback=on_topic)
+    topic_detector.set_pipeline(empty_pipeline)
+
+    for i in range(0, len(transcript.words), chunk_size):
+        chunk_words = transcript.words[i : i + chunk_size]
+        if not chunk_words:
+            continue
+
+        chunk_transcript = TranscriptType(
+            words=chunk_words, translation=transcript.translation
+        )
+
+        await topic_detector.push(chunk_transcript)
+
+    await topic_detector.flush()
+    return topics
+
+
+async def generate_title(
+    topics: list[TitleSummary],
+    *,
+    on_title_callback: Callable,
+    empty_pipeline: EmptyPipeline,
+    logger: structlog.BoundLogger,
+):
+    if not topics:
+        logger.warning("No topics for title generation")
+        return
+
+    processor = TranscriptFinalTitleProcessor(callback=on_title_callback)
+    processor.set_pipeline(empty_pipeline)
+
+    for topic in topics:
+        await processor.push(topic)
+
+    await processor.flush()
+
+
+async def generate_summaries(
+    topics: list[TitleSummary],
+    transcript: Transcript,
+    *,
+    on_long_summary_callback: Callable,
+    on_short_summary_callback: Callable,
+    empty_pipeline: EmptyPipeline,
+    logger: structlog.BoundLogger,
+):
+    if not topics:
+        logger.warning("No topics for summary generation")
+        return
+
+    processor = TranscriptFinalSummaryProcessor(
+        transcript=transcript,
+        callback=on_long_summary_callback,
+        on_short_summary=on_short_summary_callback,
+    )
+    processor.set_pipeline(empty_pipeline)
+
+    for topic in topics:
+        await processor.push(topic)
+
+    await processor.flush()

server/reflector/pipelines/transcription_helpers.py

@@ -0,0 +1,34 @@
+from reflector.processors.file_transcript import FileTranscriptInput
+from reflector.processors.file_transcript_auto import FileTranscriptAutoProcessor
+from reflector.processors.types import Transcript as TranscriptType
+
+
+async def transcribe_file_with_processor(
+    audio_url: str,
+    language: str,
+    processor_name: str | None = None,
+) -> TranscriptType:
+    processor = (
+        FileTranscriptAutoProcessor(name=processor_name)
+        if processor_name
+        else FileTranscriptAutoProcessor()
+    )
+    input_data = FileTranscriptInput(audio_url=audio_url, language=language)
+
+    result: TranscriptType | None = None
+
+    async def capture_result(transcript):
+        nonlocal result
+        result = transcript
+
+    processor.on(capture_result)
+    await processor.push(input_data)
+    await processor.flush()
+
+    if not result:
+        processor_label = processor_name or "default"
+        raise ValueError(
+            f"No transcript captured from {processor_label} processor for audio: {audio_url}"
+        )
+
+    return result