Merge branch 'main' of github.com:Monadical-SAS/reflector into feat-sharing

server/migrations/versions/f819277e5169_audio_location.py

@@ -0,0 +1,35 @@
+"""audio_location
+
+Revision ID: f819277e5169
+Revises: 4814901632bc
+Create Date: 2023-11-16 10:29:09.351664
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = "f819277e5169"
+down_revision: Union[str, None] = "4814901632bc"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.add_column(
+        "transcript",
+        sa.Column(
+            "audio_location", sa.String(), server_default="local", nullable=False
+        ),
+    )
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    op.drop_column("transcript", "audio_location")
+    # ### end Alembic commands ###

server/reflector/db/transcripts.py

@@ -11,6 +11,7 @@ from pydantic import BaseModel, Field
 from reflector.db import database, metadata
 from reflector.processors.types import Word as ProcessorWord
 from reflector.settings import settings
+from reflector.storage import Storage
 
 transcripts = sqlalchemy.Table(
     "transcript",
@@ -28,6 +29,12 @@ transcripts = sqlalchemy.Table(
     sqlalchemy.Column("events", sqlalchemy.JSON),
     sqlalchemy.Column("source_language", sqlalchemy.String, nullable=True),
     sqlalchemy.Column("target_language", sqlalchemy.String, nullable=True),
+    sqlalchemy.Column(
+        "audio_location",
+        sqlalchemy.String,
+        nullable=False,
+        server_default="local",
+    ),
     # with user attached, optional
     sqlalchemy.Column("user_id", sqlalchemy.String),
     sqlalchemy.Column(
@@ -39,15 +46,22 @@ transcripts = sqlalchemy.Table(
 )
 
 
-def generate_uuid4():
+def generate_uuid4() -> str:
     return str(uuid4())
 
 
-def generate_transcript_name():
+def generate_transcript_name() -> str:
     now = datetime.utcnow()
     return f"Transcript {now.strftime('%Y-%m-%d %H:%M:%S')}"
 
 
+def get_storage() -> Storage:
+    return Storage.get_instance(
+        name=settings.TRANSCRIPT_STORAGE_BACKEND,
+        settings_prefix="TRANSCRIPT_STORAGE_",
+    )
+
+
 class AudioWaveform(BaseModel):
     data: list[float]
 
@@ -114,6 +128,7 @@ class Transcript(BaseModel):
     source_language: str = "en"
     target_language: str = "en"
     share_mode: Literal["private", "semi-private", "public"] = "private"
+    audio_location: str = "local"
 
     def add_event(self, event: str, data: BaseModel) -> TranscriptEvent:
         ev = TranscriptEvent(event=event, data=data.model_dump())
@@ -140,6 +155,10 @@ class Transcript(BaseModel):
     def data_path(self):
         return Path(settings.DATA_DIR) / self.id
 
+    @property
+    def audio_wav_filename(self):
+        return self.data_path / "audio.wav"
+
     @property
     def audio_mp3_filename(self):
         return self.data_path / "audio.mp3"
@@ -148,6 +167,10 @@ class Transcript(BaseModel):
     def audio_waveform_filename(self):
         return self.data_path / "audio.json"
 
+    @property
+    def storage_audio_path(self):
+        return f"{self.id}/audio.mp3"
+
     @property
     def audio_waveform(self):
         try:
@@ -160,6 +183,40 @@ class Transcript(BaseModel):
 
         return AudioWaveform(data=data)
 
+    async def get_audio_url(self) -> str:
+        if self.audio_location == "local":
+            return self._generate_local_audio_link()
+        elif self.audio_location == "storage":
+            return await self._generate_storage_audio_link()
+        raise Exception(f"Unknown audio location {self.audio_location}")
+
+    async def _generate_storage_audio_link(self) -> str:
+        return await get_storage().get_file_url(self.storage_audio_path)
+
+    def _generate_local_audio_link(self) -> str:
+        # we need to create an url to be used for diarization
+        # we can't use the audio_mp3_filename because it's not accessible
+        # from the diarization processor
+        from datetime import timedelta
+
+        from reflector.app import app
+        from reflector.views.transcripts import create_access_token
+
+        path = app.url_path_for(
+            "transcript_get_audio_mp3",
+            transcript_id=self.id,
+        )
+        url = f"{settings.BASE_URL}{path}"
+        if self.user_id:
+            # we pass token only if the user_id is set
+            # otherwise, the audio is public
+            token = create_access_token(
+                {"sub": self.user_id},
+                expires_delta=timedelta(minutes=15),
+            )
+            url += f"?token={token}"
+        return url
+
 
 class TranscriptController:
     async def get_all(
@@ -336,5 +393,22 @@ class TranscriptController:
         transcript.upsert_topic(topic)
         await self.update(transcript, {"topics": transcript.topics_dump()})
 
+    async def move_mp3_to_storage(self, transcript: Transcript):
+        """
+        Move mp3 file to storage
+        """
+
+        # store the audio on external storage
+        await get_storage().put_file(
+            transcript.storage_audio_path,
+            transcript.audio_mp3_filename.read_bytes(),
+        )
+
+        # indicate on the transcript that the audio is now on storage
+        await self.update(transcript, {"audio_location": "storage"})
+
+        # unlink the local file
+        transcript.audio_mp3_filename.unlink(missing_ok=True)
+
 
 transcripts_controller = TranscriptController()

server/reflector/pipelines/main_live_pipeline.py

@@ -12,13 +12,11 @@ It is directly linked to our data model.
 """
 
 import asyncio
+import functools
 from contextlib import asynccontextmanager
-from datetime import timedelta
-from pathlib import Path
 
-from celery import shared_task
+from celery import chord, group, shared_task
 from pydantic import BaseModel
-from reflector.app import app
 from reflector.db.transcripts import (
     Transcript,
     TranscriptDuration,
@@ -55,6 +53,22 @@ from reflector.processors.types import (
 from reflector.processors.types import Transcript as TranscriptProcessorType
 from reflector.settings import settings
 from reflector.ws_manager import WebsocketManager, get_ws_manager
+from structlog import BoundLogger as Logger
+
+
+def asynctask(f):
+    @functools.wraps(f)
+    def wrapper(*args, **kwargs):
+        coro = f(*args, **kwargs)
+        try:
+            loop = asyncio.get_running_loop()
+        except RuntimeError:
+            loop = None
+        if loop and loop.is_running():
+            return loop.run_until_complete(coro)
+        return asyncio.run(coro)
+
+    return wrapper
 
 
 def broadcast_to_sockets(func):
@@ -75,6 +89,26 @@ def broadcast_to_sockets(func):
     return wrapper
 
 
+def get_transcript(func):
+    """
+    Decorator to fetch the transcript from the database from the first argument
+    """
+
+    async def wrapper(**kwargs):
+        transcript_id = kwargs.pop("transcript_id")
+        transcript = await transcripts_controller.get_by_id(transcript_id=transcript_id)
+        if not transcript:
+            raise Exception("Transcript {transcript_id} not found")
+        tlogger = logger.bind(transcript_id=transcript.id)
+        try:
+            return await func(transcript=transcript, logger=tlogger, **kwargs)
+        except Exception as exc:
+            tlogger.error("Pipeline error", exc_info=exc)
+            raise
+
+    return wrapper
+
+
 class StrValue(BaseModel):
     value: str
 
@@ -99,6 +133,19 @@ class PipelineMainBase(PipelineRunner):
             raise Exception("Transcript not found")
         return result
 
+    def get_transcript_topics(self, transcript: Transcript) -> list[TranscriptTopic]:
+        return [
+            TitleSummaryWithIdProcessorType(
+                id=topic.id,
+                title=topic.title,
+                summary=topic.summary,
+                timestamp=topic.timestamp,
+                duration=topic.duration,
+                transcript=TranscriptProcessorType(words=topic.words),
+            )
+            for topic in transcript.topics
+        ]
+
     @asynccontextmanager
     async def transaction(self):
         async with self._lock:
@@ -116,7 +163,7 @@ class PipelineMainBase(PipelineRunner):
                 "flush": "processing",
                 "error": "error",
             }
-        elif isinstance(self, PipelineMainDiarization):
+        elif isinstance(self, PipelineMainFinalSummaries):
             status_mapping = {
                 "push": "processing",
                 "flush": "processing",
@@ -124,7 +171,8 @@ class PipelineMainBase(PipelineRunner):
                 "ended": "ended",
             }
         else:
-            raise Exception(f"Runner {self.__class__} is missing status mapping")
+            # intermediate pipeline don't update status
+            return
 
         # mutate to model status
         status = status_mapping.get(status)
@@ -262,9 +310,10 @@ class PipelineMainBase(PipelineRunner):
 
 
 class PipelineMainLive(PipelineMainBase):
-    audio_filename: Path | None = None
+    """
-    source_language: str = "en"
+    Main pipeline for live streaming, attach to RTC connection
-    target_language: str = "en"
+    Any long post process should be done in the post pipeline
+    """
 
     async def create(self) -> Pipeline:
         # create a context for the whole rtc transaction
@@ -274,7 +323,7 @@ class PipelineMainLive(PipelineMainBase):
 
         processors = [
             AudioFileWriterProcessor(
-                path=transcript.audio_mp3_filename,
+                path=transcript.audio_wav_filename,
                 on_duration=self.on_duration,
             ),
             AudioChunkerProcessor(),
@@ -283,26 +332,13 @@ class PipelineMainLive(PipelineMainBase):
             TranscriptLinerProcessor(),
             TranscriptTranslatorProcessor.as_threaded(callback=self.on_transcript),
             TranscriptTopicDetectorProcessor.as_threaded(callback=self.on_topic),
-            BroadcastProcessor(
-                processors=[
-                    TranscriptFinalTitleProcessor.as_threaded(callback=self.on_title),
-                    AudioWaveformProcessor.as_threaded(
-                        audio_path=transcript.audio_mp3_filename,
-                        waveform_path=transcript.audio_waveform_filename,
-                        on_waveform=self.on_waveform,
-                    ),
-                ]
-            ),
         ]
         pipeline = Pipeline(*processors)
         pipeline.options = self
         pipeline.set_pref("audio:source_language", transcript.source_language)
         pipeline.set_pref("audio:target_language", transcript.target_language)
         pipeline.logger.bind(transcript_id=transcript.id)
-        pipeline.logger.info(
+        pipeline.logger.info("Pipeline main live created")
-            "Pipeline main live created",
-            transcript_id=self.transcript_id,
-        )
 
         return pipeline
 
@@ -310,26 +346,106 @@ class PipelineMainLive(PipelineMainBase):
         # when the pipeline ends, connect to the post pipeline
         logger.info("Pipeline main live ended", transcript_id=self.transcript_id)
         logger.info("Scheduling pipeline main post", transcript_id=self.transcript_id)
-        task_pipeline_main_post.delay(transcript_id=self.transcript_id)
+        pipeline_post(transcript_id=self.transcript_id)
 
 
 class PipelineMainDiarization(PipelineMainBase):
     """
-    Diarization is a long time process, so we do it in a separate pipeline
+    Diarize the audio and update topics
-    When done, adjust the short and final summary
     """
 
     async def create(self) -> Pipeline:
         # create a context for the whole rtc transaction
         # add a customised logger to the context
         self.prepare()
-        processors = []
+        pipeline = Pipeline(
-        if settings.DIARIZATION_ENABLED:
+            AudioDiarizationAutoProcessor(callback=self.on_topic),
-            processors += [
+        )
-                AudioDiarizationAutoProcessor(callback=self.on_topic),
+        pipeline.options = self
-            ]
 
-        processors += [
+        # now let's start the pipeline by pushing information to the
+        # first processor diarization processor
+        # XXX translation is lost when converting our data model to the processor model
+        transcript = await self.get_transcript()
+
+        # diarization works only if the file is uploaded to an external storage
+        if transcript.audio_location == "local":
+            pipeline.logger.info("Audio is local, skipping diarization")
+            return
+
+        topics = self.get_transcript_topics(transcript)
+        audio_url = await transcript.get_audio_url()
+        audio_diarization_input = AudioDiarizationInput(
+            audio_url=audio_url,
+            topics=topics,
+        )
+
+        # as tempting to use pipeline.push, prefer to use the runner
+        # to let the start just do one job.
+        pipeline.logger.bind(transcript_id=transcript.id)
+        pipeline.logger.info("Diarization pipeline created")
+        self.push(audio_diarization_input)
+        self.flush()
+
+        return pipeline
+
+
+class PipelineMainFromTopics(PipelineMainBase):
+    """
+    Pseudo class for generating a pipeline from topics
+    """
+
+    def get_processors(self) -> list:
+        raise NotImplementedError
+
+    async def create(self) -> Pipeline:
+        self.prepare()
+
+        # get transcript
+        self._transcript = transcript = await self.get_transcript()
+
+        # create pipeline
+        processors = self.get_processors()
+        pipeline = Pipeline(*processors)
+        pipeline.options = self
+        pipeline.logger.bind(transcript_id=transcript.id)
+        pipeline.logger.info(f"{self.__class__.__name__} pipeline created")
+
+        # push topics
+        topics = self.get_transcript_topics(transcript)
+        for topic in topics:
+            self.push(topic)
+
+        self.flush()
+
+        return pipeline
+
+
+class PipelineMainTitleAndShortSummary(PipelineMainFromTopics):
+    """
+    Generate title from the topics
+    """
+
+    def get_processors(self) -> list:
+        return [
+            BroadcastProcessor(
+                processors=[
+                    TranscriptFinalTitleProcessor.as_threaded(callback=self.on_title),
+                    TranscriptFinalShortSummaryProcessor.as_threaded(
+                        callback=self.on_short_summary
+                    ),
+                ]
+            )
+        ]
+
+
+class PipelineMainFinalSummaries(PipelineMainFromTopics):
+    """
+    Generate summaries from the topics
+    """
+
+    def get_processors(self) -> list:
+        return [
             BroadcastProcessor(
                 processors=[
                     TranscriptFinalLongSummaryProcessor.as_threaded(
@@ -341,65 +457,164 @@ class PipelineMainDiarization(PipelineMainBase):
                 ]
             ),
         ]
-        pipeline = Pipeline(*processors)
-        pipeline.options = self
 
-        # now let's start the pipeline by pushing information to the
+
-        # first processor diarization processor
+class PipelineMainWaveform(PipelineMainFromTopics):
-        # XXX translation is lost when converting our data model to the processor model
+    """
-        transcript = await self.get_transcript()
+    Generate waveform
-        topics = [
+    """
-            TitleSummaryWithIdProcessorType(
+
-                id=topic.id,
+    def get_processors(self) -> list:
-                title=topic.title,
+        return [
-                summary=topic.summary,
+            AudioWaveformProcessor.as_threaded(
-                timestamp=topic.timestamp,
+                audio_path=self._transcript.audio_wav_filename,
-                duration=topic.duration,
+                waveform_path=self._transcript.audio_waveform_filename,
-                transcript=TranscriptProcessorType(words=topic.words),
+                on_waveform=self.on_waveform,
-            )
+            ),
-            for topic in transcript.topics
         ]
 
-        # we need to create an url to be used for diarization
-        # we can't use the audio_mp3_filename because it's not accessible
-        # from the diarization processor
-        from reflector.views.transcripts import create_access_token
 
-        path = app.url_path_for(
+@get_transcript
-            "transcript_get_audio_mp3",
+async def pipeline_waveform(transcript: Transcript, logger: Logger):
-            transcript_id=transcript.id,
+    logger.info("Starting waveform")
-        )
+    runner = PipelineMainWaveform(transcript_id=transcript.id)
-        url = f"{settings.BASE_URL}{path}"
+    await runner.run()
-        if transcript.user_id:
+    logger.info("Waveform done")
-            # we pass token only if the user_id is set
-            # otherwise, the audio is public
-            token = create_access_token(
-                {"sub": transcript.user_id},
-                expires_delta=timedelta(minutes=15),
-            )
-            url += f"?token={token}"
-        audio_diarization_input = AudioDiarizationInput(
-            audio_url=url,
-            topics=topics,
-        )
 
-        # as tempting to use pipeline.push, prefer to use the runner
-        # to let the start just do one job.
-        pipeline.logger.bind(transcript_id=transcript.id)
-        pipeline.logger.info(
-            "Pipeline main post created", transcript_id=self.transcript_id
-        )
-        self.push(audio_diarization_input)
-        self.flush()
 
-        return pipeline
+@get_transcript
+async def pipeline_convert_to_mp3(transcript: Transcript, logger: Logger):
+    logger.info("Starting convert to mp3")
+
+    # If the audio wav is not available, just skip
+    wav_filename = transcript.audio_wav_filename
+    if not wav_filename.exists():
+        logger.warning("Wav file not found, may be already converted")
+        return
+
+    # Convert to mp3
+    mp3_filename = transcript.audio_mp3_filename
+
+    import av
+
+    with av.open(wav_filename.as_posix()) as in_container:
+        in_stream = in_container.streams.audio[0]
+        with av.open(mp3_filename.as_posix(), "w") as out_container:
+            out_stream = out_container.add_stream("mp3")
+            for frame in in_container.decode(in_stream):
+                for packet in out_stream.encode(frame):
+                    out_container.mux(packet)
+
+    # Delete the wav file
+    transcript.audio_wav_filename.unlink(missing_ok=True)
+
+    logger.info("Convert to mp3 done")
+
+
+@get_transcript
+async def pipeline_upload_mp3(transcript: Transcript, logger: Logger):
+    if not settings.TRANSCRIPT_STORAGE_BACKEND:
+        logger.info("No storage backend configured, skipping mp3 upload")
+        return
+
+    logger.info("Starting upload mp3")
+
+    # If the audio mp3 is not available, just skip
+    mp3_filename = transcript.audio_mp3_filename
+    if not mp3_filename.exists():
+        logger.warning("Mp3 file not found, may be already uploaded")
+        return
+
+    # Upload to external storage and delete the file
+    await transcripts_controller.move_mp3_to_storage(transcript)
+
+    logger.info("Upload mp3 done")
+
+
+@get_transcript
+async def pipeline_diarization(transcript: Transcript, logger: Logger):
+    logger.info("Starting diarization")
+    runner = PipelineMainDiarization(transcript_id=transcript.id)
+    await runner.run()
+    logger.info("Diarization done")
+
+
+@get_transcript
+async def pipeline_title_and_short_summary(transcript: Transcript, logger: Logger):
+    logger.info("Starting title and short summary")
+    runner = PipelineMainTitleAndShortSummary(transcript_id=transcript.id)
+    await runner.run()
+    logger.info("Title and short summary done")
+
+
+@get_transcript
+async def pipeline_summaries(transcript: Transcript, logger: Logger):
+    logger.info("Starting summaries")
+    runner = PipelineMainFinalSummaries(transcript_id=transcript.id)
+    await runner.run()
+    logger.info("Summaries done")
+
+
+# ===================================================================
+# Celery tasks that can be called from the API
+# ===================================================================
 
 
 @shared_task
-def task_pipeline_main_post(transcript_id: str):
+@asynctask
-    logger.info(
+async def task_pipeline_waveform(*, transcript_id: str):
-        "Starting main post pipeline",
+    await pipeline_waveform(transcript_id=transcript_id)
-        transcript_id=transcript_id,
+
+
+@shared_task
+@asynctask
+async def task_pipeline_convert_to_mp3(*, transcript_id: str):
+    await pipeline_convert_to_mp3(transcript_id=transcript_id)
+
+
+@shared_task
+@asynctask
+async def task_pipeline_upload_mp3(*, transcript_id: str):
+    await pipeline_upload_mp3(transcript_id=transcript_id)
+
+
+@shared_task
+@asynctask
+async def task_pipeline_diarization(*, transcript_id: str):
+    await pipeline_diarization(transcript_id=transcript_id)
+
+
+@shared_task
+@asynctask
+async def task_pipeline_title_and_short_summary(*, transcript_id: str):
+    await pipeline_title_and_short_summary(transcript_id=transcript_id)
+
+
+@shared_task
+@asynctask
+async def task_pipeline_final_summaries(*, transcript_id: str):
+    await pipeline_summaries(transcript_id=transcript_id)
+
+
+def pipeline_post(*, transcript_id: str):
+    """
+    Run the post pipeline
+    """
+    chain_mp3_and_diarize = (
+        task_pipeline_waveform.si(transcript_id=transcript_id)
+        | task_pipeline_convert_to_mp3.si(transcript_id=transcript_id)
+        | task_pipeline_upload_mp3.si(transcript_id=transcript_id)
+        | task_pipeline_diarization.si(transcript_id=transcript_id)
     )
-    runner = PipelineMainDiarization(transcript_id=transcript_id)
+    chain_title_preview = task_pipeline_title_and_short_summary.si(
-    runner.start_sync()
+        transcript_id=transcript_id
+    )
+    chain_final_summaries = task_pipeline_final_summaries.si(
+        transcript_id=transcript_id
+    )
+
+    chain = chord(
+        group(chain_mp3_and_diarize, chain_title_preview),
+        chain_final_summaries,
+    )
+    chain.delay()

server/reflector/pipelines/runner.py

@@ -106,6 +106,14 @@ class PipelineRunner(BaseModel):
             if not self.pipeline:
                 self.pipeline = await self.create()
 
+            if not self.pipeline:
+                # no pipeline created in create, just finish it then.
+                await self._set_status("ended")
+                self._ev_done.set()
+                if self.on_ended:
+                    await self.on_ended()
+                return
+
             # start the loop
             await self._set_status("started")
             while not self._ev_done.is_set():
@@ -119,8 +127,7 @@ class PipelineRunner(BaseModel):
             self._logger.exception("Runner error")
             await self._set_status("error")
             self._ev_done.set()
-            if self.on_ended:
+            raise
-                await self.on_ended()
 
     async def cmd_push(self, data):
         if self._is_first_push:

server/reflector/settings.py

@@ -54,7 +54,7 @@ class Settings(BaseSettings):
     TRANSCRIPT_MODAL_API_KEY: str | None = None
 
     # Audio transcription storage
-    TRANSCRIPT_STORAGE_BACKEND: str = "aws"
+    TRANSCRIPT_STORAGE_BACKEND: str | None = None
 
     # Storage configuration for AWS
     TRANSCRIPT_STORAGE_AWS_BUCKET_NAME: str = "reflector-bucket"
@@ -62,9 +62,6 @@ class Settings(BaseSettings):
     TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID: str | None = None
     TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY: str | None = None
 
-    # Transcript MP3 storage
-    TRANSCRIPT_MP3_STORAGE_BACKEND: str = "aws"
-
     # LLM
     # available backend: openai, modal, oobabooga
     LLM_BACKEND: str = "oobabooga"

server/reflector/storage/base.py

@@ -1,6 +1,7 @@
+import importlib
+
 from pydantic import BaseModel
 from reflector.settings import settings
-import importlib
 
 
 class FileResult(BaseModel):
@@ -17,7 +18,7 @@ class Storage:
         cls._registry[name] = kclass
 
     @classmethod
-    def get_instance(cls, name, settings_prefix=""):
+    def get_instance(cls, name: str, settings_prefix: str = ""):
         if name not in cls._registry:
             module_name = f"reflector.storage.storage_{name}"
             importlib.import_module(module_name)
@@ -45,3 +46,9 @@ class Storage:
 
     async def _delete_file(self, filename: str):
         raise NotImplementedError
+
+    async def get_file_url(self, filename: str) -> str:
+        return await self._get_file_url(filename)
+
+    async def _get_file_url(self, filename: str) -> str:
+        raise NotImplementedError

server/reflector/storage/storage_aws.py

@@ -1,6 +1,6 @@
 import aioboto3
-from reflector.storage.base import Storage, FileResult
 from reflector.logger import logger
+from reflector.storage.base import FileResult, Storage
 
 
 class AwsStorage(Storage):
@@ -44,16 +44,18 @@ class AwsStorage(Storage):
                 Body=data,
             )
 
+    async def _get_file_url(self, filename: str) -> FileResult:
+        bucket = self.aws_bucket_name
+        folder = self.aws_folder
+        s3filename = f"{folder}/{filename}" if folder else filename
+        async with self.session.client("s3") as client:
             presigned_url = await client.generate_presigned_url(
                 "get_object",
                 Params={"Bucket": bucket, "Key": s3filename},
                 ExpiresIn=3600,
             )
 
-            return FileResult(
+            return presigned_url
-                filename=filename,
-                url=presigned_url,
-            )
 
     async def _delete_file(self, filename: str):
         bucket = self.aws_bucket_name

server/reflector/views/transcripts.py

@@ -1,12 +1,14 @@
 from datetime import datetime, timedelta
 from typing import Annotated, Literal, Optional
 
+import httpx
 import reflector.auth as auth
 from fastapi import (
     APIRouter,
     Depends,
     HTTPException,
     Request,
+    Response,
     WebSocket,
     WebSocketDisconnect,
     status,
@@ -245,6 +247,42 @@ async def transcript_get_audio_mp3(
         transcript_id, user_id=user_id
     )
 
+    if transcript.audio_location == "storage":
+        # proxy S3 file, to prevent issue with CORS
+        url = await transcript.get_audio_url()
+        headers = {}
+
+        copy_headers = ["range", "accept-encoding"]
+        for header in copy_headers:
+            if header in request.headers:
+                headers[header] = request.headers[header]
+
+        async with httpx.AsyncClient() as client:
+            resp = await client.request(request.method, url, headers=headers)
+            return Response(
+                content=resp.content,
+                status_code=resp.status_code,
+                headers=resp.headers,
+            )
+
+    if transcript.audio_location == "storage":
+        # proxy S3 file, to prevent issue with CORS
+        url = await transcript.get_audio_url()
+        headers = {}
+
+        copy_headers = ["range", "accept-encoding"]
+        for header in copy_headers:
+            if header in request.headers:
+                headers[header] = request.headers[header]
+
+        async with httpx.AsyncClient() as client:
+            resp = await client.request(request.method, url, headers=headers)
+            return Response(
+                content=resp.content,
+                status_code=resp.status_code,
+                headers=resp.headers,
+            )
+
     if not transcript.audio_mp3_filename.exists():
         raise HTTPException(status_code=500, detail="Audio not found")
 
@@ -269,8 +307,8 @@ async def transcript_get_audio_waveform(
         transcript_id, user_id=user_id
     )
 
-    if not transcript.audio_mp3_filename.exists():
+    if not transcript.audio_waveform_filename.exists():
-        raise HTTPException(status_code=500, detail="Audio not found")
+        raise HTTPException(status_code=404, detail="Audio not found")
 
     return transcript.audio_waveform
 

server/tests/conftest.py

@@ -1,4 +1,5 @@
 from unittest.mock import patch
+from tempfile import NamedTemporaryFile
 
 import pytest
 
@@ -7,7 +8,6 @@ import pytest
 @pytest.mark.asyncio
 async def setup_database():
     from reflector.settings import settings
-    from tempfile import NamedTemporaryFile
 
     with NamedTemporaryFile() as f:
         settings.DATABASE_URL = f"sqlite:///{f.name}"
@@ -103,6 +103,25 @@ async def dummy_llm():
         yield
 
 
+@pytest.fixture
+async def dummy_storage():
+    from reflector.storage.base import Storage
+
+    class DummyStorage(Storage):
+        async def _put_file(self, *args, **kwargs):
+            pass
+
+        async def _delete_file(self, *args, **kwargs):
+            pass
+
+        async def _get_file_url(self, *args, **kwargs):
+            return "http://fake_server/audio.mp3"
+
+    with patch("reflector.storage.base.Storage.get_instance") as mock_storage:
+        mock_storage.return_value = DummyStorage()
+        yield
+
+
 @pytest.fixture
 def nltk():
     with patch("reflector.llm.base.LLM.ensure_nltk") as mock_nltk:
@@ -133,4 +152,17 @@ def celery_enable_logging():
 
 @pytest.fixture(scope="session")
 def celery_config():
-    return {"broker_url": "memory://", "result_backend": "rpc"}
+    with NamedTemporaryFile() as f:
+        yield {
+            "broker_url": "memory://",
+            "result_backend": f"db+sqlite:///{f.name}",
+        }
+
+
+@pytest.fixture(scope="session")
+def fake_mp3_upload():
+    with patch(
+        "reflector.db.transcripts.TranscriptController.move_mp3_to_storage"
+    ) as mock_move:
+        mock_move.return_value = True
+        yield

server/tests/test_transcripts_rtc_ws.py

@@ -66,6 +66,8 @@ async def test_transcript_rtc_and_websocket(
     dummy_transcript,
     dummy_processors,
     dummy_diarization,
+    dummy_storage,
+    fake_mp3_upload,
     ensure_casing,
     appserver,
     sentence_tokenize,
@@ -220,6 +222,8 @@ async def test_transcript_rtc_and_websocket_and_fr(
     dummy_transcript,
     dummy_processors,
     dummy_diarization,
+    dummy_storage,
+    fake_mp3_upload,
     ensure_casing,
     appserver,
     sentence_tokenize,