- Filter OrbStack/Docker Desktop PIDs from port conflict check (false positives on Mac)
- Check all infra ports (5432, 6379, 3900, 3903) not just app ports
- Fix ollama model detection to match on name column only
- Document OrbStack and cross-project port conflicts in troubleshooting
* feat: add local pyannote file diarization processor
Enables file diarization without Modal by using pyannote.audio locally.
Downloads model bundle from S3 on first use, caches locally, patches
config to use local paths. Set DIARIZATION_BACKEND=pyannote to enable.
* fix: standalone setup enables pyannote diarization and public mode
Replace DIARIZATION_ENABLED=false with DIARIZATION_BACKEND=pyannote so
file uploads get speaker diarization out of the box. Add PUBLIC_MODE=true
so unauthenticated users can list/browse transcripts.
* fix: touch env files before first compose_cmd in standalone setup
docker-compose.yml references www/.env.local as env_file, but the
setup script only creates it in step 4. compose_cmd calls in step 3
(Garage) fail on a fresh clone when the file doesn't exist yet.
* feat: standalone uses self-hosted GPU service for transcription+diarization
Replace in-process pyannote approach with self-hosted gpu/self_hosted/ service.
Same HTTP API as Modal — just TRANSCRIPT_URL/DIARIZATION_URL point to local container.
- Add gpu/self_hosted/Dockerfile.cpu (GPU Dockerfile minus NVIDIA CUDA)
- Add S3 model bundle fallback in diarizer.py when HF_TOKEN not set
- Add gpu service to docker-compose.standalone.yml with compose env overrides
- Fix /browse empty in PUBLIC_MODE (search+list queries filtered out roomless transcripts)
- Remove audio_diarization_pyannote.py, file_diarization_pyannote.py and tests
- Remove pyannote-audio from server local deps
* fix: allow unauthenticated GPU requests when no API key configured
OAuth2PasswordBearer with auto_error=True rejects requests without
Authorization header before apikey_auth can check if auth is needed.
* fix: rename standalone gpu service to cpu to match Dockerfile.cpu usage
* docs: add programmatic testing section and fix gpu->cpu naming in setup script/docs
- Add "Testing programmatically" section to standalone docs with curl commands
for creating transcript, uploading audio, polling status, checking result
- Fix setup-standalone.sh to reference `cpu` service (was still `gpu` after rename)
- Update all docs references from gpu to cpu service naming
---------
Co-authored-by: Igor Loskutov <igor.loskutoff@gmail.com>
Add TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL setting to enable S3-compatible
backends (Garage, MinIO). When set, uses path-style addressing and
routes all requests to the custom endpoint. When unset, AWS behavior
is unchanged.
- AwsStorage: accept aws_endpoint_url, pass to all 6 session.client()
calls, configure path-style addressing and base_url
- Fix 4 direct AwsStorage constructions in Hatchet workflows to pass
endpoint_url (would have silently targeted wrong endpoint)
- Standalone: add Garage service to docker-compose.standalone.yml,
setup script initializes layout/bucket/key and writes credentials
- Fix compose_cmd() bug: Mac path was missing standalone yml
- garage.toml template with runtime secret generation via openssl
Single script takes fresh clone to working Reflector: Ollama/LLM setup,
env file generation (server/.env + www/.env.local), docker compose up,
health checks. No Hatchet in standalone — live pipeline is pure Celery.
Ollama profiles (ollama-gpu, ollama-cpu) are only for Linux standalone
deployment. Mac devs never use them. Separate file keeps the main
compose clean and provides a natural home for future standalone services
(MinIO, etc.).
Linux: docker compose -f docker-compose.yml -f docker-compose.standalone.yml --profile ollama-gpu up -d
Mac: docker compose up -d (native Ollama, no standalone file needed)
- Add setup script (scripts/setup-local-llm.sh) for one-command Ollama setup
Mac: native Metal GPU, Linux: containerized via docker-compose profiles
- Add ollama-gpu and ollama-cpu docker-compose profiles for Linux
- Add extra_hosts to server/hatchet-worker-llm for host.docker.internal
- Pass response_format JSON schema in StructuredOutputWorkflow.extract()
enabling grammar-based constrained decoding on Ollama/llama.cpp/vLLM/OpenAI
- Update .env.example with Ollama as default LLM option
- Add Ollama PRD and local dev setup docs
* feat: add transcript format parameter to GET endpoint
Add transcript_format query parameter to /v1/transcripts/{id} endpoint
with support for multiple output formats using discriminated unions.
Formats supported:
- text: Plain speaker dialogue (default)
- text-timestamped: Dialogue with [MM:SS] timestamps
- webvtt-named: WebVTT subtitles with participant names
- json: Structured segments with full metadata
Response models use Pydantic discriminated unions with transcript_format
as discriminator field. POST/PATCH endpoints return GetTranscriptWithParticipants
for minimal responses. GET endpoint returns format-specific models.
* Copy transcript format
* Regenerate types
* Fix transcript formats
* Don't throw inside try
* Remove any type
* Toast share copy errors
* transcript_format exhaustiveness and python idiomatic assert_never
* format_timestamp_mmss clear type definition
* Rename seconds_to_timestamp
* Test transcript format with overlapping speakers
* exact match for vtt multispeaker test
---------
Co-authored-by: Sergey Mankovsky <sergey@monadical.com>
Co-authored-by: Igor Loskutov <igor.loskutoff@gmail.com>
