From adc4c20bf40991a147dd9032614461f73033e41a Mon Sep 17 00:00:00 2001 From: Igor Monadical Date: Wed, 11 Feb 2026 12:41:32 -0500 Subject: [PATCH] feat: add local pyannote file diarization processor (#858) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 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 --- docker-compose.standalone.yml | 52 ++ docs/docs/installation/setup-standalone.md | 60 +- gpu/self_hosted/Dockerfile.cpu | 39 + gpu/self_hosted/app/auth.py | 6 +- gpu/self_hosted/app/services/diarizer.py | 73 +- scripts/setup-standalone.sh | 32 +- server/pyproject.toml | 1 - server/reflector/db/search.py | 3 +- server/reflector/db/transcripts.py | 2 +- .../processors/audio_diarization_pyannote.py | 74 -- server/reflector/settings.py | 7 +- server/uv.lock | 676 +----------------- 12 files changed, 248 insertions(+), 777 deletions(-) create mode 100644 gpu/self_hosted/Dockerfile.cpu delete mode 100644 server/reflector/processors/audio_diarization_pyannote.py diff --git a/docker-compose.standalone.yml b/docker-compose.standalone.yml index 996392d0..bc5c6c14 100644 --- a/docker-compose.standalone.yml +++ b/docker-compose.standalone.yml @@ -58,7 +58,59 @@ services: timeout: 5s retries: 5 + # Override server/worker/beat to use self-hosted GPU service for transcription+diarization. + # compose `environment:` overrides values from `env_file:` — no need to edit server/.env. + server: + environment: + TRANSCRIPT_BACKEND: modal + TRANSCRIPT_URL: http://cpu:8000 + DIARIZATION_BACKEND: modal + DIARIZATION_URL: http://cpu:8000 + + worker: + environment: + TRANSCRIPT_BACKEND: modal + TRANSCRIPT_URL: http://cpu:8000 + DIARIZATION_BACKEND: modal + DIARIZATION_URL: http://cpu:8000 + + cpu: + build: + context: ./gpu/self_hosted + dockerfile: Dockerfile.cpu + volumes: + - gpu_cache:/root/.cache + restart: unless-stopped + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:8000/docs"] + interval: 15s + timeout: 5s + retries: 10 + start_period: 120s + + gpu-nvidia: + build: + context: ./gpu/self_hosted + profiles: ["gpu-nvidia"] + volumes: + - gpu_cache:/root/.cache + deploy: + resources: + reservations: + devices: + - driver: nvidia + count: all + capabilities: [gpu] + restart: unless-stopped + healthcheck: + test: ["CMD", "curl", "-f", "http://localhost:8000/docs"] + interval: 15s + timeout: 5s + retries: 10 + start_period: 120s + volumes: garage_data: garage_meta: ollama_data: + gpu_cache: diff --git a/docs/docs/installation/setup-standalone.md b/docs/docs/installation/setup-standalone.md index 888ca234..78eaa2f9 100644 --- a/docs/docs/installation/setup-standalone.md +++ b/docs/docs/installation/setup-standalone.md @@ -42,8 +42,10 @@ Generates `server/.env` and `www/.env.local` with standalone defaults: | `REDIS_HOST` | `redis` | Docker-internal hostname | | `CELERY_BROKER_URL` | `redis://redis:6379/1` | Docker-internal hostname | | `AUTH_BACKEND` | `none` | No Authentik in standalone | -| `TRANSCRIPT_BACKEND` | `whisper` | Local transcription | -| `DIARIZATION_ENABLED` | `false` | No diarization backend | +| `TRANSCRIPT_BACKEND` | `modal` | HTTP API to self-hosted CPU service | +| `TRANSCRIPT_URL` | `http://cpu:8000` | Docker-internal CPU service | +| `DIARIZATION_BACKEND` | `modal` | HTTP API to self-hosted CPU service | +| `DIARIZATION_URL` | `http://cpu:8000` | Docker-internal CPU service | | `TRANSLATION_BACKEND` | `passthrough` | No Modal | | `LLM_URL` | `http://host.docker.internal:11434/v1` (Mac) | Ollama endpoint | @@ -80,19 +82,23 @@ Garage config template lives at `scripts/garage.toml`. The setup script generate > **Note**: Presigned URLs embed the Garage Docker hostname (`http://garage:3900`). This is fine — the server proxies S3 responses to the browser. Modal GPU workers cannot reach internal Garage, but standalone doesn't use Modal. -### 4. Transcription and diarization (NOT YET IMPLEMENTED) +### 4. Transcription and diarization -Standalone uses `TRANSCRIPT_BACKEND=whisper` for local CPU-based transcription. Diarization is disabled. +Standalone runs the self-hosted ML service (`gpu/self_hosted/`) in a CPU-only Docker container named `cpu`. This is the same FastAPI service used for Modal.com GPU deployments, but built with `Dockerfile.cpu` (no NVIDIA CUDA dependencies). The compose service is named `cpu` (not `gpu`) to make clear it runs without GPU acceleration; the source code lives in `gpu/self_hosted/` because it's shared with the GPU deployment. -> Another developer is working on optimizing the local transcription experience. For now, local Whisper works for short recordings but is slow on CPU. +The `modal` backend name is reused — it just means "HTTP API client". Setting `TRANSCRIPT_URL` / `DIARIZATION_URL` to `http://cpu:8000` routes requests to the local container instead of Modal.com. + +On first start, the service downloads pyannote speaker diarization models (~1GB) from a public S3 bundle. Models are cached in a Docker volume (`gpu_cache`) so subsequent starts are fast. No HuggingFace token or API key needed. + +> **Performance**: CPU-only transcription and diarization work but are slow (~15 min for a 3 min file). For faster processing on Linux with NVIDIA GPU, use `--profile gpu-nvidia` instead (see `docker-compose.standalone.yml`). ### 5. Docker services ```bash -docker compose up -d postgres redis garage server worker beat web +docker compose up -d postgres redis garage cpu server worker beat web ``` -All services start in a single command. Garage is already started by step 3 but is included for idempotency. No Hatchet in standalone mode — LLM processing (summaries, topics, titles) runs via Celery tasks. +All services start in a single command. Garage and `cpu` are already started by earlier steps but included for idempotency. No Hatchet in standalone mode — LLM processing (summaries, topics, titles) runs via Celery tasks. ### 6. Database migrations @@ -101,6 +107,7 @@ Run automatically by the `server` container on startup (`runserver.sh` calls `al ### 7. Health check Verifies: +- CPU service responds (transcription + diarization ready) - Server responds at `http://localhost:1250/health` - Frontend serves at `http://localhost:3000` - LLM endpoint reachable from inside containers @@ -114,9 +121,42 @@ Verifies: | `postgres` | 5432 | PostgreSQL database | | `redis` | 6379 | Cache + Celery broker | | `garage` | 3900, 3903 | S3-compatible object storage (S3 API + admin API) | +| `cpu` | — | Self-hosted transcription + diarization (CPU-only) | | `worker` | — | Celery worker (live pipeline post-processing) | | `beat` | — | Celery beat (scheduled tasks) | +## Testing programmatically + +After the setup script completes, verify the full pipeline (upload, transcription, diarization, LLM summary) via the API: + +```bash +# 1. Create a transcript +TRANSCRIPT_ID=$(curl -s -X POST 'http://localhost:1250/v1/transcripts' \ + -H 'Content-Type: application/json' \ + -d '{"name":"test-upload"}' | python3 -c "import sys,json; print(json.load(sys.stdin)['id'])") +echo "Created: $TRANSCRIPT_ID" + +# 2. Upload an audio file (single-chunk upload) +curl -s "http://localhost:1250/v1/transcripts/${TRANSCRIPT_ID}/record/upload?chunk_number=0&total_chunks=1" \ + -X POST -F "chunk=@/path/to/audio.mp3" + +# 3. Poll until processing completes (status: ended or error) +while true; do + STATUS=$(curl -s "http://localhost:1250/v1/transcripts/${TRANSCRIPT_ID}" \ + | python3 -c "import sys,json; print(json.load(sys.stdin)['status'])") + echo "Status: $STATUS" + case "$STATUS" in ended|error) break;; esac + sleep 10 +done + +# 4. Check the result +curl -s "http://localhost:1250/v1/transcripts/${TRANSCRIPT_ID}" | python3 -m json.tool +``` + +Expected result: status `ended`, auto-generated `title`, `short_summary`, `long_summary`, and `transcript` text with `Speaker 0` / `Speaker 1` labels. + +CPU-only processing is slow (~15 min for a 3 min audio file). Diarization finishes in ~3 min, transcription takes the rest. + ## Troubleshooting ### Port conflicts (most common issue) @@ -158,9 +198,11 @@ These require external accounts and infrastructure that can't be scripted: ## Current status +All steps implemented. The setup script handles everything end-to-end: + - Step 1 (Ollama/LLM) — implemented - Step 2 (environment files) — implemented -- Step 3 (object storage / Garage) — implemented (`docker-compose.standalone.yml` + `setup-standalone.sh`) -- Step 4 (transcription/diarization) — in progress by another developer +- Step 3 (object storage / Garage) — implemented +- Step 4 (transcription/diarization) — implemented (self-hosted GPU service) - Steps 5-7 (Docker, migrations, health) — implemented - **Unified script**: `scripts/setup-standalone.sh` diff --git a/gpu/self_hosted/Dockerfile.cpu b/gpu/self_hosted/Dockerfile.cpu new file mode 100644 index 00000000..7e02ac5c --- /dev/null +++ b/gpu/self_hosted/Dockerfile.cpu @@ -0,0 +1,39 @@ +FROM python:3.12-slim + +ENV PYTHONUNBUFFERED=1 \ + UV_LINK_MODE=copy \ + UV_NO_CACHE=1 + +WORKDIR /tmp +RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \ + --mount=type=cache,target=/var/lib/apt,sharing=locked \ + apt-get update \ + && apt-get install -y \ + ffmpeg \ + curl \ + ca-certificates \ + gnupg \ + wget +ADD https://astral.sh/uv/install.sh /uv-installer.sh +RUN sh /uv-installer.sh && rm /uv-installer.sh +ENV PATH="/root/.local/bin/:$PATH" + +RUN mkdir -p /app +WORKDIR /app +COPY pyproject.toml uv.lock /app/ + + +COPY ./app /app/app +COPY ./main.py /app/ +COPY ./runserver.sh /app/ + +# prevent uv failing with too many open files on big cpus +ENV UV_CONCURRENT_INSTALLS=16 + +# first install +RUN --mount=type=cache,target=/root/.cache/uv \ + uv sync --compile-bytecode --locked + +EXPOSE 8000 + +CMD ["sh", "/app/runserver.sh"] diff --git a/gpu/self_hosted/app/auth.py b/gpu/self_hosted/app/auth.py index 9c74e90c..02699f1b 100644 --- a/gpu/self_hosted/app/auth.py +++ b/gpu/self_hosted/app/auth.py @@ -3,14 +3,14 @@ import os from fastapi import Depends, HTTPException, status from fastapi.security import OAuth2PasswordBearer -oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token") +oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token", auto_error=False) -def apikey_auth(apikey: str = Depends(oauth2_scheme)): +def apikey_auth(apikey: str | None = Depends(oauth2_scheme)): required_key = os.environ.get("REFLECTOR_GPU_APIKEY") if not required_key: return - if apikey == required_key: + if apikey and apikey == required_key: return raise HTTPException( status_code=status.HTTP_401_UNAUTHORIZED, diff --git a/gpu/self_hosted/app/services/diarizer.py b/gpu/self_hosted/app/services/diarizer.py index 2935ffc5..8b5a2086 100644 --- a/gpu/self_hosted/app/services/diarizer.py +++ b/gpu/self_hosted/app/services/diarizer.py @@ -1,10 +1,65 @@ +import logging import os +import tarfile import threading +from pathlib import Path +from urllib.request import urlopen import torch import torchaudio +import yaml from pyannote.audio import Pipeline +logger = logging.getLogger(__name__) + +S3_BUNDLE_URL = "https://reflector-public.s3.us-east-1.amazonaws.com/pyannote-speaker-diarization-3.1.tar.gz" +BUNDLE_CACHE_DIR = Path("/root/.cache/pyannote-bundle") + + +def _ensure_model(cache_dir: Path) -> str: + """Download and extract S3 model bundle if not cached.""" + model_dir = cache_dir / "pyannote-speaker-diarization-3.1" + config_path = model_dir / "config.yaml" + + if config_path.exists(): + logger.info("Using cached model bundle at %s", model_dir) + return str(model_dir) + + cache_dir.mkdir(parents=True, exist_ok=True) + tarball_path = cache_dir / "model.tar.gz" + + logger.info("Downloading model bundle from %s", S3_BUNDLE_URL) + with urlopen(S3_BUNDLE_URL) as response, open(tarball_path, "wb") as f: + while chunk := response.read(8192): + f.write(chunk) + + logger.info("Extracting model bundle") + with tarfile.open(tarball_path, "r:gz") as tar: + tar.extractall(path=cache_dir, filter="data") + tarball_path.unlink() + + _patch_config(model_dir, cache_dir) + return str(model_dir) + + +def _patch_config(model_dir: Path, cache_dir: Path) -> None: + """Rewrite config.yaml to reference local pytorch_model.bin paths.""" + config_path = model_dir / "config.yaml" + with open(config_path) as f: + config = yaml.safe_load(f) + + config["pipeline"]["params"]["segmentation"] = str( + cache_dir / "pyannote-segmentation-3.0" / "pytorch_model.bin" + ) + config["pipeline"]["params"]["embedding"] = str( + cache_dir / "pyannote-wespeaker-voxceleb-resnet34-LM" / "pytorch_model.bin" + ) + + with open(config_path, "w") as f: + yaml.dump(config, f) + + logger.info("Patched config.yaml with local model paths") + class PyannoteDiarizationService: def __init__(self): @@ -14,10 +69,20 @@ class PyannoteDiarizationService: def load(self): self._device = "cuda" if torch.cuda.is_available() else "cpu" - self._pipeline = Pipeline.from_pretrained( - "pyannote/speaker-diarization-3.1", - use_auth_token=os.environ.get("HF_TOKEN"), - ) + hf_token = os.environ.get("HF_TOKEN") + + if hf_token: + logger.info("Loading pyannote model from HuggingFace (HF_TOKEN set)") + self._pipeline = Pipeline.from_pretrained( + "pyannote/speaker-diarization-3.1", + use_auth_token=hf_token, + ) + else: + logger.info("HF_TOKEN not set — loading model from S3 bundle") + model_path = _ensure_model(BUNDLE_CACHE_DIR) + config_path = Path(model_path) / "config.yaml" + self._pipeline = Pipeline.from_pretrained(str(config_path)) + self._pipeline.to(torch.device(self._device)) def diarize_file(self, file_path: str, timestamp: float = 0.0) -> dict: diff --git a/scripts/setup-standalone.sh b/scripts/setup-standalone.sh index 82ef408a..790339f6 100755 --- a/scripts/setup-standalone.sh +++ b/scripts/setup-standalone.sh @@ -184,8 +184,10 @@ ENVEOF env_set "$SERVER_ENV" "CELERY_BROKER_URL" "redis://redis:6379/1" env_set "$SERVER_ENV" "CELERY_RESULT_BACKEND" "redis://redis:6379/1" env_set "$SERVER_ENV" "AUTH_BACKEND" "none" - env_set "$SERVER_ENV" "TRANSCRIPT_BACKEND" "whisper" - env_set "$SERVER_ENV" "DIARIZATION_ENABLED" "false" + env_set "$SERVER_ENV" "PUBLIC_MODE" "true" + # TRANSCRIPT_BACKEND, TRANSCRIPT_URL, DIARIZATION_BACKEND, DIARIZATION_URL + # are set via docker-compose.standalone.yml `environment:` overrides — not written here + # so we don't clobber the user's server/.env for non-standalone use. env_set "$SERVER_ENV" "TRANSLATION_BACKEND" "passthrough" env_set "$SERVER_ENV" "LLM_URL" "$LLM_URL_VALUE" env_set "$SERVER_ENV" "LLM_MODEL" "$MODEL" @@ -305,7 +307,7 @@ step_services() { fi # server runs alembic migrations on startup automatically (see runserver.sh) - compose_cmd up -d postgres redis garage server worker beat web + compose_cmd up -d postgres redis garage cpu server worker beat web ok "Containers started" info "Server is running migrations (alembic upgrade head)..." } @@ -316,6 +318,26 @@ step_services() { step_health() { info "Step 6: Health checks" + # CPU service may take a while on first start (model download + load). + # No host port exposed — check via docker exec. + info "Waiting for CPU service (first start downloads ~1GB of models)..." + local cpu_ok=false + for i in $(seq 1 120); do + if compose_cmd exec -T cpu curl -sf http://localhost:8000/docs > /dev/null 2>&1; then + cpu_ok=true + break + fi + echo -ne "\r Waiting for CPU service... ($i/120)" + sleep 5 + done + echo "" + if [[ "$cpu_ok" == "true" ]]; then + ok "CPU service healthy (transcription + diarization)" + else + warn "CPU service not ready yet — it will keep loading in the background" + warn "Check with: docker compose logs cpu" + fi + wait_for_url "http://localhost:1250/health" "Server API" 60 3 echo "" ok "Server API healthy" @@ -356,6 +378,10 @@ main() { LLM_URL_VALUE="" OLLAMA_PROFILE="" + # docker-compose.yml may reference env_files that don't exist yet; + # touch them so compose_cmd works before the steps that populate them. + touch "$SERVER_ENV" "$WWW_ENV" + step_llm echo "" step_server_env diff --git a/server/pyproject.toml b/server/pyproject.toml index 1cbb5320..c2213a5f 100644 --- a/server/pyproject.toml +++ b/server/pyproject.toml @@ -68,7 +68,6 @@ evaluation = [ "pydantic>=2.1.1", ] local = [ - "pyannote-audio>=3.3.2", "faster-whisper>=0.10.0", ] silero-vad = [ diff --git a/server/reflector/db/search.py b/server/reflector/db/search.py index 5d9bc507..d61bb27f 100644 --- a/server/reflector/db/search.py +++ b/server/reflector/db/search.py @@ -26,6 +26,7 @@ from reflector.db.rooms import rooms from reflector.db.transcripts import SourceKind, TranscriptStatus, transcripts from reflector.db.utils import is_postgresql from reflector.logger import logger +from reflector.settings import settings from reflector.utils.string import NonEmptyString, try_parse_non_empty_string DEFAULT_SEARCH_LIMIT = 20 @@ -396,7 +397,7 @@ class SearchController: transcripts.c.user_id == params.user_id, rooms.c.is_shared ) ) - else: + elif not settings.PUBLIC_MODE: base_query = base_query.where(rooms.c.is_shared) if params.room_id: base_query = base_query.where(transcripts.c.room_id == params.room_id) diff --git a/server/reflector/db/transcripts.py b/server/reflector/db/transcripts.py index b3f6d49c..f3f9a410 100644 --- a/server/reflector/db/transcripts.py +++ b/server/reflector/db/transcripts.py @@ -406,7 +406,7 @@ class TranscriptController: query = query.where( or_(transcripts.c.user_id == user_id, rooms.c.is_shared) ) - else: + elif not settings.PUBLIC_MODE: query = query.where(rooms.c.is_shared) if source_kind: diff --git a/server/reflector/processors/audio_diarization_pyannote.py b/server/reflector/processors/audio_diarization_pyannote.py deleted file mode 100644 index 5778a732..00000000 --- a/server/reflector/processors/audio_diarization_pyannote.py +++ /dev/null @@ -1,74 +0,0 @@ -import os - -import torch -import torchaudio -from pyannote.audio import Pipeline - -from reflector.processors.audio_diarization import AudioDiarizationProcessor -from reflector.processors.audio_diarization_auto import AudioDiarizationAutoProcessor -from reflector.processors.types import AudioDiarizationInput, DiarizationSegment - - -class AudioDiarizationPyannoteProcessor(AudioDiarizationProcessor): - """Local diarization processor using pyannote.audio library""" - - def __init__( - self, - model_name: str = "pyannote/speaker-diarization-3.1", - pyannote_auth_token: str | None = None, - device: str | None = None, - **kwargs, - ): - super().__init__(**kwargs) - self.model_name = model_name - self.auth_token = pyannote_auth_token or os.environ.get("HF_TOKEN") - self.device = device - - if device is None: - self.device = "cuda" if torch.cuda.is_available() else "cpu" - - self.logger.info(f"Loading pyannote diarization model: {self.model_name}") - self.diarization_pipeline = Pipeline.from_pretrained( - self.model_name, use_auth_token=self.auth_token - ) - self.diarization_pipeline.to(torch.device(self.device)) - self.logger.info(f"Diarization model loaded on device: {self.device}") - - async def _diarize(self, data: AudioDiarizationInput) -> list[DiarizationSegment]: - try: - # Load audio file (audio_url is assumed to be a local file path) - self.logger.info(f"Loading local audio file: {data.audio_url}") - waveform, sample_rate = torchaudio.load(data.audio_url) - audio_input = {"waveform": waveform, "sample_rate": sample_rate} - self.logger.info("Running speaker diarization") - diarization = self.diarization_pipeline(audio_input) - - # Convert pyannote diarization output to our format - segments = [] - for segment, _, speaker in diarization.itertracks(yield_label=True): - # Extract speaker number from label (e.g., "SPEAKER_00" -> 0) - speaker_id = 0 - if speaker.startswith("SPEAKER_"): - try: - speaker_id = int(speaker.split("_")[-1]) - except (ValueError, IndexError): - # Fallback to hash-based ID if parsing fails - speaker_id = hash(speaker) % 1000 - - segments.append( - { - "start": round(segment.start, 3), - "end": round(segment.end, 3), - "speaker": speaker_id, - } - ) - - self.logger.info(f"Diarization completed with {len(segments)} segments") - return segments - - except Exception as e: - self.logger.exception(f"Diarization failed: {e}") - raise - - -AudioDiarizationAutoProcessor.register("pyannote", AudioDiarizationPyannoteProcessor) diff --git a/server/reflector/settings.py b/server/reflector/settings.py index 099792cc..805239bc 100644 --- a/server/reflector/settings.py +++ b/server/reflector/settings.py @@ -85,9 +85,7 @@ class Settings(BaseSettings): ) # Diarization - # backends: - # - pyannote: in-process model loading (no HTTP, runs in same process) - # - modal: HTTP API client (works with Modal.com OR self-hosted gpu/self_hosted/) + # backend: modal — HTTP API client (works with Modal.com OR self-hosted gpu/self_hosted/) DIARIZATION_ENABLED: bool = True DIARIZATION_BACKEND: str = "modal" DIARIZATION_URL: str | None = None @@ -96,9 +94,6 @@ class Settings(BaseSettings): # Diarization: modal backend DIARIZATION_MODAL_API_KEY: str | None = None - # Diarization: local pyannote.audio - DIARIZATION_PYANNOTE_AUTH_TOKEN: str | None = None - # Audio Padding (Modal.com backend) PADDING_URL: str | None = None PADDING_MODAL_API_KEY: str | None = None diff --git a/server/uv.lock b/server/uv.lock index ac2849f1..3735f66c 100644 --- a/server/uv.lock +++ b/server/uv.lock @@ -235,12 +235,6 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 }, ] -[[package]] -name = "antlr4-python3-runtime" -version = "4.9.3" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b", size = 117034 } - 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