feat: add local pyannote file diarization processor (#858)

* 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>

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:

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`

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"]

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,

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"
+        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=os.environ.get("HF_TOKEN"),
+                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:

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

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 = [

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)

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:

server/reflector/processors/audio_diarization_pyannote.py

@@ -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)

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

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 }
-
 [[package]]
 name = "anyio"
 version = "4.9.0"
@@ -267,21 +261,6 @@ wheels = [
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