Migrate project from Poetry to uv and consolidate lint tooling

.github/workflows/ci.yml

@@ -0,0 +1,46 @@
+name: CI
+
+"on":
+  pull_request:
+  push:
+    branches:
+      - main
+  workflow_dispatch:
+
+concurrency:
+  group: ci-${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+permissions:
+  contents: read
+
+jobs:
+  validate:
+    name: Lint, type-check, and tests
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+
+      - name: Set up uv
+        uses: astral-sh/setup-uv@v5
+        with:
+          enable-cache: true
+
+      - name: Sync dependencies
+        run: uv sync --group dev
+
+      - name: Ruff
+        run: uv run ruff check .
+
+      - name: MyPy
+        run: uv run mypy src tests main.py h100_server.py
+
+      - name: Pytest
+        run: uv run pytest -q

README.md

@@ -6,13 +6,15 @@ Subtitle generation tools for live and archived streams. LiveVTT focuses on prod
 
 ### Requirements
 - Python 3.10+
+- `uv` CLI: used to sync and install dependencies.
+  - Install via `pip install uv` for Python environments.
+  - Or use `brew install uv` on macOS / Linux if available.
+  - See the official `uv` project page for more installation options: https://uv.run
 - FFmpeg + FFprobe: FFprobe is included with FFmpeg. Download from the [official FFmpeg site](https://ffmpeg.org/download.html). Install via `apt install ffmpeg` (Linux), `brew install ffmpeg` (macOS), or `choco install ffmpeg` (Windows).
 
 ### Install dependencies
 ```bash
-python -m venv .venv
-. .venv/bin/activate
-pip install -r requirements.txt
+uv sync
 ```
 
 ### Transcribe archived media

docs/CLOUD_GPU_SETUP.md

@@ -77,7 +77,7 @@ On your current server with the video archive:
 
 ```bash
 # Install new dependencies
-poetry install
+uv sync --no-dev
 
 # Test connection to cloud GPU
 curl http://YOUR_CLOUD_GPU_IP:8000/health
@@ -87,7 +87,7 @@ curl http://YOUR_CLOUD_GPU_IP:8000/health
 ### Step 3: Run Archive Transcription
 
 ```bash
-poetry run python src/python/tools/archive_transcriber_remote.py \
+uv run python src/python/tools/archive_transcriber_remote.py \
   --remote-url http://YOUR_CLOUD_GPU_IP:8000 \
   --workers 4 \
   --progress \

h100_server.py

@@ -11,9 +11,8 @@
 import tempfile
 from typing import Any, Iterable, Optional, Protocol, cast
 
-from flask import Flask, jsonify, request  # type: ignore
 from faster_whisper import WhisperModel  # type: ignore
-
+from flask import Flask, jsonify, request  # type: ignore
 
 app: Flask = Flask(__name__)  # type: ignore
 
@@ -46,9 +45,7 @@ def get_model(model_name: str = "large-v3-turbo") -> WhisperModel:
     """Get or load a Whisper model"""
     if model_name not in MODELS:
         print(f"Loading model: {model_name}", flush=True)
-        MODELS[model_name] = WhisperModel(
-            model_name, device="cuda", compute_type="float16"
-        )
+        MODELS[model_name] = WhisperModel(model_name, device="cuda", compute_type="float16")
         print(f"Model loaded: {model_name}", flush=True)
     return MODELS[model_name]
 
@@ -59,9 +56,7 @@ def parse_beam_size(value: Any) -> int:
     except (TypeError, ValueError) as exc:
         raise ValueError("beam_size must be an integer") from exc
     if beam_size < MIN_BEAM_SIZE or beam_size > MAX_BEAM_SIZE:
-        raise ValueError(
-            f"beam_size must be between {MIN_BEAM_SIZE} and {MAX_BEAM_SIZE}"
-        )
+        raise ValueError(f"beam_size must be between {MIN_BEAM_SIZE} and {MAX_BEAM_SIZE}")
     return beam_size