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SnapOtterandGitHub
6e3a14ec6b
* fix: remove all automatic third-party egress (OSM tiles, Scalar fonts, editor Google Fonts, AI model download fallbacks) Phone-home audit follow-up. The product no longer makes any automatic third-party request; user-initiated click-outs stay, and production now fails closed on missing AI models. 1. GPS leak via OSM tiles: the strip-metadata panel auto-loaded tile.openstreetmap.org tiles encoding the photo's GPS position. The Leaflet mini-map is gone; coordinates render as text plus an explicit View on map link (openstreetmap.org, opens on click only). Removed tile.openstreetmap.org from the CSP img-src, dropped the leaflet dependency, added the viewOnMap i18n key to all 21 locales. 2. Scalar docs fonts: /api/docs loaded Inter and JetBrains Mono from fonts.scalar.com. Scalar now renders with withDefaultFonts: false and both --scalar-font and --scalar-font-code pinned to system stacks; fonts.scalar.com removed from the docs CSP font-src. Verified by injecting GET /api/docs/: config carries withDefaultFonts false and the served page has no fonts.scalar.com reference. 3. Editor Google Fonts: the editor font picker built fonts.googleapis.com stylesheet URLs for 25 web fonts the served CSP already blocked. The remote loading path is deleted; the picker now offers system fonts only, with a SELF_HOSTED_FONTS seam (FontFace API, same origin) for bundling fonts later. Unknown families saved in old documents fall back to the browser default. 4. Python sidecar fails closed on model downloads: new packages/ai/python/offline_guard.py gates every runtime download fallback (inpaint, outpaint, restore, noise_removal, detect_faces, enhance_faces, face_landmarks, red_eye_removal, remove_bg, ocr, transcribe, upscale) behind SNAPOTTER_ALLOW_MODEL_DOWNLOAD=1 with an actionable error. Bundled models keep working untouched. 5. OCR and transcription library-internal downloads: unbundled PaddleOCR language and detection fallbacks now raise the guard error naming the language instead of resolving models over the network; faster-whisper gets local_files_only when downloads are off. 6. GFPGAN and CodeFormer cwd-relative weights: facexlib and codeformer-pip resolve helper weights relative to the process cwd and fetch them from GitHub when absent. They are now symlinked from the installed bundle files under MODELS_PATH/gfpgan/facelib before the libraries load, failing closed when unresolvable. Defense in depth: HF_HUB_OFFLINE=1 and TRANSFORMERS_OFFLINE=1 are set in the runtime image and in the sidecar spawn env; install_feature.py lifts them for user-initiated bundle installs and restores them afterwards (it can run in-process inside the dispatcher). SNAPOTTER_ALLOW_MODEL_DOWNLOAD is documented in .env.example, default off. Validation: typecheck 9/9 workspaces, Biome clean on touched files, 5178 unit tests pass, py_compile on all touched scripts, guard behavior exercised in both dispatcher exec and per-request import modes, zero remaining runtime references to the three hosts. Docker build and live AI inference need post-merge verification on the GPU host. Claude-Session: https://claude.ai/code/session_01XGB4pGvTvb7sUX4JN745U7 * fix: allow AI model downloads by default, make strict offline mode opt-in Product call: ease of use first. The download gating from the previous commit inverts its default: runtime model fetches (public model weights only, never user data) are allowed out of the box so AI tools self-heal, and SNAPOTTER_ALLOW_MODEL_DOWNLOAD=0 becomes the explicit strict offline mode for airgapped deployments, where every fallback raises the actionable error instead of fetching. Changes: offline_guard blocks only on an explicit 0/false; the unconditional HF_HUB_OFFLINE/TRANSFORMERS_OFFLINE image ENV is removed and bridge.ts sets those flags for the sidecar only in strict mode; .env.example documents the new default; install_feature's lift/restore stays. All bundled-path preferences, pre-existence checks, and symlink pre-placement remain, so installed bundles never trigger a download. The OSM, Scalar font, and editor font fixes are unchanged. Validation rerun: typecheck 9/9, Biome clean on touched files, 5178 unit tests pass, py_compile on touched scripts, guard behavior verified for unset/1 (allowed) and 0/false (blocked with the new message). Claude-Session: https://claude.ai/code/session_01XGB4pGvTvb7sUX4JN745U7
98 lines
2.9 KiB
Python
98 lines
2.9 KiB
Python
"""Speech-to-text transcription using faster-whisper (CTranslate2)."""
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import sys
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import json
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import os
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from gpu import gpu_available
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MODELS_PATH = os.environ.get(
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"MODELS_PATH",
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os.path.join(os.environ.get("DATA_DIR", "/data"), "ai", "models"),
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)
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def emit_progress(percent, stage):
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"""Emit structured progress to stderr for bridge.ts to capture."""
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print(json.dumps({"progress": percent, "stage": stage}), file=sys.stderr, flush=True)
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def main():
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input_path = sys.argv[1]
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settings = json.loads(sys.argv[2]) if len(sys.argv) > 2 else {}
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language = settings.get("language", "auto")
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# "task" is accepted for forward-compat but only "transcribe" is used today
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_task = settings.get("task", "transcribe")
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try:
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emit_progress(5, "Loading model")
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# Lazy import -- faster_whisper is only available when the
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# transcription bundle is installed; keeping it lazy lets
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# py_compile succeed without the dependency.
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from faster_whisper import WhisperModel
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model_dir = os.path.join(MODELS_PATH, "faster-whisper-small")
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# When the bundled model dir is absent, faster-whisper treats the
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# argument as a Hugging Face repo id and downloads it; strict offline
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# mode blocks that fallback with a clear error.
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from offline_guard import downloads_allowed, ensure_download_allowed
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if not os.path.isdir(model_dir):
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ensure_download_allowed("Whisper transcription model (faster-whisper-small)")
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if gpu_available():
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device, compute_type = "cuda", "float16"
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else:
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device, compute_type = "cpu", "int8"
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model = WhisperModel(
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model_dir,
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device=device,
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compute_type=compute_type,
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local_files_only=not downloads_allowed(),
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)
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emit_progress(20, "Transcribing")
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lang_arg = None if language == "auto" else language
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segments_iter, info = model.transcribe(
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input_path,
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language=lang_arg,
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vad_filter=True,
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)
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detected_language = info.language if info else (language if language != "auto" else "en")
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segments = []
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batch_count = 0
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for seg in segments_iter:
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segments.append({
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"start": round(seg.start, 3),
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"end": round(seg.end, 3),
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"text": seg.text.strip(),
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})
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batch_count += 1
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if batch_count % 5 == 0:
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emit_progress(min(20 + batch_count, 90), "Transcribing")
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emit_progress(95, "Done")
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full_text = " ".join(s["text"] for s in segments)
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print(json.dumps({
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"success": True,
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"language": detected_language,
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"segments": segments,
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"text": full_text,
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}))
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except Exception as e:
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print(json.dumps({"error": str(e)}))
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sys.exit(1)
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if __name__ == "__main__":
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main()
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