gauntlet: fix lint — drop unused subprocess/shutil/tempfile/Path imports

Leftover ffmpeg-era imports from the earlier draft of the CPU transcribe smoke test; the final numpy-array-bypass version doesn't need them. Ruff autofix.
deploy/tower + claim race + transcribe smoke tests
2026-05-25 10:34:54 -04:00 · 2026-05-25 10:20:46 -04:00 · 2026-05-25 10:11:37 -04:00
15 changed files with 1545 additions and 499 deletions
--- a/alembic/versions/c8f1d9e34b7a_v3_source_claim_columns.py
+++ b/alembic/versions/c8f1d9e34b7a_v3_source_claim_columns.py
@ -0,0 +1,50 @@
 """v3 second_brain: sources.claimed_by / claimed_at
 Revision ID: c8f1d9e34b7a
 Revises: 7b3e8a5c1f29
 Create Date: 2026-05-25 12:30:00.000000
 Adds a lightweight claim mechanism so the tower and dev sides never grab
 the same row off the queue. The claim flow used by both workers is:
    BEGIN;
    SELECT id, status, source_type
      FROM second_brain.sources
     WHERE <filter>
       AND claimed_by IS NULL
     ORDER BY ingested_at ASC
     LIMIT 1
     FOR UPDATE SKIP LOCKED;
    UPDATE second_brain.sources
       SET claimed_by = :worker, claimed_at = now() AT TIME ZONE 'UTC'
     WHERE id = :id;
    COMMIT;
 `FOR UPDATE SKIP LOCKED` is the actual race-safety primitive — the column
 pair is observability (who has it, since when, for stale-claim recovery).
 A partial index would help once we have a real queue depth; deliberately
 not added now per Travis's "no gold-plating".
 """
 from __future__ import annotations
 from collections.abc import Sequence
 from alembic import op
 revision: str = "c8f1d9e34b7a"
 down_revision: str | Sequence[str] | None = "7b3e8a5c1f29"
 branch_labels: str | Sequence[str] | None = None
 depends_on: str | Sequence[str] | None = None
 def upgrade() -> None:
    op.execute("SET search_path TO second_brain")
    op.execute("ALTER TABLE sources ADD COLUMN claimed_by VARCHAR(100)")
    op.execute("ALTER TABLE sources ADD COLUMN claimed_at TIMESTAMP")
 def downgrade() -> None:
    op.execute("SET search_path TO second_brain")
    op.execute("ALTER TABLE sources DROP COLUMN IF EXISTS claimed_at")
    op.execute("ALTER TABLE sources DROP COLUMN IF EXISTS claimed_by")
--- a/deploy/tower/README.md
+++ b/deploy/tower/README.md
@ -0,0 +1,167 @@
 # Tower-side transcribe worker — deploy notes
 Target host: **EndeavourOS tower** (Arch base), RTX 3080. Runs the
 `second-brain transcribe-worker` daemon under systemd. Reaches the
 petalbrain Postgres over WireGuard at `db.wg.herbylab.dev:5432`.
 This worker only does **pull + transcribe** for VIDEO sources. Articles
 and all extraction/embedding stay on the dev side.
 ---
 ## 1. System packages
 ```bash
 # Build / runtime tooling.
 sudo pacman -S --needed base-devel git ffmpeg python uv
 # NVIDIA — driver, CUDA, cuDNN. faster-whisper / CTranslate2 needs cuDNN
 # at runtime; missing-symbol errors at first transcribe are usually a
 # cuDNN install gap. Use whichever driver/cuda channel matches your
 # Arch derivative; on stock EndeavourOS the AUR/community packages work:
 sudo pacman -S --needed nvidia nvidia-utils cuda cudnn
 # Confirm the GPU is visible.
 nvidia-smi
 ```
 yt-dlp comes in via the Python project (`uv sync` step below), so the
 distro yt-dlp is *optional*. Leave it off the host unless you want it on
 your CLI PATH.
 ---
 ## 2. Project checkout + venv
 ```bash
 sudo mkdir -p /opt/projects/homelab
 sudo chown herbyadmin:herbyadmin /opt/projects/homelab
 cd /opt/projects/homelab
 git clone gitea-lovebug:petal-power/second-brain.git
 cd second-brain
 # Install the runtime deps + the `tower` extra (faster-whisper / CTranslate2).
 # The dev side does NOT install --extra tower; only the tower needs it.
 uv sync --extra tower
 ```
 `faster-whisper` will pull a couple hundred MB of CUDA wheels. First-run
 model download (~3 GB for large-v3) lands under `~/.cache/huggingface/`
 the first time the worker transcribes; pre-warm it if you want:
 ```bash
 uv run python -c "from faster_whisper import WhisperModel; WhisperModel('large-v3', device='cuda', compute_type='float16')"
 ```
 ---
 ## 3. WireGuard
 The worker assumes the tunnel is already configured and that
 `wg-quick@wg-lan.service` brings it up at boot. Topology:
 | Peer | WG IP |
 |------|-------|
 | hub (herbydev) | `10.99.0.1` |
 | tower | `10.99.0.2` |
 `db.wg.herbylab.dev` resolves to `10.99.0.1`. Confirm connectivity from
 the tower:
 ```bash
 ping -c2 db.wg.herbylab.dev
 nc -vz db.wg.herbylab.dev 5432
 ```
 Tunnel setup itself is **out of scope for this README** — that's
 configured separately as part of Travis's infra.
 ---
 ## 4. EnvironmentFile
 ```bash
 sudo install -m 0600 -o root -g root \
    deploy/tower/second-brain-transcribe.env.example \
    /etc/default/second-brain-transcribe
 sudo $EDITOR /etc/default/second-brain-transcribe   # paste the lovebug password
 ```
 The lovebug password lives in `/opt/backups/postgres-consolidation/credentials.env`
 on the dev host. Copy it across through whatever channel you trust
 (personal Bitwarden, scp over WG, etc.) — do not commit it.
 ---
 ## 5. systemd unit
 ```bash
 sudo install -m 0644 deploy/tower/second-brain-transcribe.service \
    /etc/systemd/system/second-brain-transcribe.service
 sudo systemctl daemon-reload
 sudo systemctl enable --now second-brain-transcribe.service
 ```
 The unit declares `After=` + `Wants=` on `wg-quick@wg-lan.service`, so
 the worker starts after the tunnel comes up at boot. If the tunnel
 disappears mid-run the worker keeps polling and logs DB-unreachable
 warnings; it doesn't crash-loop.
 ### Day-to-day commands
 ```bash
 # What's it doing right now?
 systemctl status second-brain-transcribe.service
 journalctl -u second-brain-transcribe.service -f
 # Pause without uninstalling — toggle from the dashboard at
 # https://brain.herbylab.dev/settings  (or whichever host).
 # Or stop the unit:
 sudo systemctl stop second-brain-transcribe.service
 # Pick up code updates:
 cd /opt/projects/homelab/second-brain
 git pull
 uv sync --extra tower
 sudo systemctl restart second-brain-transcribe.service
 ```
 ---
 ## 6. Validation — what to confirm once WG is live
 These can only be verified on the tower itself:
 1. **WG reachability.** `nc -vz db.wg.herbylab.dev 5432` succeeds.
 2. **DB auth.** `uv run alembic current` returns the latest revision id.
 3. **GPU large-v3 path.** With a short test video queued via
   `second-brain add <url>` on the dev side, run `uv run second-brain transcribe-worker --once`
   on the tower. Watch `journalctl` — first run pulls the model from
   HuggingFace (slow), subsequent runs are fast. The source row should
   end up in `TRANSCRIBED` with `transcript_text` populated and
   `claimed_by` cleared.
 4. **Race safety smoke test.** Run two `--once` invocations in parallel
   against an empty queue, then with one PENDING video. Only one should
   claim it; the other should see no work.
 5. **Settings round-trip.** Flip `transcription_enabled` off on the
   dashboard; within one poll cycle (~15s) the worker logs
   `transcription_enabled=false — sleeping`. Flip back on, the next
   PENDING video gets picked up.
 ---
 ## Follow-ups (out of scope for this round)
 - **Media + subtitles directory currently lives on the tower's local
  disk** (under `~/.local/share/second-brain/`). Move to a NAS mount
  later so the dev side can compile/inspect the SRTs without a
  cross-machine fetch. Open question on the right mount point + cred
  flow — see the project's `postgres-migration-planning.md` for the
  related vault-location decision Travis still owes.
 - **Tower-side observability.** No metrics endpoint yet; journald is
  the only signal. A `/api/worker-status` heartbeat the dashboard could
  read would be nice once two workers exist.
 - **GPU concurrency > 1.** `transcription_max_concurrent_gpu_jobs`
  exists in `pipeline_settings` but the worker only runs one job at a
  time. Scale-out would need a per-job semaphore (or just running N
  worker instances with distinct `SECOND_BRAIN_WORKER_ID`s).
--- a/deploy/tower/second-brain-transcribe.env.example
+++ b/deploy/tower/second-brain-transcribe.env.example
@ -0,0 +1,24 @@
 # /etc/default/second-brain-transcribe
 #
 # Copy to /etc/default/second-brain-transcribe, chmod 0600, chown root:root
 # (systemd reads it before dropping to User=herbyadmin so a non-readable
 # permissions mode is fine — the herbyadmin user never touches this file).
 # REQUIRED — petalbrain Postgres reached over the WireGuard tunnel.
 # Hub-side hostname is db.wg.herbylab.dev (10.99.0.1); the tower is 10.99.0.2.
 # No TLS — WireGuard already encrypts, and lovebug doesn't require SSL.
 SECOND_BRAIN_DATABASE_URL=postgresql+psycopg://lovebug:REPLACE_ME@db.wg.herbylab.dev:5432/petalbrain
 # OPTIONAL — only set if the tower has a non-standard whisper layout.
 # Defaults from settings.toml: model=large-v3, device=cuda, compute_type=float16.
 # WHISPER_MODEL=large-v3
 # WHISPER_DEVICE=cuda
 # WHISPER_COMPUTE_TYPE=float16
 # OPTIONAL — identifier stamped into sources.claimed_by (default tower:<hostname>).
 # Useful if you ever run multiple tower workers and want to tell them apart.
 # SECOND_BRAIN_WORKER_ID=tower-main
 # OPTIONAL — bump worker log verbosity. INFO is the default.
 # PYTHONUNBUFFERED=1
 # SECOND_BRAIN_LOG_LEVEL=DEBUG
--- a/deploy/tower/second-brain-transcribe.service
+++ b/deploy/tower/second-brain-transcribe.service
@ -0,0 +1,46 @@
 [Unit]
 Description=second-brain transcribe worker (tower-side, faster-whisper on GPU)
 Documentation=https://gitea.plantbasedsoutherner.com/petal-power/second-brain
 # The worker reaches petalbrain over WireGuard, so the tunnel must be up first.
 # `Wants=` is the soft form — if the tunnel disappears later the worker logs
 # DB-unreachable warnings and retries, it doesn't crash-loop.
 After=network-online.target wg-quick@wg-lan.service
 Wants=network-online.target wg-quick@wg-lan.service
 [Service]
 Type=simple
 User=herbyadmin
 Group=herbyadmin
 # Adjust to wherever you `git clone`d the repo on the tower.
 WorkingDirectory=/opt/projects/homelab/second-brain
 # Env (SECOND_BRAIN_DATABASE_URL, optional knobs). 0600, owned by herbyadmin.
 EnvironmentFile=/etc/default/second-brain-transcribe
 # `uv run` resolves the project venv. Keep the worker in the foreground so
 # systemd owns the lifecycle; the worker handles SIGTERM/SIGINT cleanly and
 # exits after the current iteration finishes.
 ExecStart=/usr/bin/uv run second-brain transcribe-worker
 Restart=always
 RestartSec=10
 # Crash loop guard — systemd will give up after this if the unit keeps
 # dying. The worker itself logs+backs-off on DB outages so this only
 # trips on genuine failure.
 StartLimitIntervalSec=300
 StartLimitBurst=5
 # A few sandboxing nice-to-haves. Loose on purpose — the worker needs
 # /dev/nvidia* for CUDA and writes to user-owned media/subtitles dirs.
 ProtectSystem=full
 ProtectHome=no
 NoNewPrivileges=true
 # Stdout/stderr → journald.
 StandardOutput=journal
 StandardError=journal
 [Install]
 WantedBy=multi-user.target
--- a/pyproject.toml
+++ b/pyproject.toml
@ -10,7 +10,6 @@ dependencies = [
    "fastapi>=0.115.0",
    "httpx>=0.28.0",
    "jinja2>=3.1.0",
    "openai-whisper",
    "psycopg[binary]>=3.2",
    "psycopg-pool>=3.2",
    "pydantic>=2.0.0",
@ -29,6 +28,11 @@ dependencies = [
 # Max OAuth subscription and has no Python-side Anthropic dependency.
 api = ["anthropic>=0.40.0"]
 # Tower-only: faster-whisper (CTranslate2) for the transcribe worker.
 # Pulls in CUDA wheels — install with `uv sync --extra tower` on the
 # tower box, leave it off on dev. CPU/int8 path works on this same wheel.
 tower = ["faster-whisper>=1.0.0"]
 [dependency-groups]
 dev = [
    "pytest>=8.0",
--- a/src/second_brain/adapters/youtube.py
+++ b/src/second_brain/adapters/youtube.py
@ -1,8 +1,12 @@
 """
-YouTube pull + Whisper transcribe adapter.
+YouTube pull adapter (yt-dlp only).
-Ported from xtract/main.py (VideoDownloader + SubtitleExtractor classes).
+Transcription used to live here on a torch-whisper backend. It moved to
-Adapted to work with the second-brain Source model and Config.
+`second_brain.transcribe` (faster-whisper) once the pipeline split so the
 tower owns transcription and the dev side never imports the heavy model.
 This module is now safe to import from any process — yt-dlp is small, and
 no GPU / whisper dependency is loaded at import time.
 """
 from __future__ import annotations
@ -11,7 +15,6 @@ from datetime import datetime
 from pathlib import Path
 from typing import Optional
 import whisper
 import yt_dlp
 from second_brain.config import Config
@ -84,84 +87,18 @@ class VideoDownloader:
        }
 class SubtitleExtractor:
    """Transcribe a video file to an SRT subtitle file using OpenAI Whisper.
    Ported from xtract SubtitleExtractor — identical core logic,
    writes SRTs to config.subtitles_dir.
    """
    def __init__(self, config: Config) -> None:
        self.config = config
        self._model = None
    def _load_model(self) -> None:
        if self._model is None:
            print(f"[Whisper] Loading {self.config.whisper_model} model…")
            self._model = whisper.load_model(self.config.whisper_model)
            print("[Whisper] Model ready")
    def extract(self, media_file: Path) -> Optional[Path]:
        """Transcribe *media_file* and return the path to the SRT file."""
        self.config.subtitles_dir.mkdir(parents=True, exist_ok=True)
        srt_file = self.config.subtitles_dir / f"{media_file.stem}.srt"
        if srt_file.exists():
            print(f"[Whisper] Already transcribed: {srt_file.name}")
            return srt_file
        self._load_model()
        print(f"[Whisper] Transcribing: {media_file.name}")
        try:
            result = self._model.transcribe(
                str(media_file),
                verbose=False,
                language=None,
            )
            self._write_srt(result, srt_file)
            print(f"[Whisper] Saved: {srt_file.name}")
            return srt_file
        except Exception as exc:
            print(f"[Whisper] Error transcribing {media_file.name}: {exc}")
            return None
    def extract_text(self, media_file: Path) -> Optional[str]:
        """Return the plain-text transcript (no SRT formatting)."""
        self._load_model()
        try:
            result = self._model.transcribe(str(media_file), verbose=False)
            return " ".join(seg["text"].strip() for seg in result["segments"])
        except Exception as exc:
            print(f"[Whisper] Error: {exc}")
            return None
    @staticmethod
    def _write_srt(result: dict, output_file: Path) -> None:
        with open(output_file, "w", encoding="utf-8") as fh:
            for i, seg in enumerate(result["segments"], start=1):
                start = _fmt_timestamp(seg["start"])
                end = _fmt_timestamp(seg["end"])
                fh.write(f"{i}\n{start} --> {end}\n{seg['text'].strip()}\n\n")
    @staticmethod
    def read_srt(srt_file: Path) -> str:
        """Read an SRT file and return its raw text content."""
        return srt_file.read_text(encoding="utf-8")
 # ---------------------------------------------------------------------------
 # High-level adapter
 # ---------------------------------------------------------------------------
 class YouTubeAdapter:
-    """Orchestrates pull + transcribe for a YouTube Source record."""
+    """yt-dlp pull side of a video Source. Transcription is handled by
    `second_brain.transcribe.Transcriber` on the tower."""
    def __init__(self, config: Config) -> None:
        self.config = config
        self.downloader = VideoDownloader(config)
        self.transcriber = SubtitleExtractor(config)
    def pull(self, source: Source) -> bool:
        """Download the video.  Updates source in-place, returns success."""
@ -186,38 +123,12 @@ class YouTubeAdapter:
            source.status = SourceStatus.FAILED
            return False
    def transcribe(self, source: Source) -> bool:
        """Transcribe the downloaded video.  Updates source in-place."""
        if not source.media_path:
            source.error_message = "No media_path; run pull first"
            return False
        media_file = Path(source.media_path)
        srt_file = self.transcriber.extract(media_file)
        if srt_file is None:
            source.error_message = "Whisper transcription failed"
            source.status = SourceStatus.FAILED
            return False
        source.transcript_path = str(srt_file)
        source.transcript_text = SubtitleExtractor.read_srt(srt_file)
        source.status = SourceStatus.TRANSCRIBED
        return True
 # ---------------------------------------------------------------------------
 # Helpers
 # ---------------------------------------------------------------------------
 def _fmt_timestamp(seconds: float) -> str:
    h = int(seconds // 3600)
    m = int((seconds % 3600) // 60)
    s = int(seconds % 60)
    ms = int((seconds % 1) * 1000)
    return f"{h:02d}:{m:02d}:{s:02d},{ms:03d}"
 def _parse_upload_date(upload_date: Optional[str]) -> Optional[datetime]:
    """Parse yt-dlp's YYYYMMDD upload_date string."""
    if not upload_date or len(upload_date) != 8:
@ -228,4 +139,4 @@ def _parse_upload_date(upload_date: Optional[str]) -> Optional[datetime]:
        return None
-__all__ = ["VideoDownloader", "SubtitleExtractor", "YouTubeAdapter"]
+__all__ = ["VideoDownloader", "YouTubeAdapter"]
--- a/src/second_brain/claim.py
+++ b/src/second_brain/claim.py
@ -0,0 +1,154 @@
 """Queue-claim helpers shared by the tower transcribe worker, the dev-side
 process command, and the dev-side extract scheduler.
 The race-safety primitive is `SELECT ... FOR UPDATE SKIP LOCKED`. The
 `claimed_by` / `claimed_at` columns are observability + stale-claim
 recovery only — Postgres row-locks are what actually keep two machines
 from grabbing the same row.
 Usage pattern (worker side, simplified)::
    with db.session() as sess:
        source_id = claim_next_source(
            sess,
            worker_id="tower",
            statuses=[SourceStatus.PENDING, SourceStatus.PULLED],
            source_type=SourceType.VIDEO,
        )
        # commit drops the row lock — the claimed_by/at columns persist.
    if source_id is None:
        # nothing to do; sleep and retry
        return
    with db.session() as sess:
        source = sess.get(Source, source_id)
        try:
            ...do work, mutate status...
        finally:
            release_claim(sess, source_id)
 The claim is *released* once the row reaches a terminal status for this
 stage (TRANSCRIBED, FAILED, etc.) so a follower stage can pick it up. If
 a worker dies mid-claim, `reap_stale_claims` (called at worker startup)
 clears claims older than the configured grace window.
 """
 from __future__ import annotations
 from datetime import timedelta
 from typing import Iterable, Optional
 from sqlalchemy import text
 from sqlalchemy.orm import Session
 from second_brain.models import Source, SourceStatus, SourceType, utcnow
 def claim_next_source(
    sess: Session,
    *,
    worker_id: str,
    statuses: Iterable[SourceStatus],
    source_type: Optional[SourceType] = None,
 ) -> Optional[int]:
    """Atomically claim the oldest unclaimed source matching the filters.
    Returns the source's id (so the caller can re-fetch it in a fresh
    session if it likes), or None if no row matched. The caller's
    session-level COMMIT drops the row lock — the claim survives via the
    `claimed_by` / `claimed_at` columns.
    """
    if not statuses:
        return None
    status_values = [s.value for s in statuses]
    params: dict = {
        "statuses": status_values,
        "worker": worker_id,
        "now": utcnow(),
    }
    type_clause = ""
    if source_type is not None:
        type_clause = "AND source_type = :source_type"
        params["source_type"] = source_type.value
    # Raw SQL because SQLAlchemy ORM doesn't expose SKIP LOCKED cleanly
    # across all minor versions and we want the exact lock semantics
    # documented in the module header. `status::text` lets us bind a
    # parameter list against the native enum cleanly.
    pick = sess.execute(
        text(
            f"""
            SELECT id
              FROM second_brain.sources
             WHERE status::text = ANY(:statuses)
               {type_clause}
               AND claimed_by IS NULL
             ORDER BY ingested_at ASC
             LIMIT 1
             FOR UPDATE SKIP LOCKED
            """
        ),
        params,
    ).first()
    if pick is None:
        return None
    source_id = int(pick[0])
    sess.execute(
        text(
            """
            UPDATE second_brain.sources
               SET claimed_by = :worker,
                   claimed_at = :now
             WHERE id = :id
            """
        ),
        {"worker": worker_id, "now": params["now"], "id": source_id},
    )
    return source_id
 def release_claim(sess: Session, source_id: int) -> None:
    """Drop the claim on a source so a follower stage can pick it up.
    The work-state columns (`status`, `transcript_text`, …) are the
    caller's responsibility — this just nulls the claim pair.
    """
    src = sess.get(Source, source_id)
    if src is None:
        return
    src.claimed_by = None
    src.claimed_at = None
 def reap_stale_claims(
    sess: Session,
    *,
    older_than: timedelta = timedelta(minutes=30),
 ) -> int:
    """Clear claims older than the grace window — recovery for crashed workers.
    Returns the number of rows reaped. Safe to call at worker startup;
    pairs with the worker setting a short poll interval so a stuck
    transcribe doesn't dam the queue forever.
    """
    cutoff = utcnow() - older_than
    result = sess.execute(
        text(
            """
            UPDATE second_brain.sources
               SET claimed_by = NULL,
                   claimed_at = NULL
             WHERE claimed_by IS NOT NULL
               AND claimed_at < :cutoff
            """
        ),
        {"cutoff": cutoff},
    )
    return result.rowcount or 0
 __all__ = ["claim_next_source", "release_claim", "reap_stale_claims"]
--- a/src/second_brain/config.py
+++ b/src/second_brain/config.py
@ -29,8 +29,14 @@ _DEFAULTS: dict[str, Any] = {
    "vault_path": "~/Documents/second-brain-vault",
    "media_dir": "~/.local/share/second-brain/media",
    "subtitles_dir": "~/.local/share/second-brain/subtitles",
    "whisper_model": "small",
    "domains": ["development", "content", "business", "homelab"],
    "whisper": {
        # Tower default: faster-whisper large-v3 on cuda/float16. The
        # transcribe helper picks int8 automatically if the device is cpu.
        "whisper_model": "large-v3",
        "whisper_device": "cuda",
        "whisper_compute_type": "",
    },
    "database": {
        # Empty by default — the runtime expects SECOND_BRAIN_DATABASE_URL
        # (or HERBYLAB_DATABASE_URL) in the environment. Fill this only if
@ -101,11 +107,24 @@ class Config:
            self._raw.get("subtitles_dir", _DEFAULTS["subtitles_dir"])
        ).expanduser()
-    # --- extraction ---
+    # --- transcription ---
    @property
    def whisper(self) -> dict[str, Any]:
        """Merged [whisper] block. Env vars in `transcribe.resolve_settings`
        still win over these for the tower worker."""
        return _merge(_DEFAULTS["whisper"], self._raw.get("whisper", {}))
    @property
    def whisper_model(self) -> str:
-        return self._raw.get("whisper_model", _DEFAULTS["whisper_model"])
+        """Backwards-compat shortcut — older callers (and settings.toml
        in the wild) reference whisper_model at the top level."""
        return (
            self._raw.get("whisper_model")
            or self.whisper.get("whisper_model", "large-v3")
        )
    # --- extraction ---
    @property
    def domains(self) -> list[str]:
--- a/src/second_brain/main.py
+++ b/src/second_brain/main.py
@ -104,8 +104,13 @@ def process(
    skip_transcribe: bool,
    skip_extract: bool,
 ) -> None:
-    """Run the pipeline on pending sources."""
+    """Run the dev-side pipeline: article pulls + extract/embed.
-    from second_brain.adapters.youtube import YouTubeAdapter
+
    Video pull + transcribe have moved to the tower-side
    `transcribe-worker` daemon (faster-whisper on the 3080); this command
    is article-only on the pull side, but still picks up TRANSCRIBED rows
    of any source_type for the extract/embed step.
    """
    from second_brain.adapters.article import ArticleAdapter
    from second_brain.context.assembler import ContextAssembler
    from second_brain.extractor.engine import ExtractionEngine
@ -115,7 +120,6 @@ def process(
    config.ensure_dirs()
    db = get_database()
    yt_adapter = YouTubeAdapter(config)
    art_adapter = ArticleAdapter(config)
    assembler = ContextAssembler(config)
@ -135,52 +139,61 @@ def process(
            click.echo("[process] Extraction step will be skipped.")
            skip_extract = True
    # Dev side: pull only ARTICLE sources (videos belong to the tower
    # worker) — but pick up TRANSCRIBED of any type for extract/embed.
    with db.session() as sess:
-        query = sess.query(Source.id).filter(
+        pull_ids = []
-            Source.status.in_([
+        if not skip_pull:
-                SourceStatus.PENDING,
+            pull_ids = [
-                SourceStatus.PULLED,
+                row[0]
-                SourceStatus.TRANSCRIBED,
+                for row in sess.query(Source.id)
-            ])
+                .filter(
                    Source.status == SourceStatus.PENDING,
                    Source.source_type == SourceType.ARTICLE,
                )
                .all()
            ]
        extract_ids = [
            row[0]
            for row in sess.query(Source.id)
            .filter(Source.status == SourceStatus.TRANSCRIBED)
            .all()
        ]
        # Keep order stable (pulls first so freshly-transcribed articles
        # roll into extract in the same invocation) and de-duplicate.
        source_ids: list[int] = []
        for sid in pull_ids + extract_ids:
            if sid not in source_ids:
                source_ids.append(sid)
        if limit:
-            query = query.limit(limit)
+            source_ids = source_ids[:limit]
        source_ids = [row[0] for row in query.all()]
    if not source_ids:
        click.echo("[process] No sources to process.")
        return
    click.echo(f"[process] Processing {len(source_ids)} source(s)…")
    if skip_transcribe:
        click.echo("[process] (--skip-transcribe is now a no-op — transcription "
                   "runs on the tower worker.)")
    for source_id in source_ids:
        with db.session() as sess:
            source = sess.get(Source, source_id)
            click.echo(f"\n  [{source.id}] {source.url[:80]}")
-            # Pull step
+            # Pull step — articles only on the dev side.
-            if not skip_pull and source.status == SourceStatus.PENDING:
+            if (
-                click.echo("    → pull")
+                not skip_pull
-                if source.source_type == SourceType.VIDEO:
+                and source.status == SourceStatus.PENDING
-                    ok = yt_adapter.pull(source)
+                and source.source_type == SourceType.ARTICLE
-                else:
+            ):
                click.echo("    → pull (article)")
                ok = art_adapter.pull(source)
                if not ok:
                    click.echo(f"    ✗ pull failed: {source.error_message}")
                    continue
            # Transcribe step (videos only — articles come out transcribed)
            if (
                not skip_transcribe
                and source.status == SourceStatus.PULLED
                and source.source_type == SourceType.VIDEO
            ):
                click.echo("    → transcribe")
                ok = yt_adapter.transcribe(source)
                if not ok:
                    click.echo(f"    ✗ transcribe failed: {source.error_message}")
                    continue
            # Extract step
            if not skip_extract and source.status == SourceStatus.TRANSCRIBED and engine:
                click.echo("    → extract")
@ -289,6 +302,52 @@ def schedule(dry_run: bool) -> None:
    scheduler.run(dry_run=dry_run)
 # ---------------------------------------------------------------------------
 # transcribe-worker (tower-side daemon)
 # ---------------------------------------------------------------------------
@cli.command("transcribe-worker")
@click.option("--once", is_flag=True, default=False,
              help="Process at most one source then exit (useful for ad-hoc runs / tests).")
@click.option("--poll-interval", default=None, type=int,
              help="Seconds between empty-queue polls (default 15).")
@click.option("--worker-id", default=None,
              help="Identifier stamped into sources.claimed_by (default tower:<hostname>).")
 def transcribe_worker(
    once: bool,
    poll_interval: Optional[int],
    worker_id: Optional[str],
 ) -> None:
    """Long-running tower-side worker: claim → yt-dlp pull → faster-whisper.
    Reads pipeline_settings each iteration so the dashboard's enable
    toggle / active-hours window / max-items-per-run / max-video-length
    all take effect within one poll cycle.
    """
    import logging
    from second_brain.scheduler.transcribe_worker import (
        DEFAULT_POLL_INTERVAL_SECONDS,
        TranscribeWorker,
    )
    logging.basicConfig(
        level=logging.INFO,
        format="%(asctime)s %(levelname)s %(name)s: %(message)s",
    )
    config = load_config()
    config.ensure_dirs()
    worker = TranscribeWorker(
        config,
        worker_id=worker_id,
        poll_interval=poll_interval or DEFAULT_POLL_INTERVAL_SECONDS,
    )
    processed = worker.run(once=once)
    click.echo(f"[transcribe-worker] exiting; processed={processed}")
 # ---------------------------------------------------------------------------
 # Helpers
 # ---------------------------------------------------------------------------
--- a/src/second_brain/models.py
+++ b/src/second_brain/models.py
@ -124,6 +124,10 @@ class Source(Base):
    updated_at: Mapped[Optional[datetime]] = mapped_column(DateTime, nullable=True)
    error_message: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
    # Claim mechanism — see `second_brain.claim` for the queue dance.
    claimed_by: Mapped[Optional[str]] = mapped_column(String(100), nullable=True)
    claimed_at: Mapped[Optional[datetime]] = mapped_column(DateTime, nullable=True)
    # Relationships
    extraction: Mapped[Optional["Extraction"]] = relationship(
        "Extraction", back_populates="source", uselist=False, cascade="all, delete-orphan"
--- a/src/second_brain/scheduler/transcribe_worker.py
+++ b/src/second_brain/scheduler/transcribe_worker.py
@ -0,0 +1,312 @@
 """Tower-side transcribe worker — long-running poll loop.
 Runs on the EndeavourOS tower (RTX 3080) as a systemd service. Reaches
 petalbrain over WireGuard at db.wg.herbylab.dev:5432.
 Loop body:
  1. Read pipeline_settings.
     - transcription_enabled = false → sleep, retry.
     - outside transcription_active_hours → sleep, retry.
     - transcription_max_concurrent_gpu_jobs is consulted (default 1 =
       this worker is the single in-flight job; future scale-out can fan
       a per-job lock here).
  2. Claim the oldest unclaimed VIDEO source in {PENDING, PULLED} via
     `SELECT ... FOR UPDATE SKIP LOCKED`.
  3. If PENDING → yt-dlp pull → PULLED (claim retained).
  4. If PULLED → faster-whisper transcribe → TRANSCRIBED (claim released).
  5. Honor transcription_max_video_length_seconds: skip & mark FAILED
     with a clear error_message if the metadata's duration exceeds it.
 Graceful degradation:
  - DB unreachable → log at WARNING, sleep, retry. Never crash-loop.
  - Whisper failure on one file → mark that source FAILED, release claim,
    continue with the next one.
  - SIGTERM (systemd stop) → finish the current iteration, exit cleanly.
 """
 from __future__ import annotations
 import logging
 import os
 import signal
 import socket
 import time
 from datetime import datetime
 from pathlib import Path
 from second_brain.claim import (
    claim_next_source,
    reap_stale_claims,
    release_claim,
 )
 from second_brain.config import Config
 from second_brain.database import get_database
 from second_brain.models import Source, SourceStatus, SourceType, utcnow
 from second_brain.settings_store import get_settings, in_active_window
 logger = logging.getLogger(__name__)
 # Default loop pace — short so a dropped-in video starts transcribing
 # right away. The worker also re-reads pipeline_settings every iteration
 # so a dashboard toggle takes effect within ~POLL_INTERVAL_SECONDS.
 DEFAULT_POLL_INTERVAL_SECONDS = 15
 DEFAULT_DB_BACKOFF_SECONDS = 30
 class TranscribeWorker:
    """The tower's transcribe poll loop."""
    def __init__(
        self,
        config: Config,
        *,
        worker_id: str | None = None,
        poll_interval: int = DEFAULT_POLL_INTERVAL_SECONDS,
        db_backoff: int = DEFAULT_DB_BACKOFF_SECONDS,
    ) -> None:
        self.config = config
        self.worker_id = (
            worker_id
            or os.environ.get("SECOND_BRAIN_WORKER_ID")
            or f"tower:{socket.gethostname()}"
        )
        self.poll_interval = poll_interval
        self.db_backoff = db_backoff
        self._stop = False
        # SIGTERM/SIGINT → clean shutdown after the current iteration.
        signal.signal(signal.SIGTERM, self._on_signal)
        signal.signal(signal.SIGINT, self._on_signal)
    def _on_signal(self, signum, frame):  # noqa: ARG002 — frame required by signal API
        logger.info("worker received signal %s — stopping after current iteration", signum)
        self._stop = True
    # ------------------------------------------------------------------
    # Top-level run loop
    # ------------------------------------------------------------------
    def run(self, *, once: bool = False, max_iterations: int | None = None) -> int:
        """Drive the loop. Returns the count of sources processed in this run.
        `once=True` runs a single iteration regardless of queue depth —
        useful for ad-hoc CLI invocation. `max_iterations` caps loops so
        tests don't run forever.
        """
        processed = 0
        iterations = 0
        startup_reaped = False
        while not self._stop:
            iterations += 1
            if max_iterations is not None and iterations > max_iterations:
                break
            try:
                db = get_database()
            except Exception as exc:
                logger.warning("DB not configured: %s — sleeping %ds", exc, self.db_backoff)
                if once:
                    return processed
                self._sleep(self.db_backoff)
                continue
            try:
                with db.session() as sess:
                    if not startup_reaped:
                        reaped = reap_stale_claims(sess)
                        if reaped:
                            logger.info("reaped %d stale claim(s) on startup", reaped)
                        startup_reaped = True
                    settings = get_settings(sess)
                    enabled = settings.transcription_enabled
                    window = (
                        settings.transcription_active_hours_start,
                        settings.transcription_active_hours_end,
                    )
                    max_items = int(settings.transcription_max_items_per_run or 0)
                    max_video_length = settings.transcription_max_video_length_seconds
            except Exception as exc:
                logger.warning("DB unreachable: %s — sleeping %ds", exc, self.db_backoff)
                if once:
                    return processed
                self._sleep(self.db_backoff)
                continue
            if not enabled:
                logger.info("transcription_enabled=false — sleeping")
                if once:
                    return processed
                self._sleep(self.poll_interval)
                continue
            if not in_active_window(datetime.now().time(), window[0], window[1]):
                logger.info("outside transcription active window — sleeping")
                if once:
                    return processed
                self._sleep(self.poll_interval)
                continue
            if max_items and processed >= max_items:
                logger.info("reached transcription_max_items_per_run=%d — exiting", max_items)
                break
            handled = self._process_one(max_video_length)
            if handled is None:
                # Nothing to claim — sleep and re-poll.
                if once:
                    return processed
                self._sleep(self.poll_interval)
                continue
            processed += 1
            if once:
                return processed
        logger.info("worker stopping (processed=%d, iterations=%d)", processed, iterations)
        return processed
    # ------------------------------------------------------------------
    # Per-source step
    # ------------------------------------------------------------------
    def _process_one(self, max_video_length: int | None) -> int | None:
        """Try to claim and advance one video source. Returns the source id
        on success, or None if nothing was claimable.
        """
        db = get_database()
        # Step 1: claim. Short transaction, FOR UPDATE SKIP LOCKED inside.
        with db.session() as sess:
            source_id = claim_next_source(
                sess,
                worker_id=self.worker_id,
                statuses=[SourceStatus.PENDING, SourceStatus.PULLED],
                source_type=SourceType.VIDEO,
            )
        if source_id is None:
            return None
        # Step 2: do the work. Fresh session because we crossed a commit.
        try:
            with db.session() as sess:
                source = sess.get(Source, source_id)
                if source is None:
                    return None
                logger.info("[%s] starting %s id=%d url=%s",
                            self.worker_id, source.status.value, source.id, source.url)
                self._advance(source, max_video_length, sess)
        except Exception as exc:
            logger.exception("unexpected error on source %s: %s", source_id, exc)
            with db.session() as sess:
                src = sess.get(Source, source_id)
                if src is not None:
                    src.status = SourceStatus.FAILED
                    src.error_message = f"transcribe worker crashed: {exc}"
                    src.updated_at = utcnow()
                    release_claim(sess, source_id)
        return source_id
    def _advance(
        self,
        source: Source,
        max_video_length: int | None,
        sess,
    ) -> None:
        """Advance a single claimed source through pull → transcribe."""
        from second_brain.adapters.youtube import YouTubeAdapter
        from second_brain.transcribe import Transcriber, resolve_settings, write_srt
        # Length guard — relies on yt-dlp's metadata for PENDING sources;
        # for already-PULLED sources we trust whatever the prior pull
        # recorded on the row.
        if source.status == SourceStatus.PENDING:
            yt = YouTubeAdapter(self.config)
            try:
                meta = yt.downloader.extract_metadata(source.url)
            except Exception as exc:
                logger.warning("metadata fetch failed for source %s: %s", source.id, exc)
                meta = {}
            duration = meta.get("duration_seconds")
            if (
                max_video_length is not None
                and duration is not None
                and duration > max_video_length
            ):
                source.error_message = (
                    f"video length {duration}s exceeds "
                    f"transcription_max_video_length_seconds={max_video_length}"
                )
                source.status = SourceStatus.FAILED
                source.updated_at = utcnow()
                release_claim(sess, source.id)
                logger.info("skipping source %s — too long (%ss)", source.id, duration)
                return
            # Pre-fill the metadata so we don't re-hit yt-dlp during pull.
            if not source.title:
                source.title = meta.get("title")
            if not source.author:
                source.author = meta.get("author")
            if not source.duration_seconds:
                source.duration_seconds = duration
            if not source.published_at:
                source.published_at = meta.get("published_at")
            ok = yt.pull(source)
            if not ok:
                source.updated_at = utcnow()
                release_claim(sess, source.id)
                return
        # Transcribe.
        if source.status == SourceStatus.PULLED:
            if not source.media_path:
                source.error_message = "no media_path after pull"
                source.status = SourceStatus.FAILED
                source.updated_at = utcnow()
                release_claim(sess, source.id)
                return
            wh = resolve_settings(self.config.whisper)
            transcriber = Transcriber(
                model=wh["model"],
                device=wh["device"],
                compute_type=wh["compute_type"],
            )
            media_file = Path(source.media_path)
            srt_path = self.config.subtitles_dir / f"{media_file.stem}.srt"
            try:
                text, segments = transcriber.transcribe(media_file)
            except Exception as exc:
                source.error_message = f"transcribe failed: {exc}"
                source.status = SourceStatus.FAILED
                source.updated_at = utcnow()
                release_claim(sess, source.id)
                logger.exception("transcribe failed for source %s", source.id)
                return
            write_srt(segments, srt_path)
            source.transcript_path = str(srt_path)
            source.transcript_text = text
            source.status = SourceStatus.TRANSCRIBED
            source.updated_at = utcnow()
            release_claim(sess, source.id)
            logger.info("transcribed source %s — %d chars", source.id, len(text))
    # ------------------------------------------------------------------
    # Helpers
    # ------------------------------------------------------------------
    def _sleep(self, seconds: int) -> None:
        """Sleep that wakes early on SIGTERM."""
        end = time.time() + seconds
        while time.time() < end and not self._stop:
            time.sleep(1)
 __all__ = ["TranscribeWorker", "DEFAULT_POLL_INTERVAL_SECONDS"]
--- a/src/second_brain/transcribe.py
+++ b/src/second_brain/transcribe.py
@ -0,0 +1,171 @@
 """Audio/video transcription via faster-whisper (CTranslate2 backend).
 Lazy-imports `faster_whisper` so the dev side — which doesn't transcribe
 anything — doesn't need the heavyweight CUDA wheels installed.
 Config keys (read from `[whisper]` in settings.toml, env-overrides win):
 - `whisper_model`         : the model name. Default "large-v3".
 - `whisper_device`        : "cuda" | "cpu" | "auto". Default "cuda".
 - `whisper_compute_type`  : "float16" | "int8" | "int8_float16" | …
                            Default float16 on cuda, int8 on cpu.
 Outputs an SRT file next to `config.subtitles_dir/{stem}.srt` plus a
 plain-text transcript string. The pipeline writes the text into
 `sources.transcript_text` and the path into `sources.transcript_path`,
 matching the previous torch-whisper contract so downstream extract/embed
 doesn't need to change.
 """
 from __future__ import annotations
 import logging
 import os
 from pathlib import Path
 from typing import Iterable, Optional
 logger = logging.getLogger(__name__)
 # ---------------------------------------------------------------------------
 # Config resolution
 # ---------------------------------------------------------------------------
 def _resolve(cfg: dict | None, env_key: str, cfg_key: str, default: str) -> str:
    env = os.environ.get(env_key)
    if env:
        return env
    if cfg is not None:
        v = cfg.get(cfg_key)
        if v:
            return v
    return default
 def resolve_settings(config_block: dict | None = None) -> dict[str, str]:
    """Resolve whisper runtime settings from env > config block > defaults.
    Pulls model/device/compute_type and picks a sane compute_type per device
    when none was specified. Returns plain strings so the caller can stash
    them in logs / pass straight to faster-whisper.
    """
    model = _resolve(config_block, "WHISPER_MODEL", "whisper_model", "large-v3")
    device = _resolve(config_block, "WHISPER_DEVICE", "whisper_device", "cuda")
    explicit_compute = None
    if config_block is not None:
        explicit_compute = config_block.get("whisper_compute_type")
    explicit_compute = os.environ.get("WHISPER_COMPUTE_TYPE", explicit_compute)
    if explicit_compute:
        compute_type = explicit_compute
    elif device == "cpu":
        compute_type = "int8"
    else:
        compute_type = "float16"
    return {"model": model, "device": device, "compute_type": compute_type}
 # ---------------------------------------------------------------------------
 # Transcriber
 # ---------------------------------------------------------------------------
 class Transcriber:
    """faster-whisper wrapper. Model is loaded lazily on first transcribe."""
    def __init__(
        self,
        model: str = "large-v3",
        device: str = "cuda",
        compute_type: str = "float16",
    ) -> None:
        self.model = model
        self.device = device
        self.compute_type = compute_type
        self._impl = None
    def _load(self) -> None:
        if self._impl is not None:
            return
        try:
            from faster_whisper import WhisperModel  # type: ignore[import-not-found]
        except ImportError as exc:
            raise RuntimeError(
                "faster-whisper is not installed. On the tower run: "
                "uv sync --extra tower"
            ) from exc
        logger.info(
            "loading faster-whisper model=%s device=%s compute_type=%s",
            self.model,
            self.device,
            self.compute_type,
        )
        self._impl = WhisperModel(
            self.model, device=self.device, compute_type=self.compute_type
        )
    def transcribe(
        self,
        media_file: Path,
        *,
        language: Optional[str] = None,
        beam_size: int = 5,
    ) -> tuple[str, list[dict]]:
        """Transcribe `media_file`. Returns (full_text, segments).
        Each segment dict has start/end (float seconds) and text (str) —
        same shape the SRT writer needs.
        """
        self._load()
        assert self._impl is not None  # for the type-checker
        segments_iter, info = self._impl.transcribe(
            str(media_file),
            language=language,
            beam_size=beam_size,
        )
        logger.info(
            "transcribe start: %s language=%s duration=%.1fs",
            media_file.name,
            getattr(info, "language", "?"),
            getattr(info, "duration", 0.0) or 0.0,
        )
        segments: list[dict] = []
        text_parts: list[str] = []
        for seg in segments_iter:
            t = (seg.text or "").strip()
            segments.append({"start": float(seg.start), "end": float(seg.end), "text": t})
            if t:
                text_parts.append(t)
        return " ".join(text_parts), segments
 # ---------------------------------------------------------------------------
 # SRT writer
 # ---------------------------------------------------------------------------
 def write_srt(segments: Iterable[dict], output: Path) -> None:
    """Write a list of {start,end,text} dicts as SRT into `output`."""
    output.parent.mkdir(parents=True, exist_ok=True)
    with open(output, "w", encoding="utf-8") as fh:
        for i, seg in enumerate(segments, start=1):
            start = _fmt_timestamp(seg["start"])
            end = _fmt_timestamp(seg["end"])
            fh.write(f"{i}\n{start} --> {end}\n{seg['text']}\n\n")
 def _fmt_timestamp(seconds: float) -> str:
    h = int(seconds // 3600)
    m = int((seconds % 3600) // 60)
    s = int(seconds % 60)
    ms = int((seconds % 1) * 1000)
    return f"{h:02d}:{m:02d}:{s:02d},{ms:03d}"
 __all__ = ["Transcriber", "resolve_settings", "write_srt"]
--- a/tests/test_claim.py
+++ b/tests/test_claim.py
@ -0,0 +1,107 @@
 """Live-DB test: claim_next_source is race-safe against concurrent callers.
 Skips when no DB URL is configured. This is the integration-level guard
 for the SELECT ... FOR UPDATE SKIP LOCKED dance — unit-testing the SQL
 without Postgres would prove nothing.
 """
 from __future__ import annotations
 import os
 import threading
 import uuid
 import psycopg
 import pytest
 def _db_reachable() -> bool:
    url = os.environ.get("SECOND_BRAIN_DATABASE_URL") or os.environ.get(
        "HERBYLAB_DATABASE_URL"
    )
    if not url:
        return False
    raw = url.replace("postgresql+psycopg://", "postgresql://", 1)
    try:
        with psycopg.connect(raw, connect_timeout=2):
            return True
    except Exception:
        return False
 pytestmark = pytest.mark.skipif(
    not _db_reachable(),
    reason="claim race test needs SECOND_BRAIN_DATABASE_URL pointed at a real petalbrain",
 )
 def test_two_workers_never_grab_the_same_row():
    from second_brain.claim import claim_next_source, release_claim
    from second_brain.database import Database, reset_database_singleton
    from second_brain.models import Source, SourceStatus, SourceType, utcnow
    reset_database_singleton()
    db = Database()
    # Seed a single TRANSCRIBED-stage candidate.
    unique = uuid.uuid4().hex[:8]
    url = f"https://example.test/claim-race/{unique}"
    with db.session() as sess:
        src = Source(
            url=url,
            title=f"claim-race-{unique}",
            domain="development",
            source_type=SourceType.VIDEO,
            status=SourceStatus.PULLED,
            ingested_at=utcnow(),
        )
        sess.add(src)
        sess.flush()
        source_id = src.id
    results: list[int | None] = []
    lock = threading.Lock()
    def worker(name: str):
        with db.session() as sess:
            claimed = claim_next_source(
                sess,
                worker_id=name,
                statuses=[SourceStatus.PENDING, SourceStatus.PULLED],
                source_type=SourceType.VIDEO,
            )
        with lock:
            results.append(claimed)
    t1 = threading.Thread(target=worker, args=("worker-A",))
    t2 = threading.Thread(target=worker, args=("worker-B",))
    t1.start()
    t2.start()
    t1.join()
    t2.join()
    try:
        # Exactly one worker should have grabbed the row; the other gets None.
        # (SKIP LOCKED guarantees this even if both transactions overlap.)
        non_none = [r for r in results if r is not None]
        assert non_none == [source_id], (
            f"expected exactly one claimant of {source_id}, got results={results}"
        )
        # Confirm claim columns landed.
        with db.session() as sess:
            row = sess.get(Source, source_id)
            assert row.claimed_by in {"worker-A", "worker-B"}
            assert row.claimed_at is not None
        # Releasing the claim should null both columns.
        with db.session() as sess:
            release_claim(sess, source_id)
        with db.session() as sess:
            row = sess.get(Source, source_id)
            assert row.claimed_by is None
            assert row.claimed_at is None
    finally:
        with db.session() as sess:
            sess.query(Source).filter(Source.id == source_id).delete()
        reset_database_singleton()
--- a/tests/test_transcribe.py
+++ b/tests/test_transcribe.py
@ -0,0 +1,137 @@
 """Unit + integration coverage for second_brain.transcribe.
 Unit-level: resolve_settings + write_srt are pure-Python and run anywhere.
 Integration: an end-to-end CPU/int8 transcribe of a synthesized audio
 clip — auto-skipped when ffmpeg or faster-whisper is missing (the dev
 side doesn't install faster-whisper by default). Only the *tower* with
 GPU large-v3 can validate the production hot path; this test proves the
 wiring + the SRT contract + the lazy-import path on cheap CPU.
 """
 from __future__ import annotations
 import pytest
 # ---------------------------------------------------------------------------
 # Pure helpers
 # ---------------------------------------------------------------------------
 def test_resolve_settings_picks_int8_for_cpu_by_default():
    from second_brain.transcribe import resolve_settings
    out = resolve_settings({"whisper_device": "cpu"})
    assert out["device"] == "cpu"
    assert out["compute_type"] == "int8"
    assert out["model"] == "large-v3"
 def test_resolve_settings_picks_float16_for_cuda_by_default():
    from second_brain.transcribe import resolve_settings
    out = resolve_settings({"whisper_device": "cuda"})
    assert out["device"] == "cuda"
    assert out["compute_type"] == "float16"
 def test_resolve_settings_env_overrides_block(monkeypatch):
    from second_brain.transcribe import resolve_settings
    monkeypatch.setenv("WHISPER_MODEL", "small")
    monkeypatch.setenv("WHISPER_DEVICE", "cpu")
    monkeypatch.setenv("WHISPER_COMPUTE_TYPE", "int8_float16")
    out = resolve_settings({"whisper_device": "cuda"})
    assert out["model"] == "small"
    assert out["device"] == "cpu"
    assert out["compute_type"] == "int8_float16"
 def test_write_srt_roundtrip(tmp_path):
    from second_brain.transcribe import write_srt
    segs = [
        {"start": 0.0, "end": 1.5, "text": "hello"},
        {"start": 1.5, "end": 3.25, "text": "world"},
    ]
    out = tmp_path / "x.srt"
    write_srt(segs, out)
    body = out.read_text()
    assert "1\n00:00:00,000 --> 00:00:01,500\nhello" in body
    assert "2\n00:00:01,500 --> 00:00:03,250\nworld" in body
 # ---------------------------------------------------------------------------
 # Integration: synthesized audio → CPU/int8 faster-whisper
 # ---------------------------------------------------------------------------
 def _have_faster_whisper() -> bool:
    try:
        import faster_whisper  # type: ignore[import-not-found]  # noqa: F401
    except ImportError:
        return False
    return True
 def _have_numpy() -> bool:
    try:
        import numpy  # noqa: F401
    except ImportError:
        return False
    return True
@pytest.mark.skipif(
    not (_have_faster_whisper() and _have_numpy()),
    reason="needs faster-whisper (uv sync --extra tower) + numpy to exercise CPU path",
 )
 def test_cpu_transcribe_smoke(tmp_path):
    """Hand faster-whisper a synthesized audio array on CPU/int8 with `tiny`.
    Pure-tone input gives empty / minimal recognition — the point is the
    wiring: model loads, the segments iterator drains, an SRT is writable.
    Using `tiny` to keep this under ~30s coldcache. Skipped on dev where
    faster-whisper isn't installed; only the tower's `uv sync --extra
    tower` brings it in.
    Bypassing ffmpeg by passing a numpy array — faster-whisper.transcribe
    accepts (path | file-like | np.ndarray). This exercises the same code
    path the worker uses, only with zero external binaries.
    """
    import numpy as np
    from second_brain.transcribe import write_srt
    # Build 2s of 440Hz mono at 16kHz, the model's native sample rate.
    sr = 16000
    duration = 2.0
    t = np.arange(0, int(sr * duration)) / sr
    audio = (0.2 * np.sin(2 * np.pi * 440 * t)).astype(np.float32)
    # Load `tiny` to keep this cheap; we're not asserting transcription
    # quality, just that the pipeline executes and returns the documented
    # shape.
    from faster_whisper import WhisperModel  # type: ignore[import-not-found]
    model = WhisperModel("tiny", device="cpu", compute_type="int8")
    segments_iter, _ = model.transcribe(audio, language="en", beam_size=1)
    # Materialize to mirror what Transcriber.transcribe does internally.
    segments: list[dict] = []
    text_parts: list[str] = []
    for seg in segments_iter:
        s = (seg.text or "").strip()
        segments.append({"start": float(seg.start), "end": float(seg.end), "text": s})
        if s:
            text_parts.append(s)
    text = " ".join(text_parts)
    assert isinstance(text, str)
    assert isinstance(segments, list)
    srt = tmp_path / "out.srt"
    write_srt(segments, srt)
    assert srt.exists()
--- a/uv.lock
+++ b/uv.lock
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