Add M2 transcription pipeline: faster-whisper with GPU auto-detect (v0.2.0)

adscrub transcribe: downloads audio, transcribes episodes with no
chapter-sourced ad spans, stores segment timestamps. Device picked at
runtime via ctranslate2.get_cuda_device_count() -- CUDA if visible, CPU
int8 otherwise -- so the same code works from a plain dev shell or a
GPU-enabled Docker deploy. Added compose.gpu.yaml to request the host's
GPU via the nvidia Docker runtime and build with the cublas/cudnn extra.

Corrected CLAUDE.md/docs/PLAN.md: code has a real RTX 2070 SUPER and
Docker's nvidia runtime is registered -- the earlier 'no GPU' note only
reflected this interactive shell's LXC lacking device passthrough, not
the host's actual capability.

CHANGELOG.md

@@ -7,6 +7,27 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## [0.2.0] - 2026-07-10
+
+### Added
+
+- Transcription pipeline (`adscrub transcribe`): downloads episode audio (cached,
+  re-run safe), transcribes with faster-whisper, stores segment timestamps in
+  `data/transcripts/<id>.json`. Skips episodes already covered by a chapter-sourced
+  ad span (M1) or already transcribed.
+- Device auto-detection via `ctranslate2.get_cuda_device_count()` — CUDA float16 if
+  a GPU is visible to the process, CPU int8 otherwise. No config needed; works the
+  same whether run from a plain dev shell or a GPU-enabled Docker deploy.
+- `compose.gpu.yaml` override: requests the host's GPU via the `nvidia` Docker
+  runtime and builds the image with the optional `gpu` extra (cuBLAS/cuDNN).
+
+### Fixed
+
+- CLAUDE.md/docs/PLAN.md previously claimed this host had no GPU at all; corrected
+  — `code` has a physical RTX 2070 SUPER and Docker's `nvidia` runtime is
+  registered. The earlier claim only reflected this interactive dev shell's LXC not
+  having the device nodes passed through, which is a narrower fact.
+
 ## [0.1.0] - 2026-07-10
 
 ### Added

CLAUDE.md

@@ -38,12 +38,17 @@ episode works on any feed, at the cost of real per-episode compute.
   Fingerprinting (à la AdBlockRadio) only catches ads that repeat verbatim; modern
   podcast ads are frequently host-read and/or dynamically inserted per listener, so
   there's often nothing to fingerprint against. Transcribe, then classify the text.
-- **No CUDA device on this host** (`code`, a LAN host — confirmed no `/dev/nvidia*`,
-  no `nvidia-smi`, 2026-07-10) despite Tendril's GPU work happening elsewhere on the
-  fleet. Whisper here means CPU-only inference (int8-quantized small/base model) until
-  proven otherwise on a box with real passthrough. This is the same open question hark's
-  own PLAN.md already flagged and deferred — now empirically confirmed, not just
-  suspected.
+- **GPU is real, just not wired into this interactive shell.** `code` (a LAN host)
+  physically has an RTX 2070 SUPER (`lspci`: NVIDIA TU104), and Docker on this host has
+  the `nvidia` runtime registered plus a CDI spec for `/dev/nvidia0` (confirmed
+  2026-07-10) — so a container run with GPU passthrough requested (`--gpus`/CDI device)
+  gets real CUDA. The interactive dev LXC this Claude session runs in just doesn't have
+  the device nodes passed to *it*, which is a different, narrower fact than "no GPU on
+  this host" (an earlier version of this note wrongly conflated the two — don't repeat
+  that mistake). Transcription code must not hard-require CUDA either way: detect it at
+  runtime (`ctranslate2.get_cuda_device_count()`, no torch needed) and fall back to CPU
+  int8 cleanly, since the CLI itself may still run outside GPU passthrough (e.g. ad hoc
+  from a dev shell) even when the Docker deploy target has it.
 - Integration output is a regenerated RSS feed, same pattern as hark: the player
   subscribes to it like any podcast, zero app-side changes.
 

Dockerfile

@@ -3,6 +3,11 @@
 # Every pipeline stage is a one-shot command, e.g.:
 #   docker compose run --rm adscrub ingest
 #   docker compose run --rm adscrub chapters
+#   docker compose run --rm adscrub transcribe
+#
+# Build with --build-arg GPU=1 (or `docker compose -f compose.yaml -f compose.gpu.yaml
+# build`) to pull in the cuBLAS/cuDNN extra for faster-whisper's CUDA path — only
+# needed on a host that actually passes a GPU through (see CLAUDE.md).
 
 FROM python:3.13-slim
 
@@ -10,13 +15,18 @@ COPY --from=ghcr.io/astral-sh/uv:0.7 /uv /uvx /bin/
 
 WORKDIR /app
 ENV UV_LINK_MODE=copy UV_COMPILE_BYTECODE=1
+ARG GPU=0
 
 # dependency layer first: rebuilds only when the lockfile changes
 COPY pyproject.toml uv.lock README.md ./
-RUN --mount=type=cache,target=/root/.cache/uv uv sync --frozen --no-dev --no-install-project
+RUN --mount=type=cache,target=/root/.cache/uv \
+    if [ "$GPU" = "1" ]; then uv sync --frozen --no-dev --no-install-project --extra gpu; \
+    else uv sync --frozen --no-dev --no-install-project; fi
 
 COPY src ./src
-RUN --mount=type=cache,target=/root/.cache/uv uv sync --frozen --no-dev
+RUN --mount=type=cache,target=/root/.cache/uv \
+    if [ "$GPU" = "1" ]; then uv sync --frozen --no-dev --extra gpu; \
+    else uv sync --frozen --no-dev; fi
 
 ENV PATH="/app/.venv/bin:$PATH" \
     ADSCRUB_DB=/app/data/adscrub.db

README.md

@@ -14,8 +14,9 @@ on, and a feed-level proxy works with any podcast app, not just one.
 1. **Ingest** (done) — register a source feed, fetch it, upsert episodes into SQLite.
 2. **Chapters** (done) — many feeds already mark ad breaks via Podcasting 2.0
    `<podcast:chapters>`; scan those first since it's free (no transcription needed).
-3. **Transcribe** (not built — M2) — for episodes with no usable chapter markers,
-   transcribe locally with Whisper.
+3. **Transcribe** (done) — for episodes with no usable chapter markers, download
+   the audio and transcribe locally with Whisper (faster-whisper; CUDA if a GPU is
+   visible to the process, CPU int8 otherwise — auto-detected, no config needed).
 4. **Detect** (not built — M3) — classify ad spans from the transcript via an LLM
    call (host-read ad patterns: "brought to you by", promo codes, URL drops, tone
    shift) — this matters because modern ads are often host-read and unique per
@@ -25,10 +26,7 @@ on, and a feed-level proxy works with any podcast app, not just one.
 6. **Serve** (not built — M4/M5) — re-host a cleaned RSS feed pointing at the cut
    audio; this is the only thing the podcast player ever sees.
 
-See [docs/PLAN.md](docs/PLAN.md) for the full milestone breakdown, including the
-open question about GPU/Whisper feasibility on this host (no CUDA device present
-here — CPU-only inference is the working assumption until proven otherwise
-elsewhere on the homelab).
+See [docs/PLAN.md](docs/PLAN.md) for the full milestone breakdown.
 
 ## Usage
 
@@ -37,11 +35,18 @@ uv sync
 uv run adscrub add-feed https://feeds.example.com/show   # register a feed to proxy
 uv run adscrub ingest                                     # fetch it, upsert episodes
 uv run adscrub chapters                                   # scan chapter markers for ad spans
+uv run adscrub transcribe                                 # Whisper the rest
 uv run adscrub stats                                      # counts
 ```
 
-`transcribe` / `detect` / `cut` / `serve` are registered subcommands that report
-"not built yet" until their milestones land — see docs/PLAN.md.
+`detect` / `cut` / `serve` are registered subcommands that report "not built yet"
+until their milestones land — see docs/PLAN.md.
+
+Transcription runs CPU-only by default. `code` does have a real GPU (RTX 2070
+SUPER) and Docker here has the `nvidia` runtime registered, but that's only wired
+up for containers that request it — see `compose.gpu.yaml` and CLAUDE.md for the
+GPU deploy path (`uv sync --extra gpu` pulls in the cuBLAS/cuDNN libs faster-whisper
+needs for CUDA).
 
 The database defaults to `./adscrub.db`; override with `--db` or `$ADSCRUB_DB`.
 

compose.gpu.yaml

@@ -0,0 +1,18 @@
+# GPU override: requests the host's RTX 2070 SUPER via the nvidia Docker runtime
+# (confirmed registered on `code`, 2026-07-10 — see CLAUDE.md) and builds the image
+# with the cuBLAS/cuDNN extra so faster-whisper actually uses it.
+#
+#   docker compose -f compose.yaml -f compose.gpu.yaml build
+#   docker compose -f compose.yaml -f compose.gpu.yaml run --rm adscrub transcribe
+services:
+  adscrub:
+    build:
+      args:
+        GPU: "1"
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: ["gpu"]

compose.yaml

@@ -2,6 +2,7 @@
 #   docker compose run --rm adscrub add-feed https://feeds.example.com/show
 #   docker compose run --rm adscrub ingest
 #   docker compose run --rm adscrub chapters
+#   docker compose run --rm adscrub transcribe   # CPU by default — see compose.gpu.yaml
 #   docker compose run --rm adscrub stats
 services:
   adscrub:

docs/PLAN.md

@@ -20,17 +20,23 @@ Milestones. Each one ships something usable and gets a CHANGELOG version.
   Not yet tested against a real-world feed (only a synthetic fixture) — validate against
   an actual subscribed show before trusting this path in production.
 
-## M2 — transcription
+## M2 — transcription (done, 0.2.0)
 
-- Local Whisper (faster-whisper, CPU-first) for episodes with no usable chapters URL,
-  or where the chapters pass found nothing.
-- **Open question:** no CUDA device on `code` (confirmed 2026-07-10 — no `/dev/nvidia*`).
-  Decide whether M2 runs CPU-only here (slow but simple) or ships the transcription
-  step to a different homelab box with real GPU passthrough (adds a network hop and a
-  deployment target, but much faster). Don't guess — benchmark CPU-only first; only
-  build the remote-dispatch option if CPU throughput is actually a problem given
-  realistic episode volume.
-- Store transcript + word/segment timestamps; `episodes.transcript_path`.
+- Local Whisper (faster-whisper) for episodes with no usable chapters URL, or where
+  the chapters pass found nothing (`ad_segments.source = 'chapter'` absent).
+- Downloads episode audio to `data/audio/<id>.mp3` (cached, re-run safe), transcribes,
+  writes segment-level timestamps to `data/transcripts/<id>.json`.
+- **GPU:** `code` physically has an RTX 2070 SUPER and Docker here has the `nvidia`
+  runtime + CDI device registered (confirmed 2026-07-10) — resolved, this is not CPU-only
+  forever. Device selection is automatic at runtime via
+  `ctranslate2.get_cuda_device_count()` (no torch dependency needed just to check): CUDA
+  float16 if a device is visible, CPU int8 otherwise. The interactive dev shell itself
+  doesn't have the device nodes passed through, so plain `uv run adscrub transcribe`
+  here runs CPU — that's expected, not a bug. The Docker deploy path
+  (`compose.gpu.yaml` override) requests the GPU explicitly.
+- GPU runtime libs (cuBLAS/cuDNN) are an optional `gpu` extra
+  (`uv sync --extra gpu`) rather than a base dependency, since they're large,
+  CUDA-specific, and only needed on the deploy target.
 
 ## M3 — LLM ad-span classification
 
@@ -60,8 +66,6 @@ Milestones. Each one ships something usable and gets a CHANGELOG version.
 
 ## Open questions (owner input needed, don't block on these)
 
-- GPU/Whisper feasibility: CPU-only on `code`, or dispatch to a box with real
-  passthrough? (M2 decision — see above.)
 - Which Claude model for M3 classification (cost vs. accuracy on ad-span boundaries).
 - Real-world validation of the M1 chapters-URL parsing against an actual subscribed
   feed, not just the synthetic test fixture.

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "adscrub"
-version = "0.1.0"
+version = "0.2.0"
 description = "Self-hosted podcast ad-detection and removal proxy"
 readme = "README.md"
 requires-python = ">=3.12"
@@ -9,6 +9,16 @@ dependencies = [
     "feedparser>=6.0.11",
     "httpx>=0.28",
     "feedgen>=1.0.0",
+    "faster-whisper>=1.0",
+]
+
+# GPU runtime libs (cuBLAS/cuDNN) for faster-whisper's CUDA path. Large and
+# CUDA-specific, so kept out of the base install; `uv sync --extra gpu` on a
+# deploy target with real GPU passthrough (see CLAUDE.md).
+[project.optional-dependencies]
+gpu = [
+    "nvidia-cublas-cu12",
+    "nvidia-cudnn-cu12",
 ]
 
 [project.scripts]

src/adscrub/cli.py

@@ -1,4 +1,4 @@
-"""adscrub command line: add-feed, ingest, chapters, stats (transcribe/detect/cut/serve: M2+)."""
+"""adscrub command line: add-feed, ingest, chapters, transcribe, stats (detect/cut/serve: M3+)."""
 
 from __future__ import annotations
 
@@ -8,13 +8,12 @@ import sys
 
 import httpx
 
-from . import __version__, chapters, db, ingest
+from . import __version__, chapters, db, ingest, transcribe
 
 DEFAULT_DB = os.environ.get("ADSCRUB_DB", "adscrub.db")
 USER_AGENT = f"adscrub/{__version__} (homelab podcast ad-removal proxy)"
 
 _NOT_BUILT_YET = {
-    "transcribe": "M2",
     "detect": "M3",
     "cut": "M4",
     "serve": "M4/M5",
@@ -73,6 +72,35 @@ def cmd_chapters(args: argparse.Namespace) -> int:
     return 0
 
 
+def cmd_transcribe(args: argparse.Namespace) -> int:
+    conn = db.connect(args.db)
+    pending = transcribe.pending_episodes(conn, args.limit)
+    if args.dry_run:
+        total_pending = len(transcribe.pending_episodes(conn))
+        print(f"pending episodes: {total_pending}"
+              + (f" (would process {len(pending)} this run)" if args.limit else ""))
+        return 0
+    if not pending:
+        print("no episodes pending transcription", file=sys.stderr)
+        return 1
+
+    errors = 0
+    with make_client() as client:
+        for ep in pending:
+            try:
+                path = transcribe.transcribe_episode(
+                    conn, ep, client, model_size=args.model
+                )
+            except (httpx.HTTPError, OSError) as exc:
+                errors += 1
+                print(f"  FAIL  {ep['title']}: {exc}")
+                continue
+            print(f"  ok    {ep['title']} -> {path}")
+    remaining = len(transcribe.pending_episodes(conn))
+    print(f"transcribed {len(pending) - errors} episode(s) ({errors} failed, {remaining} still pending)")
+    return 1 if errors else 0
+
+
 def cmd_stats(args: argparse.Namespace) -> int:
     conn = db.connect(args.db)
     feeds = conn.execute("SELECT COUNT(*) FROM feeds").fetchone()[0]
@@ -119,6 +147,17 @@ def main(argv: list[str] | None = None) -> int:
     p = sub.add_parser("chapters", help="scan episodes' existing chapter markers for ad spans")
     p.set_defaults(func=cmd_chapters)
 
+    p = sub.add_parser(
+        "transcribe", help="transcribe episodes with no chapter-sourced ad spans"
+    )
+    p.add_argument("--limit", type=int, help="max episodes to process this run")
+    p.add_argument("--model", default=transcribe.DEFAULT_MODEL,
+                   help=f"faster-whisper model size (default: $ADSCRUB_WHISPER_MODEL or "
+                        f"{transcribe.DEFAULT_MODEL})")
+    p.add_argument("--dry-run", action="store_true",
+                   help="only report how many episodes are pending")
+    p.set_defaults(func=cmd_transcribe)
+
     p = sub.add_parser("stats", help="print database counts")
     p.set_defaults(func=cmd_stats)
 

src/adscrub/transcribe.py

@@ -1,17 +1,107 @@
 """M2: local transcription for episodes with no usable chapter markers.
 
-Not implemented yet. Intended backend: faster-whisper. This host has no
-CUDA device (`/dev/nvidia*` absent) so the first working version should
-assume CPU-only inference (int8 quantized small/base model) and treat GPU
-as an optional speed-up available only where real passthrough exists —
-see docs/PLAN.md M2 and the open question inherited from hark's own
-Whisper-feasibility note.
+Backend: faster-whisper. Device is auto-detected via
+`ctranslate2.get_cuda_device_count()` (no torch needed just for that check) —
+CUDA float16 if a GPU is visible to the process, CPU int8 otherwise. `code`
+physically has an RTX 2070 SUPER and Docker here has the `nvidia` runtime + a
+CDI device registered, but an interactive dev shell doesn't get the device
+nodes passed through, so this same code legitimately runs CPU in one context
+and GPU in another — see CLAUDE.md.
 """
 
 from __future__ import annotations
 
+import json
+import os
 import sqlite3
+from pathlib import Path
+
+import httpx
+
+from .db import utcnow
+
+DEFAULT_MODEL = os.environ.get("ADSCRUB_WHISPER_MODEL", "small")
+DEFAULT_DATA_DIR = Path(os.environ.get("ADSCRUB_DATA_DIR", "data"))
+
+_model = None
+_model_size = None
 
 
-def transcribe_episode(conn: sqlite3.Connection, episode: sqlite3.Row, audio_path: str) -> str:
-    raise NotImplementedError("M2: transcription pipeline not built yet — see docs/PLAN.md")
+def _pick_device() -> tuple[str, str]:
+    import ctranslate2
+
+    if ctranslate2.get_cuda_device_count() > 0:
+        return "cuda", "float16"
+    return "cpu", "int8"
+
+
+def load_model(model_size: str = DEFAULT_MODEL):
+    """Load (and cache) the Whisper model. Reloads only if model_size changes."""
+    global _model, _model_size
+    if _model is not None and _model_size == model_size:
+        return _model
+    from faster_whisper import WhisperModel
+
+    device, compute_type = _pick_device()
+    _model = WhisperModel(model_size, device=device, compute_type=compute_type)
+    _model_size = model_size
+    return _model
+
+
+def download_audio(client: httpx.Client, audio_url: str, dest: Path) -> Path:
+    """Fetch episode audio to dest if not already cached there."""
+    if dest.exists():
+        return dest
+    dest.parent.mkdir(parents=True, exist_ok=True)
+    tmp = dest.with_suffix(dest.suffix + ".part")
+    with client.stream("GET", audio_url) as resp:
+        resp.raise_for_status()
+        with open(tmp, "wb") as fh:
+            for chunk in resp.iter_bytes():
+                fh.write(chunk)
+    tmp.rename(dest)
+    return dest
+
+
+def transcribe_episode(
+    conn: sqlite3.Connection,
+    episode: sqlite3.Row,
+    client: httpx.Client,
+    data_dir: Path = DEFAULT_DATA_DIR,
+    model_size: str = DEFAULT_MODEL,
+) -> Path:
+    """Download (if needed), transcribe, store segment timestamps, update the episode row."""
+    audio_path = download_audio(
+        client, episode["audio_url"], data_dir / "audio" / f"{episode['id']}.mp3"
+    )
+    model = load_model(model_size)
+    segments, _info = model.transcribe(str(audio_path), vad_filter=True)
+    transcript = [
+        {"start": seg.start, "end": seg.end, "text": seg.text.strip()} for seg in segments
+    ]
+
+    transcript_path = data_dir / "transcripts" / f"{episode['id']}.json"
+    transcript_path.parent.mkdir(parents=True, exist_ok=True)
+    transcript_path.write_text(json.dumps(transcript, indent=2))
+
+    conn.execute(
+        "UPDATE episodes SET transcript_path = ?, status = 'transcribed', updated_at = ? WHERE id = ?",
+        (str(transcript_path), utcnow(), episode["id"]),
+    )
+    conn.commit()
+    return transcript_path
+
+
+def pending_episodes(conn: sqlite3.Connection, limit: int | None = None) -> list[sqlite3.Row]:
+    """Episodes with audio but no transcript yet, and no chapter-sourced ad spans
+    already found (that's the M1 fast path — no point transcribing those)."""
+    query = """
+        SELECT * FROM episodes
+        WHERE transcript_path IS NULL AND audio_url IS NOT NULL
+          AND id NOT IN (SELECT episode_id FROM ad_segments WHERE source = 'chapter')
+        ORDER BY id
+    """
+    if limit:
+        query += " LIMIT ?"
+        return conn.execute(query, (limit,)).fetchall()
+    return conn.execute(query).fetchall()

tests/test_cli.py

@@ -1,4 +1,4 @@
-from adscrub import cli, db
+from adscrub import cli, db, transcribe
 
 
 def test_add_feed_then_stats(tmp_path, capsys):
@@ -37,13 +37,57 @@ def test_chapters_with_nothing_to_scan_fails(tmp_path, capsys):
 
 def test_not_built_yet_commands_report_milestone(tmp_path, capsys):
     for name, milestone in [
-        ("transcribe", "M2"), ("detect", "M3"), ("cut", "M4"), ("serve", "M4/M5"),
+        ("detect", "M3"), ("cut", "M4"), ("serve", "M4/M5"),
     ]:
         rc = cli.main(["--db", str(tmp_path / "t.db"), name])
         assert rc == 1
         assert milestone in capsys.readouterr().err
 
 
+def test_transcribe_with_nothing_pending_fails(tmp_path, capsys):
+    rc = cli.main(["--db", str(tmp_path / "t.db"), "transcribe"])
+    assert rc == 1
+    assert "no episodes pending" in capsys.readouterr().err
+
+
+def test_transcribe_dry_run_reports_pending(tmp_path, capsys):
+    path = tmp_path / "t.db"
+    conn = db.connect(path)
+    conn.execute("INSERT INTO feeds (source_url) VALUES ('http://feed')")
+    conn.execute("INSERT INTO episodes (feed_id, guid, title, audio_url) VALUES (1, 'g1', 'ep', 'http://a/1.mp3')")
+    conn.commit()
+    conn.close()
+
+    rc = cli.main(["--db", str(path), "transcribe", "--dry-run"])
+    assert rc == 0
+    assert "pending episodes: 1" in capsys.readouterr().out
+
+
+def test_transcribe_success_path(tmp_path, capsys, monkeypatch):
+    path = tmp_path / "t.db"
+    conn = db.connect(path)
+    conn.execute("INSERT INTO feeds (source_url) VALUES ('http://feed')")
+    conn.execute("INSERT INTO episodes (feed_id, guid, title, audio_url) VALUES (1, 'g1', 'Ep One', 'http://a/1.mp3')")
+    conn.commit()
+    conn.close()
+
+    def fake_transcribe_episode(conn, ep, client, model_size=None):
+        conn.execute(
+            "UPDATE episodes SET transcript_path = 'x.json', status = 'transcribed' WHERE id = ?",
+            (ep["id"],),
+        )
+        conn.commit()
+        return "x.json"
+
+    monkeypatch.setattr(transcribe, "transcribe_episode", fake_transcribe_episode)
+
+    rc = cli.main(["--db", str(path), "transcribe"])
+    assert rc == 0
+    out = capsys.readouterr().out
+    assert "ok    Ep One -> x.json" in out
+    assert "transcribed 1 episode(s) (0 failed, 0 still pending)" in out
+
+
 def test_version(capsys):
     try:
         cli.main(["--version"])

tests/test_transcribe.py

@@ -0,0 +1,128 @@
+import json
+from dataclasses import dataclass
+
+import httpx
+import pytest
+
+from adscrub import db, transcribe
+
+AUDIO_URL = "https://example.com/audio/ep1.mp3"
+
+
+@dataclass
+class FakeSegment:
+    start: float
+    end: float
+    text: str
+
+
+@pytest.fixture
+def conn(tmp_path):
+    return db.connect(tmp_path / "test.db")
+
+
+def audio_client(calls):
+    def handler(request):
+        calls.append(str(request.url))
+        return httpx.Response(200, content=b"fake-mp3-bytes")
+
+    return httpx.Client(transport=httpx.MockTransport(handler))
+
+
+# --- download_audio ---
+
+
+def test_download_audio_fetches_once_then_caches(tmp_path):
+    calls = []
+    dest = tmp_path / "audio" / "1.mp3"
+    with audio_client(calls) as client:
+        path = transcribe.download_audio(client, AUDIO_URL, dest)
+        assert path == dest
+        assert dest.read_bytes() == b"fake-mp3-bytes"
+        transcribe.download_audio(client, AUDIO_URL, dest)
+    assert len(calls) == 1  # second call was a cache hit, no request made
+
+
+def test_download_audio_reports_http_errors(tmp_path):
+    def handler(request):
+        return httpx.Response(404)
+
+    dest = tmp_path / "audio" / "1.mp3"
+    with httpx.Client(transport=httpx.MockTransport(handler)) as client:
+        with pytest.raises(httpx.HTTPStatusError):
+            transcribe.download_audio(client, AUDIO_URL, dest)
+    assert not dest.exists()  # partial file renamed only on success
+
+
+# --- transcribe_episode (model mocked — no real Whisper inference in tests) ---
+
+
+class FakeModel:
+    def __init__(self, segments):
+        self._segments = segments
+
+    def transcribe(self, path, vad_filter=True):
+        return self._segments, object()
+
+
+def test_transcribe_episode_writes_transcript_and_updates_row(conn, tmp_path, monkeypatch):
+    conn.execute("INSERT INTO feeds (source_url) VALUES ('http://feed')")
+    conn.execute(
+        "INSERT INTO episodes (feed_id, guid, title, audio_url) VALUES (1, 'ep-1', 'Ep 1', ?)",
+        (AUDIO_URL,),
+    )
+    conn.commit()
+    ep = conn.execute("SELECT * FROM episodes WHERE guid = 'ep-1'").fetchone()
+
+    fake_segments = [FakeSegment(0.0, 5.0, " Welcome to the show "), FakeSegment(5.0, 8.0, "This is an ad")]
+    monkeypatch.setattr(transcribe, "load_model", lambda model_size=None: FakeModel(fake_segments))
+
+    calls = []
+    with audio_client(calls) as client:
+        path = transcribe.transcribe_episode(conn, ep, client, data_dir=tmp_path)
+
+    assert path == tmp_path / "transcripts" / f"{ep['id']}.json"
+    stored = json.loads(path.read_text())
+    assert stored == [
+        {"start": 0.0, "end": 5.0, "text": "Welcome to the show"},
+        {"start": 5.0, "end": 8.0, "text": "This is an ad"},
+    ]
+
+    row = conn.execute("SELECT transcript_path, status FROM episodes WHERE id = ?", (ep["id"],)).fetchone()
+    assert row["transcript_path"] == str(path)
+    assert row["status"] == "transcribed"
+
+
+# --- pending_episodes ---
+
+
+def test_pending_episodes_excludes_transcribed_and_chapter_covered(conn):
+    conn.execute("INSERT INTO feeds (source_url) VALUES ('http://feed')")
+    conn.executemany(
+        "INSERT INTO episodes (feed_id, guid, audio_url, transcript_path) VALUES (1, ?, ?, ?)",
+        [
+            ("no-audio", None, None),
+            ("already-transcribed", "http://a/1.mp3", "data/transcripts/2.json"),
+            ("needs-transcription", "http://a/3.mp3", None),
+            ("chapter-covered", "http://a/4.mp3", None),
+        ],
+    )
+    conn.execute(
+        "INSERT INTO ad_segments (episode_id, start_second, end_second, source)"
+        " SELECT id, 0, 10, 'chapter' FROM episodes WHERE guid = 'chapter-covered'"
+    )
+    conn.commit()
+
+    pending = transcribe.pending_episodes(conn)
+    assert [ep["guid"] for ep in pending] == ["needs-transcription"]
+
+
+def test_pending_episodes_respects_limit(conn):
+    conn.execute("INSERT INTO feeds (source_url) VALUES ('http://feed')")
+    conn.executemany(
+        "INSERT INTO episodes (feed_id, guid, audio_url) VALUES (1, ?, ?)",
+        [("a", "http://a/1.mp3"), ("b", "http://a/2.mp3")],
+    )
+    conn.commit()
+    assert len(transcribe.pending_episodes(conn, limit=1)) == 1
+    assert len(transcribe.pending_episodes(conn)) == 2

uv.lock

@@ -4,15 +4,22 @@ requires-python = ">=3.12"
 
 [[package]]
 name = "adscrub"
-version = "0.1.0"
+version = "0.2.0"
 source = { editable = "." }
 dependencies = [
     { name = "anthropic" },
+    { name = "faster-whisper" },
     { name = "feedgen" },
     { name = "feedparser" },
     { name = "httpx" },
 ]
 
+[package.optional-dependencies]
+gpu = [
+    { name = "nvidia-cublas-cu12" },
+    { name = "nvidia-cudnn-cu12" },
+]
+
 [package.dev-dependencies]
 dev = [
     { name = "pytest" },
@@ -21,10 +28,14 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "anthropic", specifier = ">=0.116.0" },
+    { name = "faster-whisper", specifier = ">=1.0" },
     { name = "feedgen", specifier = ">=1.0.0" },
     { name = "feedparser", specifier = ">=6.0.11" },
     { name = "httpx", specifier = ">=0.28" },
+    { name = "nvidia-cublas-cu12", marker = "extra == 'gpu'" },
+    { name = "nvidia-cudnn-cu12", marker = "extra == 'gpu'" },
 ]
+provides-extras = ["gpu"]
 
 [package.metadata.requires-dev]
 dev = [{ name = "pytest", specifier = ">=8.3" }]
@@ -70,6 +81,32 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b0/7b/90df4a0a816d98d6ea26f559d87836d494a2cf1fcf063be67df50a7bcc30/anyio-4.14.1-py3-none-any.whl", hash = "sha256:4e5533c5b8ff0a24f5d7a176cbe6877129cd183893f66b537f8f227d10527d72", size = 124875, upload-time = "2026-06-24T20:56:04.413Z" },
 ]
 
+[[package]]
+name = "av"
+version = "18.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ae/a4/570a5a35c8638aba01e739925846c35fdd6b0756a15526766d0a4dd3b7df/av-18.0.0.tar.gz", hash = "sha256:4ef7e72c3d3a872584a1215173b16e0226811037f40dcdbf75992631098df1ba", size = 4340222, upload-time = "2026-07-02T06:37:58.907Z" }
+wheels = [
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+    { url = "https://files.pythonhosted.org/packages/77/b3/2576a44b4f39c7462ced4c17fec04c756f7b0f3c5cb940d124173e417d6a/av-18.0.0-cp311-abi3-macosx_14_0_arm64.whl", hash = "sha256:35274c20d2ad3b4774fe632bcef2e34af79858ddf899352339cc3babbc13a484", size = 18175248, upload-time = "2026-07-02T06:37:01.741Z" },
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+]
+
 [[package]]
 name = "certifi"
 version = "2026.6.17"
@@ -79,6 +116,18 @@ wheels = [
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 ]
 
+[[package]]
+name = "click"
+version = "8.4.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/76/d4/81420972a676e8ffea40450d8c8c92943e7218a78fe9b64359836cc9876b/click-8.4.2.tar.gz", hash = "sha256:9a6cea6e60b17ebe0a44c5cc636d94f09bd66142c1cd7d8b4cd731c4917a15f6", size = 338000, upload-time = "2026-06-24T17:45:15.148Z" }
+wheels = [
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+]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -88,6 +137,38 @@ wheels = [
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+source = { registry = "https://pypi.org/simple" }
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+    { name = "numpy" },
+    { name = "pyyaml" },
+    { name = "setuptools" },
+]
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