ASR Transcribe to Text

Transcribe audio/video to speaker-labeled text. Local execution has two explicit routes. Long or unattended recordings use checkpointed whisper.cpp + Silero VAD blocks, then late-fuse pyannote speakers. Short/medium recordings may use the Qwen3-ASR + mlx-whisper alignment route. Neither route cuts ASR input at diarization turns; speaker attribution happens after continuous-context ASR.

Route before ASR: the transcript is the result, not the run

Before starting transcription, check the owning project's transcript catalog, external source index, and declared prior-work carriers for an existing canonical transcript using source ID, date, title, and entity terms. A verified human-reviewed/current transcript ends the task unless the user explicitly asked for a new independent comparison. Raw audio existing is not a reason to regenerate text that already exists.

When no canonical transcript exists:

1. For ordinary meeting/DJI recordings where cloud processing is allowed, use Feishu Minutes as the normal primary route (preprocess to a small M4A first).
2. Use local ASR when the user requires offline/privacy handling, Feishu is unavailable or failed, or the task explicitly needs an independent quality comparison. 2b. For a second, independent speaker timeline, use StepFun's async file endpoint via the stepfun-asr skill's asr_file.py. It is the only vendor diarization route here that does not share code or models with local pyannote, so agreement between the two is real corroboration rather than one tool agreeing with itself. Measured 2026-09-19 on a 10-minute film: both split the key turn identically, while Feishu Minutes mis-attributed it. The constraint is that it fetches a public URL — base64 is rejected — so it needs staging (see aliyun-multi-account.md); reach for it when an attribution is load-bearing, not for routine transcripts.
3. For non-meeting media or an explicit local/remote ASR request, choose the execution location by the audio-location rule below.

Do not run local ASR merely to make a two-route process look complete.

Choosing between them is usually not about speed — it's about where the audio already is. A remote GPU can be several times faster (a 4090 running vLLM measured ~61x realtime against ~15x for local MLX), but that gap is small change next to moving the files: transcription output is text, and text is ~10,000× smaller than the audio it came from (18.5 h of speech ≈ 330 K characters ≈ 1 MB, from ~2.6 GB of WAV). So:

Transcribe where the audio already lives, and move only the transcript.

Pulling a few hundred MB across a slow link to reach a faster GPU routinely costs more wall-clock than the entire transcription — measured once at 63 KB/s, which is over two hours for 500 MB, to save minutes of compute. If the recording is already on the remote box (it was recorded there, downloaded there, or lives in a share mounted there), run the ASR there and bring back the .txt.

Configuration persists in ${CLAUDE_PLUGIN_DATA}/config.json.

Speaker labels are the default. Every run produces [start-end] SPEAKER_xx: text

• CSV. Plain-text-only output is the opt-out (--no-diarization) for monologues, podcasts, or when you just want a summary — see Step 3. It also drops every timestamp: the plain-text path returns the session text and nothing else. A monologue that needs line-level timestamps (subtitles, jump-to-moment archives) runs the full pipeline and ignores the SPEAKER_00 labels.

One-time setup for diarization: pyannote is a gated HuggingFace model — it needs a token once (## Speaker Diarization & Identification below). First run without it FAILS with setup steps; after setup, full capability is permanent and auto-detected.

Step 0: Detect Platform and Load Config

cat "${CLAUDE_PLUGIN_DATA}/config.json" 2>/dev/null

If config exists, read values and proceed to Step 1.

If config does not exist, auto-detect platform first:

python3 -c "
import sys, platform
is_mac_arm = sys.platform == 'darwin' and platform.machine() in ('arm64', 'aarch64')
print(f'Platform: {sys.platform} {platform.machine()}')
print(f'Apple Silicon: {is_mac_arm}')
if is_mac_arm:
    print('RECOMMEND: local-mlx')
else:
    print('RECOMMEND: remote-api')
"

Then use AskUserQuestion with platform-aware defaults:

For macOS Apple Silicon (recommended: local):

ASR setup — your Mac has Apple Silicon, so local transcription is recommended.

Q1: Transcription mode?
  A) Local MLX — runs on your Mac's GPU, no API key needed, 15-27x realtime (Recommended)
  B) Remote API — send audio to a server (vLLM, Tailscale workstation, etc.)

Q2: Does your network have an HTTP proxy that might intercept traffic?
  A) Yes — bypass proxy for ASR traffic (Recommended if using Shadowrocket/Clash)
  B) No — direct connection

For other platforms (recommended: remote):

ASR setup — local MLX requires macOS Apple Silicon. Using remote API mode.

Q1: ASR Endpoint URL?
  A) https://asr.example.com/v1/audio/transcriptions (Self-hosted remote ASR)
  B) http://localhost:8002/v1/audio/transcriptions (Local ASR server)
  C) Custom URL

Q2: Proxy bypass needed?
  A) Yes (Recommended for Shadowrocket/Clash/corporate proxy)
  B) No

Save config:

mkdir -p "${CLAUDE_PLUGIN_DATA}"
python3 -c "
import json
config = {
    'mode': 'MODE',           # 'local-mlx' or 'remote-api'
    'model': 'MODEL_ID',      # local: 'mlx-community/Qwen3-ASR-1.7B-8bit', remote: 'Qwen/Qwen3-ASR-1.7B'
    'max_tokens': 8192,       # local only; PER 20-minute chunk, not per recording
    'endpoint': 'URL',        # remote only
    'noproxy': True,
    'max_timeout': 900        # remote only
    # 'diarization_declined': True  # set only after the user explicitly declines
    #   the pyannote setup in Step 3 — every run then warns + goes plain-text
    #   until an HF token appears (auto-detected)
}
with open('${CLAUDE_PLUGIN_DATA}/config.json', 'w') as f:
    json.dump(config, f, indent=2)
print('Config saved.')
"

Step 1: Resolve Input

Accept local files, direct media URLs, or web/podcast episode pages.

• Web or podcast page URL: inspect the page for an existing transcript first. Use an official/platform transcript only when it is directly accessible to the user's account. If the transcript endpoint requires a login token and none is available, say that clearly and fall back to ASR from the audio URL.
• Local file, direct media URL, or page URL fallback: run the bundled resolver. It extracts media from common page metadata (og:audio, media tags, JSON-LD, RSS-style enclosure links), downloads URLs with atomic temp-file replacement, verifies remote Content-Length when present, computes SHA-256, and validates the result with ffprobe.

uv run ${CLAUDE_SKILL_DIR}/scripts/resolve_media_input.py \
  INPUT_FILE_OR_URL [INPUT_FILE_OR_URL2 ...] \
  --output-dir OUTPUT_DIR \
  --manifest OUTPUT_DIR/media_manifest.json

For suspicious or high-value downloads, add --decode-check to make ffmpeg decode the whole file before transcription:

uv run ${CLAUDE_SKILL_DIR}/scripts/resolve_media_input.py \
  "https://www.xiaoyuzhoufm.com/episode/EPISODE_ID" \
  --output-dir OUTPUT_DIR \
  --manifest OUTPUT_DIR/media_manifest.json \
  --decode-check

Expected output:

Downloaded ... bytes in ...s -> OUTPUT_DIR/episode-title.m4a
OUTPUT_DIR/episode-title.m4a

Use the printed local path as INPUT_AUDIO in later steps. If your runtime shows the literal ${CLAUDE_SKILL_DIR} instead of a substituted path, resolve the skill directory per the Troubleshooting entry at the bottom of this document.

For third-party public podcasts or copyrighted media, save the transcript as a local file for the user's personal analysis. Do not paste a full long transcript into chat; provide a path, previews, summaries, or short excerpts instead.

Step 2: Extract Audio (if input is video)

For video files (mp4, mov, mkv, avi, webm), extract as 16kHz mono WAV:

ffmpeg -i INPUT_VIDEO -vn -acodec pcm_s16le -ar 16000 -ac 1 OUTPUT.wav -y

Audio files (wav, mp3, m4a, flac, ogg) can be used directly. Get duration:

ffprobe -v error -show_entries format=duration -of default=noprint_wrappers=1:nokey=1 INPUT_FILE

Cleanup: After transcription succeeds, delete extracted WAV files to save disk space.

Preprocess: Merge Segments & Shrink Metered Uploads (optional)

Run this BEFORE transcription when either applies:

• The recording is a multi-segment dump — body mics and field recorders split sessions into fixed-length files (e.g. TX02_MIC024_....wav, TX02_MIC025_....wav; TX01/TX02 = DJI MIC MINI 2S internal recording — device roster and the recorder→Feishu-Minutes paths: the meeting-ingest skill's meeting-ingest/references/architecture.md §①-L0). Merge them once to preserve session order; the explicit long-audio runner (Step 3 Path L) then owns stable source-time blocks. Transcribing device segments separately throws away cross-segment context inside those chunks.
• The audio goes to a metered ASR (Feishu Minutes, any per-minute quota) — a pitch-PRESERVED speedup cuts billed duration directly, and modern ASR does not care: 1.3x was user-verified on Feishu Minutes (2026-07-16) with no perceptible recognition difference, and public Whisper benchmarks show no sharp WER drop until 2.0x (≤1.5x = safe zone, ~3% WER increase at 1.5x; >2x unusable).

Use the bundled script — it merges, normalizes to 16 kHz mono, optionally speeds up, and verifies its own output instead of trusting the ffmpeg exit code:

uv run ${CLAUDE_SKILL_DIR}/scripts/prepare_asr_input.py SEG1.wav SEG2.wav -o merged.wav   # merge only
uv run ${CLAUDE_SKILL_DIR}/scripts/prepare_asr_input.py SEG*.wav -o upload.m4a --speed 1.3  # merge + quota-saving speedup

Expected output:

Merge order:
  1. SEG1.wav  [pcm_s24le 48000Hz ch=1 1800.14s]
  2. SEG2.wav  [pcm_s24le 48000Hz ch=1 1800.15s]
[OK] duration: 4946.19s vs expected 4946.18s (delta +0.00s)
[OK] boundary 1 @ 1384.7s: max_volume -15.5 dB
[info] overall: mean_volume -38.3 dB, max_volume 0.0 dB
Wrote upload.m4a

• Segments sort by the YYYYMMDD_HHMMSS timestamp embedded in their filenames when every file has one (recorder dumps do); otherwise the given order is kept with a note — eyeball the printed merge order before transcribing.

• Self-verification: output duration must equal Σsegments ÷ speed (±1.5 s, hard FAIL otherwise); each splice gets a 10 s volume spot-check (dead air at a boundary = wrong order or a missing segment); overall loudness prints for comparison with the source.

• Speedup must be atempo-style pitch-preserved stretch — never sample-rate trickery, which shifts pitch and breaks both ASR accuracy and diarization voiceprints.

• Pick the output format by destination — codec follows the file extension:

  Destination	Format	Why
  Local MLX pipeline (Path A)	.wav or .m4a	Both feed the pipeline directly (m4a verified 2026-07-18: a 3-min slice transcribed cleanly). M4A is ~5x smaller — 324 MB WAV → 63 MB M4A on a 2h49m merge, duration identical to the second
  Metered upload (Feishu Minutes, per-minute quota)	.m4a + --speed 1.3	AAC 48k is speech-transparent for ASR, ~30% smaller than mp3 at equal speech quality; speedup cuts billed duration ~23%
  Self-hosted vLLM endpoint (Path B)	.ogg	Accepted where MP3 is refused, and ~8× smaller than WAV — which is what keeps a long recording under the server's 25 MB request cap. See Path B's limits section
  Lossless archive	.flac	~50% of WAV, bit-perfect
  Only when the target rejects the above	.mp3	Compatibility fallback

• Keep the originals until the transcript passes Step 4 verification.

Option: Upload to Feishu Minutes for transcription

After preprocessing, use this path when the user wants Feishu Minutes, or when an ordinary meeting/DJI recording has no canonical transcript and cloud processing is allowed. It is the normal meeting-audio route, not a fallback that requires a failed local run first.

Trigger phrases: 传到妙记 / 上传到飞书妙记 / 让妙记转写 / create a minute from this audio / upload to Feishu minutes.

Track the requested outcome before uploading:

• Upload-only — the user explicitly says they only want a Minute link or only want the media uploaded. Creating minute_url is the terminal state.
• Transcript-only — the user wants the transcript/summary but no project filing. After upload, follow the current version-matched lark-minutes instructions through the ready transcript.
• Project delivery — the user wants correction, routing, a knowledge-base file, project indexes, or Git handoff. meeting-ingest owns the overall job; this skill is only its preprocessing participant. Switch to that orchestrator before upload and keep running until its delivery receipt is verified.
• Downstream unspecified — the request says to preprocess/upload (for example, “上传到飞书妙记，先转成适合 ASR 的格式”) but does not say what should happen after the Minute exists. Honor the requested upload without inventing transcript or project scope, then emit outcome_pending with the durable token/URL. A later transcript or knowledge-base request resumes that same Minute; it never starts over or depends on the original guess.

Do not infer upload-only merely because the user's first clause says “上传到妙记”. An explicit downstream outcome in the same request or project context wins.

Constraints:

• No proxy: all lark-cli calls must use LARK_CLI_NO_PROXY=1.
• Single profile: use the active Feishu profile only. Do not iterate tenant profiles or invoke tenant routing.
• No duplicate local ASR by default: Feishu owns transcription on this branch. Local ASR runs only for an explicit offline/comparison purpose.
• Load current Feishu guidance: use lark-cli-router, then read the version-matched lark-minutes upload/detail instructions. Do not copy a stale stop condition from this skill over the live CLI contract.

Step-by-step:

1. Use the already-preprocessed audio from the section above when possible. Feishu accepts .m4a, .mp3, .wav, .aac in an MP4/MOV wrapper; the preprocessor's "Metered upload" row is already shaped for this. Keep the file under 6 GB and under 6 hours — those are Feishu upload hard limits.

2. Upload to Drive as the user:

   LARK_CLI_NO_PROXY=1 lark-cli drive +upload \
     --file '<preprocessed-media-path>' \
     --name '<basename>' \
     --as user \
     --format json

   From the result, record file_token. If the command errors with a path validation or multipart failure, do not retry blindly — switch format or size strategy and try once more, then report the exact failure.

3. Create the minute from that Drive file:

   LARK_CLI_NO_PROXY=1 lark-cli minutes +upload \
     --file-token '<file_token>' \
     --as user \
     --format json

   From the result, record minute_token and minute_url.

4. Branch on the previously fixed terminal outcome:

   • Upload-only: return minute_url and stop.
   • Transcript-only: use the current lark-minutes detail command with its ready-wait behavior and --transcript. A created URL is intermediate; success requires a readable transcript artifact.
   • Project delivery: return this handoff tuple to meeting-ingest and continue in the same run: prepared_media, file_token, minute_token, minute_url, next_required_phase=minute_ready. The orchestrator waits for the cloud transcript, invokes sync-feishu-minutes for token-scoped ingest/routing/delegation, runs full transcript-fixer, updates every project-owned index, completes Git handoff, and records delivery.
   • Downstream unspecified: return minute_token, minute_url, outcome_pending, and next_required_phase=outcome_decision. Do not wait for or file a transcript until the user supplies that downstream outcome.

If readiness times out or a downstream stage blocks, report the exact last completed phase plus the durable minute_token/minute_url. Resume that same token; never re-upload just to obtain a fresh URL.

Expected output:

• Upload-only success: a single minute_url the user can open.
• Downstream-unspecified success: the requested upload is complete, while the run remains explicitly resumable at outcome_pending; do not call it project delivery.
• Transcript-only success: minute_url plus a readable transcript artifact.
• Project-delivery success: only the meeting-ingest pushed delivery receipt; preprocessing, URL creation, and transcript download are intermediate phases.
• Failure: exact API error from drive +upload or minutes +upload, plus one suggested next action.

Wrong-skill recovery: if this request lands while you are inside sync-feishu-minutes, choose by outcome: upload-only routes here; project delivery routes to meeting-ingest, which calls this preprocessing branch and then returns to token-scoped Feishu ingestion. Never silently downgrade project delivery to a Minute URL.

Step 3: Transcribe (speaker labels by default)

Path L: Long local recording — whisper.cpp + Silero VAD (default for >30 min)

Use this path for recordings longer than 30 minutes, unattended batch work, or any source likely to contain long silence / post-meeting ambient audio. It is the Apple-Silicon long-form route: source-time blocks are checkpointed, each block runs whisper.cpp's Silero VAD, 2-second overlap is de-duplicated at block seams, and pyannote speaker labels are late-fused afterward.

The four runtime assets are explicit operator inputs. Do not discover or download them silently inside a batch. whisper.cpp documents the model and VAD download scripts; verify the binary/model/VAD files before starting.

# 1. Checkpointed ASR on the original source timeline
uv run ${CLAUDE_SKILL_DIR}/scripts/transcribe_long_whispercpp.py \
  INPUT_16K_MONO_PCM16.wav OUTPUT_DIR \
  --ffmpeg-path /absolute/path/to/ffmpeg \
  --whisper-bin /absolute/path/to/whisper-cli \
  --whisper-model /absolute/path/to/ggml-large-v2-or-v3.bin \
  --vad-model /absolute/path/to/ggml-silero-v6.2.0.bin

# 2. Independent speaker timeline (controlled FFmpeg decode; no TorchCodec path)
uv run --frozen ${CLAUDE_SKILL_DIR}/scripts/diarize_speakers.py \
  INPUT_16K_MONO_PCM16.wav OUTPUT_DIR/STEM.diarization.json \
  --device mps --ffmpeg-path /absolute/path/to/ffmpeg

# 3. Keep only speech-grounded ASR segments and assign speakers by time overlap
uv run ${CLAUDE_SKILL_DIR}/scripts/fuse_whispercpp_diarization.py \
  OUTPUT_DIR/STEM.whispercpp.json \
  OUTPUT_DIR/STEM.diarization.json \
  INPUT_16K_MONO_PCM16.wav OUTPUT_DIR

If the physical recorder was left running after the business session ended, pass an evidence-backed source timestamp to both relevant steps (--end-at SECONDS). Keep the raw audio and unbounded ASR evidence, but do not force post-meeting car noise or silence into the meeting transcript.

Completion means the final TXT/CSV/fusion receipt exists and beginning, middle, end, and every outer block seam have been sampled. N/N blocks complete alone is not a quality claim. A rerun must report every completed block as cached.

Official architecture basis: whisper.cpp VAD extracts speech before inference; OpenAI Whisper resets previous-text context to avoid repetition loops; NeMo long-audio guidance uses overlapping buffered chunks. The project-specific measured evidence and parameter rationale live in references/speaker_diarization.md.

Path A: Local MLX (macOS Apple Silicon) — short/medium alternative

Run the decoupled speaker pipeline — it handles dependency pins, bounded per-chunk generation, resumable checkpoints, model loading, and process-tree cleanup internally.

Input extensions are not trusted as decoder contracts. The Qwen worker first uses the pinned MLX decoder; if that decoder reports a recognized input/container decode failure (for example miniaudio rejecting Ogg/Opus), it uses ffmpeg to create a temporary 16 kHz mono PCM WAV for the pinned default model (custom local models use their declared sample rate) and retries. GPU, memory, and unrelated runtime failures propagate unchanged and never trigger normalization. The checkpoint, output name, and provenance remain bound to the original source bytes; the temporary WAV is never a completion artifact.

uv run ${CLAUDE_SKILL_DIR}/scripts/speaker_transcribe.py \
  INPUT_AUDIO [INPUT_AUDIO2 ...] OUTPUT_DIR

Unattended batches: one file per invocation. A deterministic failure in any leg (for example ASR_DETERMINISTIC_BLOCKED: ChunkTokenLimitError when one recording hits the per-chunk token ceiling) exits the whole multi-input run by design, and the files queued after it are never started — a 31-file batch died on file 11 that way. Per-file calls contain the damage to the one recording; the intermediate legs are cached, so a rerun of the others costs only the alignment.

Expected output (per file):

Device: mps
+ uv run .../transcribe_local_mlx.py ...        (leg 1: session text)
Chunk 1/6 starting at 0.0s (max_tokens=8192)
Chunk 1/6 committed: 6310 chars, 3812 tokens
+ uv run .../word_timestamps_whisper.py ...     (leg 2: timing lattice)
... diarization ...                             (leg 3: pyannote segments)
STEM: 42 turns, speakers=['SPEAKER_00', 'SPEAKER_01'], anchored_ratio=0.93
Wrote STEM.txt, STEM.csv, STEM.alignment.json, STEM.receipt.json

Outputs per input: <stem>.txt ([MM:SS - MM:SS] SPEAKER_xx + text), <stem>.csv (file,start,end,duration,speaker,text — feeds review UIs and voiceprint ID), <stem>.diarization.json, <stem>.alignment.json (provenance

• anchored_ratio trust signal; < 0.5 prints a loud warning — verify labels against the audio before trusting them), and <stem>.receipt.json (the atomic completion record binding source bytes, all four final artifact hashes, producer scripts, pinned model/dependencies, and semantic parameters). Intermediate legs are cached in OUTPUT_DIR/_align/ so re-runs are cheap (--force redoes final legs). Each intermediate cache sidecar binds source-audio bytes, producer-script bytes, semantic parameters, and artifact bytes; file existence alone is never a cache hit. Downstream completion checks require the final receipt, not a non-empty artifact or alignment JSON alone. Qwen chunk checkpoints live below its staging directory; an interrupted run verifies the source-audio SHA-256, producer/splitter/dependency contract, immutable model revision, generation parameters, and completed chunk hashes, then skips completed chunks instead of starting the recording over. A language-agnostic 12-character n-gram guard rejects highly repetitive chunk or whole-session text before final delivery; the quality-policy ID is part of checkpoint identity, so older unchecked parts cannot silently bypass the guard.

Before using the Qwen3 route, smoke-test its leg once:

uv run ${CLAUDE_SKILL_DIR}/scripts/transcribe_local_mlx.py --smoke-test

Expected output includes Dependency stack: mlx-audio 0.3.1, mlx-lm 0.30.5, transformers 5.0.0rc3 and Smoke test OK. For performance, per-chunk token semantics, resource bounds, and recovery, read references/local_mlx_guide.md.

How it works (and why): session-wide Qwen3-ASR text + mlx-whisper word timestamps + pyannote speaker segments, aligned after the fact. It avoids the quality-damaging old cascade that transcribed each diarized speaker turn in isolation. Do not promote its bounded generation chunks into a long-form guarantee: a real multi-hour recording reached the token ceiling at 20, 10, and 5 minute windows. Architecture, alignment algorithm, and failure modes: references/decoupled_speaker_alignment.md.

First run: pyannote needs a one-time HuggingFace token. If the script exits with the setup hint (exit code 3), STOP and use AskUserQuestion:

Speaker diarization needs a one-time setup (gated model, free):
  1. Accept terms at https://hf.co/pyannote/speaker-diarization-3.1
  2. Run `huggingface-cli login` (or set HF_TOKEN)

Options:
A) Set it up now — I'll wait, then rerun with full speaker labels (Recommended)
B) Continue without speakers this time — plain text only

• A → after the user confirms login, rerun the same command. The token is auto-detected every run; full capability is permanent from then on.
• B → persist the choice (diarization_declined: true in config.json) and rerun the SAME command. The script detects the flag, prints a one-line warning with the two setup steps, and auto-falls back to plain text for that run — no need to pass --no-diarization (the fallback is automatic now, enforced in the script not just the doc). The same warn-and-continue happens on every later run while the token is still missing. When a token later appears, diarization resumes automatically (the flag is ignored once a token is present) — mention this so the user knows setup is all that's needed.

Plain-text fast path (monologue, podcast, "just summarize it"):

uv run ${CLAUDE_SKILL_DIR}/scripts/speaker_transcribe.py \
  INPUT_AUDIO OUTPUT_DIR --no-diarization

Remote/pre-made ASR text (e.g. from Path B, or another ASR service): skip the Qwen3 leg and align that text instead. --text-file pairs ONE transcript with ONE input wav — passing multiple inputs is rejected (one transcript can't be aligned to several files):

uv run ${CLAUDE_SKILL_DIR}/scripts/speaker_transcribe.py \
  INPUT_AUDIO OUTPUT_DIR --text-file TRANSCRIPT.txt

Non-Apple-Silicon machines: the whisper timing leg is MLX-only. Without it there is no timing lattice to align speakers onto — run with --no-diarization and tell the user speaker mode currently requires Apple Silicon (cloud ASR with built-in diarization, e.g. Feishu Minutes, is the no-local-GPU alternative).

Before batching many short files (promo clips, montage cuts — anything that may contain music-only audio), read ## Batch Transcription (many short files) below: one music-only clip can stall the whole batch for 10+ minutes.

Path B: Remote API

The remote endpoint returns plain text only — speakers are added locally by aligning that text (leg 1) with the local timing + diarization legs. So Path B = fetch text remotely, then run Path A's pipeline with --text-file.

Health check first (skip if already verified this session):

python3 -c "
import json, subprocess, sys
with open('${CLAUDE_PLUGIN_DATA}/config.json') as f:
    cfg = json.load(f)
base = cfg['endpoint'].rsplit('/audio/', 1)[0]
noproxy = ['--noproxy', '*'] if cfg.get('noproxy', True) else []
result = subprocess.run(
    ['curl', '-s', '--max-time', '10'] + noproxy + [f'{base}/models'],
    capture_output=True, text=True
)
if result.returncode != 0 or not result.stdout.strip():
    print(f'HEALTH CHECK FAILED: {base}/models', file=sys.stderr)
    sys.exit(1)
print(f'Service healthy: {base}')
"

Read config and send via curl:

python3 -c "
import json, subprocess, sys, os, tempfile
with open('${CLAUDE_PLUGIN_DATA}/config.json') as f:
    cfg = json.load(f)
noproxy = ['--noproxy', '*'] if cfg.get('noproxy', True) else []
timeout = str(cfg.get('max_timeout', 900))
audio_file = 'AUDIO_FILE_PATH'
output_json = tempfile.mktemp(suffix='.json', prefix='asr_')

result = subprocess.run(
    ['curl', '-s', '--max-time', timeout] + noproxy + [
        cfg['endpoint'],
        '-F', f'file=@{audio_file}',
        '-F', f'model={cfg[\"model\"]}',
        '-o', output_json
    ], capture_output=True, text=True
)

with open(output_json) as f:
    data = json.load(f)
if 'text' not in data:
    print(f'ERROR: {json.dumps(data)[:300]}', file=sys.stderr)
    sys.exit(1)
text = data['text']
print(f'Transcribed: {len(text)} chars', file=sys.stderr)
print(text)
os.unlink(output_json)
" > OUTPUT.txt

Then attach speakers locally (Apple Silicon + pyannote token required):

uv run ${CLAUDE_SKILL_DIR}/scripts/speaker_transcribe.py \
  INPUT_AUDIO OUTPUT_DIR --text-file OUTPUT.txt

Self-hosted vLLM: the limits that fail in confusing ways

Version matters here — two of these changed between releases. Behavior below was measured end-to-end against vLLM 0.15.2rc1.dev68 (a dev build; there is no 0.15.2 release — PyPI goes 0.15.1 → 0.16.0) serving Qwen/Qwen3-ASR-1.7B, then re-read against the v0.26.0 sources. Check your own version first — pip show vllm — and read the version notes on #1 and #3.

1. Send OGG, not WAV — and never MP3. MP3 is rejected outright on 0.15.x, but the reflex fix (convert to WAV) is what walks you into the size cap in #2:

OGG is ~8× smaller than WAV at the same sample rate:

ffmpeg -nostdin -v error -i INPUT -ar 16000 -ac 1 -c:a libvorbis OUTPUT.ogg

Pin the bit depth when you compare formats yourself — decoding a lossy source leaves ffmpeg free to widen it, and a 24-bit FLAC comes out larger than 16-bit PCM, which reads as "FLAC doesn't compress" when the two simply weren't the same recording. Add -sample_fmt s16.

The MP3 rejection is worth recognizing because it arrives as HTTP 200 with an error body — a check that only inspects %{http_code} reports success:

HTTP=200
{"error": {"message": "Error opening <_io.BytesIO object>: Format not recognised.", ...}}

Version note: on 0.15.x the upload is read via librosa/soundfile on a BytesIO, which refuses MP3 there even where the host's libsndfile handles MP3 on disk. v0.26.0 added a pyav fallback after a soundfile LibsndfileError (multimodal/media/audio.py), so MP3/M4A likely decode on current releases — but OGG stays the better choice for the size reason above.

2. Requests are capped at 25 MB.

{"error":{"message":"Maximum file size exceeded (parameter=audio_filesize_mb, value=28.6)",...}}

VLLM_MAX_AUDIO_CLIP_FILESIZE_MB defaults to 25 (vllm/envs.py, unchanged from 0.15.1 through v0.26.0). At OGG's ~245 KB/min that ceiling arrives around 100 minutes — comfortably past a meeting, but a full-day recording or a merged multi-segment dump will cross it. Raise it when the job is long enough to matter:

VLLM_MAX_AUDIO_CLIP_FILESIZE_MB=800 vllm serve <model> --port <port> ...

3. v0.26.0 added a second, independent limit: 10 minutes of audio. Raising the size cap does not lift it — they are separate gates, and this one rejects rather than truncates:

Audio exceeds maximum allowed duration of 600s (metadata reports 5998.0s).
Set VLLM_MAX_AUDIO_DECODE_DURATION_S to increase this limit.

VLLM_MAX_AUDIO_DECODE_DURATION_S defaults to 600 and sits on the line right after the size cap in envs.py — it does not exist in 0.15.x, so a lecture-length file that works on an older server gets refused by a freshly-installed one. On v0.26.0+ set both:

VLLM_MAX_AUDIO_CLIP_FILESIZE_MB=800 VLLM_MAX_AUDIO_DECODE_DURATION_S=36000 \
  vllm serve <model> --port <port> ...

4. On a host that can't reach huggingface.co, model loading fails even when the model is already cached locally. vLLM issues a HEAD for config.json at startup, retries five times, then exits — the error says "couldn't find them in the cached files" even though they are right there:

HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 vllm serve <model> ...

Same symptom, different cause worth ruling out first: a containerized server has its own HF_HOME and cannot see the host user's ~/.cache/huggingface, so a model you can ls is genuinely absent from its view.

5. vLLM already chunks long audio — better than a client-side splitter would. SpeechToTextConfig carries overlap_chunk_second=1 and min_energy_split_window_size=1600, i.e. it splits at the quietest point inside a ~100 ms window rather than at a fixed offset, so cuts land between words. Once the caps above are lifted, a 100-minute file goes in one request. This is why the Step 5 fallback below is scoped to servers that don't do this.

No permission to restart the server? The caps in #2/#3 are set at server start, so when you can't touch it, splitting client-side is what's left — that is Step 5, and on such an endpoint it is the right tool rather than a fallback.

⚠️ But overlap_merge_transcribe.py cannot drive a 0.15.x vLLM endpoint as-is: it cuts chunks with -acodec copy into chunk_NN.mp3, so it emits MP3 (rejected per #1) whenever the input already is MP3, and simply fails on any other input — it never checks ffmpeg's exit status, so a bad chunk surfaces later as a JSON parse error rather than as "ffmpeg failed". The chunks also live and die inside one TemporaryDirectory, so there is no point at which you could convert them. Against such an endpoint, split manually into OGG and post each piece:

ffmpeg -nostdin -v error -i INPUT -f segment -segment_time 900 \
  -ar 16000 -ac 1 -c:a libvorbis chunk_%02d.ogg

Note this loses the overlap-merge stitching, so sentences may break at the seams — which is exactly what the server-side energy-based splitter in #5 exists to avoid.

If remote health check fails, diagnose in order:

1. Network: ping -c 1 HOST or tailscale status | grep HOST
2. Service: tailscale ssh USER@HOST "curl -s localhost:PORT/v1/models"
3. Proxy: retry with --noproxy '*' toggled

4. "Is anything actually listening?" — ss alone will lie to you. It shows only your own user's processes, so a server running as another user or inside a container is invisible to it while happily serving traffic. Ask Docker in the same breath:

tailscale ssh USER@HOST "ss -ltn | grep -E ':(8000|8001|8002)'; \
  docker ps --format '{{.Names}}\t{{.Ports}}\t{{.Status}}'"

5. "Is the GPU free?" — before starting another server, check whether one is really holding VRAM. An empty compute-apps list means nothing is using it, regardless of what an older note may claim about which service "has" the GPU:

tailscale ssh USER@HOST "nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv"
# under WSL nvidia-smi is often off PATH: /usr/lib/wsl/lib/nvidia-smi

6. Restarting it? pkill -f 'vllm serve' kills the command that issued it. -f matches against the whole command line — and the command line you just typed contains that exact string, so pkill matches your own shell. Symptom: the old process dies, the new one never starts, and nothing reports an error. Wrap the first letter in a character class so the pattern cannot match itself:

tailscale ssh USER@HOST "pgrep -f '[v]llm serve'"   # check
tailscale ssh USER@HOST "pkill -f '[v]llm serve'"   # kill

The same trap applies to any pkill -f whose pattern you also typed on that line.

Step 4: Verify Output

After transcription, check completeness:

1. Confirm output is not empty
2. Check character count is plausible (~400 chars/min for Chinese, ~200 words/min for English)
3. For local MLX, confirm the checkpoint manifest says status: complete; the script refuses to write the final .txt when any chunk hits its token ceiling
4. For legacy/remote output, check the ending — a mid-sentence tail can mean truncation
5. Show user the first and last ~200 characters as preview
6. Speaker path: check the alignment report — anchored_ratio should be ≥ 0.5 (the script warns when lower), the speaker count should be plausible for the recording (a two-person interview showing 5 speakers, or a monologue split into 2+, means diarization over-segmented — see references/speaker_diarization.md for when to distrust labels)

When this run is the independent evidence track for an existing high-stakes transcript, completeness means the entire baseline recording reached a complete checkpoint/final receipt. Selected clips can settle selected utterances, but they cannot support a whole-transcript or “higher-quality final” claim. Hand the complete output to transcript-fixer; let its human gate settle unresolved proper-name forks.

If truncated or wrong, use AskUserQuestion:

Transcription may be truncated:
- Expected: ~[N] chars for [M] minutes of audio
- Got: [actual] chars ([pct]% of expected)
- Last line: "[last 100 chars...]"

Options:
A) Inspect the failed chunk; if it is dense real speech, retry with a measured
   per-chunk budget up to 16384
B) If it is music/silence repetition, classify or preprocess that chunk instead
C) Switch mode — try [local/remote] instead
D) Abort

Step 5: Fallback — Overlap-Merge (Remote API Only)

Check whether your server chunks internally before reaching for this. vLLM does (Path B limit #5), and its energy-based split beats this script's fixed-offset one — so on a vLLM endpoint you control, a too-long file is fixed by lifting the caps rather than by splitting client-side.

Chunk client-side when the endpoint can't take the whole file: it rejects long audio outright (fixed context window, hard per-request duration limit), it OOMs at the same input length every time, or it does chunk internally but you have no permission to raise its caps.

A timeout is a different failure and usually has a cheaper fix — the request was accepted and was still running. Raise max_timeout in the config first (a 100-minute file at ~60× realtime still needs a couple of minutes, and the default can be tighter than that); reach for chunking only if it times out with a generous ceiling, which means the server is genuinely too slow for one pass.

When one of those applies, fall back to chunked transcription:

python3 ${CLAUDE_SKILL_DIR}/scripts/overlap_merge_transcribe.py \
  --config "${CLAUDE_PLUGIN_DATA}/config.json" \
  INPUT_AUDIO OUTPUT.txt

Splits into 18-minute chunks with 2-minute overlap, merges using punctuation-stripped fuzzy matching. See references/overlap_merge_strategy.md for algorithm details.

For local MLX mode, overlap-merge is unnecessary — the bundled script uses the pinned Qwen low-energy splitter, commits every chunk atomically, and defaults to max_tokens=8192 per chunk.

Step 6: Recommend Transcript Correction

ASR output always contains recognition errors — homophones, garbled technical terms, broken sentences. After successful transcription, proactively suggest running the transcript-fixer skill on the output:

Transcription complete: [N] chars saved to [output_path].

ASR output typically contains recognition errors (homophones, garbled terms, broken sentences).
Would you like me to run /daymade-audio:transcript-fixer to clean up the text?

Options:
A) Yes — run daymade-audio:transcript-fixer on the output now (Recommended)
B) No — the raw transcription is good enough for my needs
C) Later — I'll run it myself when ready

If the user chooses A, invoke the transcript-fixer skill with the output file path. The two skills form a natural pipeline: transcribe → correct → review.

If the user already requested correction, multi-track merging, or a higher-quality transcript in the same turn, that request already selects A. Continue into transcript-fixer without asking the user to approve the same work again.

Reconfigure

rm "${CLAUDE_PLUGIN_DATA}/config.json"

Then re-run Step 0.

Batch Transcription (many short files)

Passing many files to one transcribe_local_mlx.py invocation is efficient (model loads once) — but only when every file contains actual speech. If the batch may include music-only / BGM-only clips (short promo videos, montage clips with subtitles instead of voiceover), do NOT batch them in one process:

• On music/rhythm-only audio the model can fall into a repetition loop hallucination (e.g. endless "One, two, three, one, two, three..."). The 8192-token per-chunk ceiling bounds resource growth, while the 12-character n-gram quality gate rejects a loop even when it stops below that ceiling; one bad file can still consume the whole chunk timeout and starve a batch.
• Drive batch jobs one-file-per-process with a per-file timeout (e.g. timeout 240 / perl -e 'alarm 240; exec @ARGV' around each invocation, skip on timeout, second pass for failures). A stuck file then costs 4 minutes, not the batch.
• For a stuck file, retry with --max-tokens 3000: real speech in a short clip fits comfortably; a looping file gets truncated output you can classify.
• Detect "no speech" instead of shipping garbage: if the transcript's unique-word ratio is extremely low (e.g. len(set(words))/len(words) < 0.06 on a 40+ char output), the clip almost certainly has no voiceover — label it as such rather than delivering the loop text. (Downstream OCR of on-screen captions is the actual fix for subtitle-only videos.)

Word-Level Timestamps (subtitles, audio-visual alignment)

mlx-whisper's word timing is the timing leg of the short/medium Qwen speaker pipeline (leg 2 — scripts/word_timestamps_whisper.py runs it automatically). This section is for using word timestamps STANDALONE: subtitle generation, aligning narration to shot boundaries, per-clip captioning.

Qwen3-ASR is an LLM-decoder ASR: it emits plain text with no alignment information, on both local and remote paths. When the task needs to know when each word is spoken, use mlx-whisper with word_timestamps=True. Whisper's cross-attention word alignment is the de-facto local solution for this class of task.

Key facts (full recipe in references/whisper_word_timestamps.md):

• Model: mlx-community/whisper-large-v3-turbo (~1.6GB). Its Chinese WER is higher than Qwen3-ASR for pure transcription, but for alignment tasks Qwen3-ASR is not an option at all; prime domain terms via initial_prompt.
• Segment granularity trap: on short videos (15–40s) whisper often returns the whole clip as one segment — always work from the word list and assign words to time windows by midpoint.
• Pairs with ffmpeg scene detection (select='gt(scene,0.3)') for the visual side; avoid PySceneDetect on non-ASCII paths.

Speaker Diarization & Identification (who said what)

Speaker labels are the DEFAULT output of Step 3. Both local routes decouple ASR from pyannote and fuse by time; neither transcribes speaker-turn cuts in isolation. This section covers the pieces.

• Short/medium pipeline — scripts/speaker_transcribe.py runs all three legs + alignment in one command and writes the speaker-labeled transcript + CSV. Architecture, alignment algorithm, trust signals (anchored_ratio), and failure modes: references/decoupled_speaker_alignment.md. Production pitfalls (over-segmentation, mic-domain effects, when to distrust labels): references/speaker_diarization.md.
• Diarization alone — scripts/diarize_speakers.py emits just the speaker × time segments (no transcription).
• Legacy cascade — scripts/speaker_transcribe_cascade.py is the old cut-then-transcribe variant (diarize → slice audio per turn → ASR each slice). It breaks ASR context at every cut and lowers text quality; kept only for extremely noisy / heavy-overlap audio where per-slice isolation of a dominant near-field speaker beats session-level ASR. Everything else uses the decoupled default.
• Voiceprint identification — diarization labels are anonymous (SPEAKER_00…) and per-file. To map them to real names, unify a speaker across files, or collapse diarization's over-segmentation, use CAM++ voiceprints via scripts/voiceprint_id.py. Recipe and the critical acoustic-domain caveat — a voiceprint built from one mic type matches the same person on a different mic far less well: references/voiceprint_speaker_id.md.

One-time pyannote setup (gated model): accept terms at hf.co/pyannote/speaker-diarization-3.1, then huggingface-cli login once (or set HF_TOKEN). Auto-detected on every run afterward.

Transcript Audit & Review (HTML)

After diarization you get a CSV per file (file,start,end,duration,speaker,text). The bundled audit HTML generator turns those CSVs into a single, reader-first review page with audio playback, per-turn flags/notes, speaker aliasing, and export.

Generate it from a speaker-transcribe output directory:

uv run ${CLAUDE_SKILL_DIR}/scripts/generate_audit_html.py \
  OUTPUT_DIR \
  --output OUTPUT_DIR/audit/index.html \
  --audio-dir /path/to/original/audio

Defaults assume a flat layout under PROJECT_DIR: PROJECT_DIR/*.csv transcripts, PROJECT_DIR/*.diarization.json, and the original audio files placed next to the outputs. speaker_transcribe.py itself writes the CSV, TXT, and diarization files flat under its OUTPUT_DIR. If your project uses a different structure, override any of those paths:

uv run ${CLAUDE_SKILL_DIR}/scripts/generate_audit_html.py \
  /path/to/project \
  --output /path/to/project/audit/index.html \
  --csv-dir /path/to/project/csv \
  --txt-dir /path/to/project/txt \
  --diarization-dir /path/to/project/diarization \
  --audio-dir /path/to/project/audio \
  --original-dir /path/to/project/original \
  --manifest /path/to/project/manifest.json \
  --title "Project Audit" \
  --subtitle "Speaker-labeled transcript review" \
  --storage-key "project-audit" \
  --known-speaker "Speaker A" \
  --known-speaker "Speaker B"

Key CLI options:

The output is a single self-contained HTML file with no external dependencies. Open it in a browser to review, flag, and annotate turns; the export button produces a report of all flagged rows with reasons and notes.

Troubleshooting

Local MLX fails while loading the model

If model loading fails with an error like:

AttributeError: 'str' object has no attribute '__module__'

the agent is probably using an unpinned or stale copy of the local MLX script. The known-good stack is:

mlx-audio 0.3.1
mlx-lm 0.30.5
transformers 5.0.0rc3

Run the bundled --smoke-test command and confirm the dependency stack line matches. Do not start a long transcription until the smoke test succeeds.

A self-hosted remote endpoint rejects the audio

Each of these points away from its real cause, which is why they are worth recognizing by symptom. Full detail and fixes: Path B's "Self-hosted vLLM: the limits that fail in confusing ways" section.

The whisper timing leg dies with httpx.ProxyError: 503 Service Unavailable

word_timestamps_whisper.py asks huggingface.co about the model before loading it from the local cache. Behind a proxy tunnel that is flapping, that request fails and the leg exits, which fails the whole file even though every model file is already on disk (6 of 44 files in one batch). Set HF_HUB_OFFLINE=1 for the run: offline mode loads straight from the cache and never opens the connection. Same variable, same reason as the vLLM server row above.

${CLAUDE_SKILL_DIR} is not substituted

Script paths in this skill use ${CLAUDE_SKILL_DIR} — the skill's own directory, which Claude Code substitutes when the skill loads. If a command reaches you with the literal ${CLAUDE_SKILL_DIR} (some runtimes don't substitute), resolve the skill directory in this order:

1. The skill-load envelope: Base directory for this skill: <path> → <path> is the skill directory.
2. No envelope → find candidates and pick the one this session's available-skills list points to (installed copies can lag a source checkout): find ~/.claude ~/.claude-profiles ~/.codex ~/workspace -maxdepth 7 -type d -name asr-transcribe-to-text 2>/dev/null | head -5

Substitute the resolved absolute path for ${CLAUDE_SKILL_DIR} everywhere in this document.

Bundled Resources

Scripts:

• resolve_media_input.py — Resolve local paths, direct media URLs, and podcast/web pages into validated local media files
• prepare_asr_input.py — Merge multi-segment recordings + normalize for ASR (16 kHz mono), optional pitch-preserved speedup for metered uploads; self-verifies duration math and splice boundaries
• transcribe_local_mlx.py — Local MLX transcription (macOS ARM64, PEP 723 deps), bounded low-energy chunks, atomic checkpoints/resume, owner-liveness binding
• transcribe_long_whispercpp.py — DEFAULT LONG-AUDIO ASR: explicit source-time blocks + overlap ownership + whisper.cpp/Silero VAD + atomic checkpoint/resume
• fuse_whispercpp_diarization.py — Late-fuse normalized whisper.cpp time segments with pyannote speech/speakers; remove ungrounded silence hallucinations and emit TXT/CSV/receipt
• speaker_transcribe.py — Short/medium decoupled pipeline (session-wide Qwen3-ASR + whisper timing + pyannote); --no-diarization plain-text fast path; --text-file for remote/pre-made ASR text
• align_speakers.py — Decoupled alignment core (stdlib): maps full transcript onto whisper word lattice + pyannote segments; cuts turns at pauses and speaker changes but never inside a Latin word; usable standalone for debugging
• word_timestamps_whisper.py — mlx-whisper word-level timestamps → JSON timing lattice (Apple Silicon)
• speaker_transcribe_cascade.py — LEGACY cut-then-transcribe variant (extremely noisy / heavy-overlap audio only)
• diarize_speakers.py — Speaker diarization alone (pyannote 3.1 @ MPS) → per-segment JSON
• voiceprint_id.py — CAM++ voiceprint enroll/match: map anonymous SPEAKER_xx to real names
• overlap_merge_transcribe.py — Chunked transcription with overlap merge (remote API fallback)
• generate_audit_html.py — Build a self-contained HTML audit/review page from speaker-transcribe CSV outputs

References:

• decoupled_speaker_alignment.md — The default architecture: why decouple, alignment algorithm, trust signals, failure modes
• speaker_diarization.md — Production pitfalls: over-segmentation, mic-domain effects, when to distrust labels; legacy cascade notes
• voiceprint_speaker_id.md — CAM++ speaker ID: enroll/match, threshold+margin gates, the acoustic-domain caveat, bootstrap
• local_mlx_guide.md — Performance benchmarks, per-chunk token/resource contract, checkpoint recovery, model compatibility
• whisper_word_timestamps.md — mlx-whisper word timing: the timing leg of the short/medium Qwen pipeline; standalone subtitle/AV-alignment recipe
• overlap_merge_strategy.md — Why naive chunking fails, fuzzy merge algorithm