asr-transcribe-to-text

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkDeclared6 observations
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

it fetches a public URL — base64 is rejected — so it needs staging (see `aliyun-multi-account.md`)

it fetches a public URL — base64 is rejected — so it needs staging (see `aliyun-multi-account.md`)

AGENTS.md

exec(compile(ast.Module(body=[node], type_ignores=[]), "gpu_guard", "exec"), namespace)

base = f"http://127.0.0.1:{server.server_port}"

Audit 0.4.1 · source sha 9cc14bf9d5fa · 20 Oct 2026Report an issue or request a re-scan

• Publisherdaymade GitHub userOwner of the repository this skill is read from
• Source folderdaymade/claude-code-skills/daymade-audio/asr-transcribe-to-textThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict9cc14bf9d5faA new audit runs when the source changes
• Repository statusNot archivedPushed today
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Transcribes audio or video to speaker-labeled, timestamped text, locally with MLX on Apple Silicon or remotely. Use for 转录 / 录音转文字 / 说话人分离 / 字幕, and also for preparing audio for ASR without transcribing: 转格式, 降采样到…

---
name: asr-transcribe-to-text
description: >-
  Transcribes audio or video to speaker-labeled, timestamped text, locally with MLX on Apple Silicon
  or remotely. Use for 转录 / 录音转文字 / 说话人分离 / 字幕, and also for preparing audio for ASR without
  transcribing: 转格式, 降采样到 16kHz, merging recorder segments, or compressing and speeding up audio
  before 飞书妙记 — even when it looks like a one-line ffmpeg job.
argument-hint: "[audio-or-video-file-path-or-url ...]"
---

# 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.

Before choosing local versus remote execution, read
[execution location and comparison](references/execution_location_and_comparison.md).
Use existing text and validated checkpoints first, then compare source placement,
processing permission, actual GPU/model identity and end-to-end quality evidence.
When measurements are missing, prefer an eligible route next to the audio and
state that this avoids a transfer; do not claim it is faster.

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

```bash
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:

```bash
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 supports local MLX; choose from data location and the execution SOP.

Q1: Transcription mode?
  A) Local MLX — runs on your Mac's GPU, no API key needed
  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:
```bash
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 a

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

1. 2026-10-20
   !CAUTION B 89 / 100

   first audit · source 9cc14bf9d5fa

Questions about asr-transcribe-to-text

• github.com/daymade/claude-code-skills/tree/main/daymade-audio/as — the audited source at commit 9cc14bf9d5fa and every finding aboveObserved · 20 Oct 2026

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