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name: transcribe description: This skill should be used when the user wants to "transcribe audio", "transcribe video", "speech to text", "convert audio to text", "transcription", or needs to extract text from audio/video files. Default engine is 妙记 (Volcano Lark Minutes ASR) for best speaker diarization; Gemini/OpenAI available as fallbacks. Supports files up to ~8.4 hours with speaker diarization.

Audio Transcription Skill (妙记 / Gemini / OpenAI)

Overview

Transcribe audio and video files. Three providers, default 妙记 (Volcano Lark Minutes ASR, volc.lark.minutes):

Provider	When	Diarization
lark (默认 妙记)	Default — best speaker accuracy, single call	Server-side, most accurate
gemini	Fallback — no Volcano creds, or non-diarized notes	Chunk-stitched + Senko/pyannote
openai	Fallback — granular turn detection	gpt-4o-transcribe-diarize

Why 妙记 is the default: verified across 5 real recordings (2026-06-17), 妙记 returned the exact speaker count on every 2-person conversation (2/2/2/2) while Gemini-chunk-stitch, OpenAI, and the raw Doubao auc models all over-counted (3–5 speakers). 妙记 does server-side diarization in a single call (no chunking, no cross-chunk speaker reconcile) and handled a 3.45-hour file in one pass.

⚠️ Ask the user how many speakers (for lark)

Before transcribing with 妙记, ask the user how many speakers are in the audio if it isn't obvious. Pass it via --speakers N. Use --speakers 0 (auto-detect) only when the count is unknown — auto is good but a known count improves accuracy. Example prompt: "How many people are speaking in this recording? (I'll auto-detect if you're not sure.)"

Capabilities

• Speaker diarization: SPEAKER_1, SPEAKER_2, … (妙记 server-side; best accuracy)
• Audio length: single-call up to several hours (妙记 handled 3.45h); Gemini/OpenAI auto-chunk
• Auto language detection + code-switching: Chinese + English mixed handled cleanly
• Formats: MP3, WAV, M4A, MP4, OGG, FLAC, WebM, AAC (auto-converted to 16kHz mono mp3 before upload)

Setup (one-time)

cd <skill-dir>           # e.g. ~/.claude/plugins/transcribe
python3 -m venv venv
./venv/bin/pip install tos requests          # 妙记 (default): TOS hosting + API
./venv/bin/pip install google-genai openai   # optional, for --provider gemini/openai
brew install ffmpeg                           # required (audio conversion + chunking)

# Credentials in the skill's .env (gitignored):
#   妙记 (default):
echo 'VOLC_API_KEY=...'           >> .env     # 妙记 x-api-key
echo 'VOLC_TOS_ACCESS_KEY=...'    >> .env     # Volcano TOS (hosts audio for 妙记 to fetch)
echo 'VOLC_TOS_SECRET_KEY=...'    >> .env
echo 'VOLC_TOS_BUCKET=...'        >> .env
echo 'VOLC_TOS_REGION=...'        >> .env     # e.g. cn-shanghai
echo 'VOLC_TOS_ENDPOINT=...'      >> .env     # e.g. tos-cn-shanghai.volces.com
#   fallbacks (optional):
echo 'GEMINI_API_KEY=...'         >> .env
echo 'OPENAI_API_KEY=...'         >> .env

How 妙记 hosting works: the skill converts the audio to a small 16kHz-mono mp3, uploads it to Volcano TOS, hands 妙记 a presigned URL, then deletes the object after transcription. 妙记 fetches from TOS intra-China (fast); the only cross-border leg is the upload, minimized by compression + parallel multipart.

Quick Start

# Default 妙记 — ask the user for speaker count first
<skill-dir>/venv/bin/python <skill-dir>/transcribe_any.py meeting.m4a --speakers 2 --out_txt transcript.md

# 妙记 auto speaker detection (count unknown)
<skill-dir>/venv/bin/python <skill-dir>/transcribe_any.py interview.m4a --out_txt transcript.md

# Fallback providers
<skill-dir>/venv/bin/python <skill-dir>/transcribe_any.py audio.mp3 --provider gemini --diarize --out_txt transcript.md
<skill-dir>/venv/bin/python <skill-dir>/transcribe_any.py audio.mp3 --provider openai --out_txt transcript.md

Replace <skill-dir> with the absolute path where the skill is installed.

Workflow

1. Ask about speaker count (for default 妙记)

If the speaker count isn't obvious, ask the user. Pass --speakers N (or omit / 0 for auto).

2. Determine output file path

ALWAYS save the transcript to a file. Replace the audio extension with _transcript.md:

• /path/to/meeting.m4a → /path/to/meeting_transcript.md

3. Run transcription

<skill-dir>/venv/bin/python <skill-dir>/transcribe_any.py "/path/audio.m4a" --speakers N --out_txt "/path/audio_transcript.md"

CLI Arguments

Argument	Description	Default
--provider	lark (妙记), gemini, or openai	lark
--speakers	Expected speaker count for lark (0 = auto)	0
--model	Gemini model (ignored for lark/openai)	gemini-3.5-flash
--diarize	Speaker ID for gemini (implied for lark/openai)	False
--language	ISO-639-1 code (zh, en, ja) for gemini/openai	Auto-detect
--out_txt	Save the markdown transcript to a file (use a .md path)	None
--out_json	Save full response to JSON	None

The skill prints (and --out_txt saves) a markdown document: a header with metadata + the diarized transcript inside a fenced code block, so it renders cleanly anywhere markdown is shown.

Tunable Env Vars

Env Var	Description	Default
VOLC_API_KEY, VOLC_TOS_*	妙记 + TOS credentials (required for lark)	—
LARK_MP3_BITRATE	mp3 bitrate for upload (lower = faster upload)	32k
LARK_SOURCE_LANG	妙记 source language	zh_cn
LARK_UPLOAD_TASKS	Parallel multipart upload connections	8
CHUNK_*, CHUNK_PARALLELISM	Gemini/OpenAI chunking knobs	see code
GEMINI_API_KEY / OPENAI_API_KEY	Fallback providers	—

Output Example

The output is a markdown document:

# meeting

> provider: lark · model: volc.lark.minutes · speakers: 2

## Transcript

```
[00:00:20 - 00:00:45] SPEAKER_1: 欢迎收听本期节目。
[00:00:45 - 00:01:05] SPEAKER_2: Thanks for having me.
```

Fallback Providers (gemini / openai)

Both auto-split long audio at silence boundaries (ffmpeg silencedetect) and transcribe chunks in parallel, then reconcile speaker labels across chunks. This was the original engine before 妙记; use it when Volcano creds aren't available.

• gemini (default fallback): Gemini 3 Flash loops/fabricates timestamps on single-call audio > ~15 min, so chunking is what makes it usable. Good punctuation + code-switching; diarization is chunk-stitched (less accurate than 妙记).
• openai gpt-4o-transcribe-diarize: always diarized, granular turn detection; sometimes runs sentences together and mis-handles Chinese fillers.

Troubleshooting

• 妙记 provider unavailable / missing creds: ensure tos requests are installed in the venv and VOLC_API_KEY + VOLC_TOS_* are in .env. Fall back with --provider gemini.
• Slow upload: cross-border upload to a China TOS region can throttle; the skill compresses + uses parallel multipart. Lower LARK_MP3_BITRATE (e.g. 16k) to shrink the upload further.
• Unicode filename error: cp "中文名.m4a" /tmp/audio.m4a then transcribe the copy.
• ffmpeg not found: required for audio conversion. brew install ffmpeg.

Notes

• 妙记 wall time: ~100–150s for a 30–60 min file (single call); the 3.45h test ran in one pass.
• Gemini API cost: ~$0.20 for a 2-hour file. OpenAI: ~$0.90 for a 2-hour file.