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🦀 ClawHub

Audio Transcribe

by @zxkane

This skill should be used when the user explicitly asks to "transcribe a meeting", "transcribe audio", "transcribe a meeting recording", "convert audio to te...

Versionv1.7.1

Downloads717

#audio #legal #communication

View on ClawHub →

TERMINAL

clawhub install zxkane-audio-transcriber-funasr

📖 About This Skill

name: audio-transcribe version: 1.7.1 description: > This skill should be used when the user explicitly asks to "transcribe a meeting", "transcribe audio", "transcribe a meeting recording", "convert audio to text", "generate meeting minutes from audio", "do speech-to-text", "transcribe with speaker diarization", "identify speakers in audio", "transcribe Chinese audio", "transcribe English audio", "transcribe Japanese audio", "multi-speaker transcription", "transcribe a podcast", "transcribe podcast episode", "transcribe an interview", "convert podcast to text", "podcast to transcript", or mentions FunASR, Paraformer, SenseVoice, Whisper, MiMo, MiMo-V2.5-ASR, meeting transcription, podcast transcription, or speaker diarization. Supports multi-speaker meeting and podcast transcription in Chinese, English, Japanese, Korean, Cantonese, and 99 languages (via Whisper), plus Xiaomi MiMo-V2.5-ASR (8B, local GPU) for stronger proper-noun and code-switching accuracy. Automatic speaker diarization via CAM++, hotword biasing (FunASR path), LLM cleanup. FunASR works on GPU and CPU; MiMo requires a local CUDA GPU with >=20GB VRAM. metadata: openclaw: requires: bins: ["python3", "ffmpeg"] env_vars: - name: AWS_REGION required: false description: "AWS region for Bedrock LLM cleanup (default: us-west-2). Bedrock uses the standard AWS credential chain (IAM role, SSO, ~/.aws/credentials, env vars) — no explicit keys needed." - name: ANTHROPIC_API_KEY required: false description: "API key for Anthropic Claude LLM cleanup" - name: OPENAI_API_KEY required: false description: "API key for OpenAI-compatible LLM cleanup" - name: OPENAI_BASE_URL required: false description: "Base URL for OpenAI-compatible API (vLLM, Ollama, etc.)" emoji: "🎙️" homepage: "https://github.com/zxkane/audio-transcriber"

Meeting & Podcast Transcription (FunASR + MiMo)

Transcribe multi-speaker audio into structured Markdown with automatic speaker diarization, hotword biasing, and optional LLM cleanup. Two ASR engine families are available: FunASR (Paraformer / SenseVoice / Whisper — fast, cheap, GPU or CPU, 99 languages) and MiMo-V2.5-ASR (Xiaomi's 8B model, local GPU only, stronger on proper nouns and code-switching). Both share the same VAD + speaker-clustering stack.

All scripts run directly from the plugin directory — no copying needed. Define this shorthand at the start of every session:

SCRIPTS=${CLAUDE_PLUGIN_ROOT}/skills/audio-transcribe/scripts

Supported Languages

| --lang | Model | Languages | Hotword | |----------|-------|-----------|---------| | zh (default) | SeACo-Paraformer | Chinese (CER 1.95%) | Yes | | zh-basic | Paraformer-large | Chinese | No | | en | Paraformer-en | English | No | | auto | SenseVoiceSmall | Auto-detect: zh/en/ja/ko/yue | No | | whisper | Whisper-large-v3-turbo | 99 languages | No | | mimo | MiMo-V2.5-ASR (local 8B, GPU-only) | zh/en/code-switch/dialects | No |

All presets include speaker diarization (CAM++) and VAD (FSMN). mimo reuses the FSMN VAD + CAM++ stack around MiMo's text output.

> Diarization caveat: auto and whisper do not output per-sentence timestamps, > so speaker diarization does not work with these presets. Use zh, zh-basic, > en, or mimo when speaker identification is needed (e.g., podcasts, meetings).

Workflow

Before starting transcription, always ask the user:

1. Audio file — path to the recording (required) 2. Type — meeting, podcast, or interview? (affects defaults) 3. Language — what language is spoken? (default: Chinese) 4. Number of speakers — how many participants? (improves diarization) 5. Speaker names — for podcasts: host + guest names; for meetings: attendee list 6. Supporting files — ask: > "Do you have any of the following to improve accuracy?" > - Attendee / guest list — for hotwords and speaker mapping > - Meeting agenda or episode topic — for hotwords (terms, names) > - Reference documents (show notes, prior notes) — for speaker identification and ASR correction

Adapt defaults by recording type:

Meeting: default --lang zh, ask about supporting files

Podcast / interview: default --lang zh, --num-speakers 2, always ask for

host + guest names, suggest --speaker-context for roles (do NOT use --lang auto — it lacks timestamps for speaker diarization)

> ⚠️ --speakers must use the speaker's real name, not a podcast alias. > The value passed to --speakers is used verbatim as the speaker label in the > output transcript. Always derive it from the host/guest's actual name (e.g. > from a shownotes "Host:" field), not from the podcast feed name or title. > > Example: if shownotes lists "Host: 张三（张三的播客）", pass --speakers '张三' > — not the alias "张三的播客". Add both the real name and the alias to > hotwords.txt so ASR can recognise both forms. > > When both --speakers and --reference are supplied, the script detects > this mistake at startup and prints an ACTION REQUIRED block naming the > suggested real name. **If you see that block, stop the run and re-invoke > with the corrected --speakers value before Phase 3** — the warning does > not abort the pipeline.

If the user provides supporting materials:

Extract participant names and key terms → create hotwords.txt (include both real name and alias)

Extract per-person context → create speaker-context.json

Pass original reference document with --reference

Use all three together for best results

Quick Start

1. Environment Setup

AUTO_YES=1 bash $SCRIPTS/setup_env.sh
Or force CPU:  AUTO_YES=1 bash $SCRIPTS/setup_env.sh cpu

The setup script patches FunASR's spectral clustering for O(N²·k) performance. Without this, recordings over ~1 hour hang for hours during speaker clustering.

2. Run Transcription

Output files are written to the current working directory.

LLM cleanup (Phase 3) is opt-in. By default, transcription runs locally without contacting any external service. To enable LLM-powered ASR correction and speaker name refinement, pass --model . Use LLM cleanup when:

The raw transcript has many ASR errors (names, technical terms)

You need polished, publication-ready output

Speaker names need to be refined from context

> ⚠️ Data Privacy: When LLM cleanup is enabled via --model, transcript > excerpts are sent to external LLM providers (AWS Bedrock, Anthropic, or > OpenAI depending on the model ID). Use --skip-llm or omit --model to > keep all data local. For Bedrock, boto3 uses the standard AWS credential > chain (IAM role, SSO, ~/.aws/credentials, env vars).

# Chinese meeting with hotwords (local-only, no LLM)
python3 $SCRIPTS/transcribe.py meeting.wav \
    --lang zh --num-speakers 9 --hotwords hotwords.txt
English meeting with speaker names
python3 $SCRIPTS/transcribe.py meeting.wav \
    --lang en --speakers "Alice,Bob,Carol,Dave"
Auto-detect language (zh/en/ja/ko/yue)
python3 $SCRIPTS/transcribe.py meeting.wav \
    --lang auto --num-speakers 6
Whisper for any language
python3 $SCRIPTS/transcribe.py meeting.wav \
    --lang whisper --num-speakers 4
Enable LLM cleanup for polished output (requires --model)
Bedrock (uses AWS credential chain: IAM role, SSO, ~/.aws/credentials)
python3 $SCRIPTS/transcribe.py meeting.wav \
    --lang zh --num-speakers 9 --hotwords hotwords.txt \
    --provider bedrock --model us.anthropic.claude-sonnet-4-6
Bedrock "global" cross-region profile (recent AWS deployments)
python3 $SCRIPTS/transcribe.py meeting.wav \
    --provider bedrock --model global.anthropic.claude-sonnet-4-6
Bedrock via litellm-style wrapper (supported; prefix is stripped for boto3)
python3 $SCRIPTS/transcribe.py meeting.wav \
    --provider bedrock --model amazon-bedrock/global.anthropic.claude-sonnet-4-6
Anthropic API (requires ANTHROPIC_API_KEY env var)
python3 $SCRIPTS/transcribe.py meeting.wav \
    --provider anthropic --model claude-sonnet-4-6
OpenAI-compatible API (requires OPENAI_API_KEY env var)
python3 $SCRIPTS/transcribe.py meeting.wav \
    --provider openai --model gpt-4o
Full pipeline with all supporting files + LLM (best quality)
python3 $SCRIPTS/transcribe.py episode.m4a \
    --lang zh --num-speakers 2 \
    --hotwords hotwords.txt \
    --speakers "关羽,张飞" \
    --speaker-context speaker-context.json \
    --reference show-notes.md \
    --model us.anthropic.claude-sonnet-4-6
Resume interrupted LLM cleanup
python3 $SCRIPTS/transcribe.py meeting.wav \
    --skip-transcribe --model us.anthropic.claude-sonnet-4-6

3. Verify Speaker Labels

If the transcript has swapped speaker labels (common with podcasts), the verification script can detect and fix mismatches using LLM analysis:

# Dry-run: check if host/guest are swapped
python3 $SCRIPTS/verify_speakers.py podcast_raw_transcript.json \
    --speakers "关羽,张飞" \
    --speaker-context speaker-context.json
Apply the fix
python3 $SCRIPTS/verify_speakers.py podcast_raw_transcript.json \
    --speakers "关羽,张飞" \
    --speaker-context speaker-context.json --fix
Multi-speaker meeting: full reassignment
python3 $SCRIPTS/verify_speakers.py meeting_raw_transcript.json \
    --speakers "Alice,Bob,Carol,Dave" \
    --speaker-context speaker-context.json --fix
Then regenerate the markdown with corrected labels
python3 $SCRIPTS/transcribe.py original.m4a \
    --skip-transcribe --clean-cache

The script analyzes the first 5 minutes (configurable with --minutes) and auto-detects podcast (2 speakers, swap detection) vs meeting (N speakers, full reassignment).

Audio Preprocessing

The script automatically converts input audio to 16kHz mono FLAC and validates that no audio is lost (detects silent truncation).

| Format | 4h14m meeting | Quality | Recommendation | |--------|--------------|---------|----------------| | FLAC | 219MB | Lossless | Default, safest | | Opus | 55MB | Lossy | Risk of truncation on long files | | WAV | 465MB | Lossless | Works but larger | | Original M4A | 173MB | Source | Also works directly |

Do NOT split long recordings — splitting breaks speaker ID consistency.

MiMo-V2.5-ASR (optional, GPU-only)

--lang mimo runs Xiaomi's MiMo-V2.5-ASR locally on a CUDA GPU. Use it when:

You want to evaluate MiMo against Paraformer on Chinese audio.

The recording has heavy code-switching, dialects (Wu, Cantonese, Hokkien,

Sichuanese), lyrics, or rare proper nouns that other presets mis-transcribe.

Requirements:

CUDA ≥12.0 and ≥20 GB VRAM (16 GB cards OOM during inference).

Python 3.12 (enforced by setup_env.sh).

~20 GB weight download (one-time) and flash-attn==2.7.4.post1 compile

(needs nvcc from the CUDA toolkit, takes 10–30 min).

Install (opt-in):

# One-time: install MiMo on top of the standard environment
AUTO_YES=1 INSTALL_MIMO=1 \
    MIMO_WEIGHTS_PATH=/mnt/models/hf \
    bash $SCRIPTS/setup_env.sh

Run:

python3 $SCRIPTS/transcribe.py podcast.m4a \
    --lang mimo --num-speakers 2 \
    --mimo-weights-path /mnt/models/hf

Resume after failure:

python3 $SCRIPTS/transcribe.py podcast.m4a \
    --lang mimo --resume-mimo --mimo-weights-path /mnt/models/hf

Limitations:

No hotword biasing (MiMo has no API for it — --hotwords is ignored).

No CPU fallback.

Inference is slower than Paraformer on the same GPU (8B model vs ~0.3B);

expect RTF around 0.1–0.2 on an A100.

Key Flags

| Flag | Purpose | |------|---------| | --lang | zh (default), zh-basic, en, auto, whisper | | --hotwords | Hotword file or string — biases ASR (zh only) | | --reference F | Reference file for LLM ASR correction | | --num-speakers N | Expected speaker count (improves diarization) | | --speakers "A,B,C" | Assign real names by first-appearance order | | --speaker-context F | JSON with per-speaker roles for LLM | | --no-detect-gender | Disable automatic speaker gender detection (CAM++ gender classifier) | | --speaker-genders "A:female,B:male" | Override per-speaker gender (also accepts positional female,male) | | --audio-format | flac (default), opus, wav | | --device cpu | Force CPU mode | | --batch-size N | Adjust for memory (60 for CPU, 100 if GPU OOM) | | --phase1-only | Exit after Phase 1 (VAD + ASR + diarization), skip Phase 2 + 3 | | --json-out PATH | Write raw transcript JSON to explicit path (overrides default naming) | | --skip-transcribe | Resume from saved *_raw_transcript.json | | --skip-llm | Skip LLM cleanup (default when --model is omitted) | | --model ID | Enable LLM cleanup with this model (auto-detects Bedrock/Anthropic/OpenAI) | | --title "..." | Output document title | | --clean-cache | Delete LLM chunk cache after completion | | --output PATH | Custom output file path | | --model-cache-dir | ModelScope model cache directory (~3GB, default: ~/.cache/modelscope/) | | --mimo-audio-tag | MiMo language hint: (default), , | | --mimo-batch N | Concurrent VAD segments per MiMo call (default 1; H100/80GB can go higher) | | --mimo-weights-path DIR | Cache dir for MiMo weights (default: $HF_HOME → ~/.cache/huggingface) | | --resume-mimo | Resume MiMo Phase 1 from *_mimo_partial.json after a mid-run failure |

Outputs

-transcript.md — Final Markdown with speaker labels and timestamps

_raw_transcript.json — Raw Phase 1 output (for resume/analysis)

Speaker Diarization Tips

FunASR's CAM++ may merge acoustically similar speakers. To improve:

1. --num-speakers N — Hint expected count 2. --hotwords — Include participant names (Chinese names work best) 3. --speaker-context — Provide per-person keywords for LLM splitting 4. Keyword matching — Search *_raw_transcript.json for unique phrases

Speaker gender

Enabled by default: each detected speaker is classified as male / female via 3D-Speaker's CAM++ gender classifier (iic/speech_campplus_two_class_gender_16k). The result appears next to each name in the Speaker List table and is injected into the LLM cleanup prompt so pronouns (他/她, he/she) get corrected.

Precedence when combined:

1. --speaker-genders "Alice:female,Bob:male" (explicit CLI) — always wins 2. Reference text hints like 主播（女）：韩梅梅 or Host (male): Alice — override auto 3. CAM++ auto-detection — fallback

Disable with --no-detect-gender if you don't need gender and want to save the ~500 MB model download and extra inference time.

CPU-only / Low-Memory Machines

Long recordings on resource-constrained machines may hit exec timeouts or OOM kills. See references/pipeline-details.md for workarounds:

Detach from agent timeouts with systemd-run or nohup

Prevent OOM via swap and/or --lang zh-basic (lighter model)

Additional Resources

references/pipeline-details.md — Architecture, model specs, benchmarks,

speaker role verification, hotword effectiveness, clustering patch

scripts/transcribe.py — Main transcription pipeline

scripts/verify_speakers.py — Speaker label verification & fix

scripts/llm_utils.py — Shared LLM infrastructure (Bedrock/Anthropic/OpenAI)

scripts/setup_env.sh — Environment setup (venv + deps + patch)

💡 Examples