audio-transcriber
Agent skill for multi-speaker meeting & podcast transcription — FunASR (Paraformer/SenseVoice/Whisper) + MiMo-V2.5-ASR + CAM++ speaker diarization + LLM clea…
Install with your AI
Paste into Claude Code, Cursor, or any agent — it reads the repo and wires the tool into your project.
⚠ Unverified source (community-unclaimed, low activity) — reveal the prompt
⚠ Unverified source (community-unclaimed, low activity). Inspect the repo before letting your agent install it. Install and set up audio-transcriber (claude-plugin project) into my current project. Found on https://claudeers.com/audio-transcriber Repo: https://github.com/zxkane/audio-transcriber Homepage/docs: https://clawhub.ai/zxkane/zxkane-audio-transcriber-funasr Detected install method: claude-plugin → /plugin install audio-transcriber@zxkane/audio-transcriber Category: skills. Platforms: cli, api. Read the repo's README for exact setup and env vars, then install it and wire it into my project. Claudeers Health Verdict: dormant; community-verified: false. Confirm the source before running anything.
⚠ Unverified / not recently updated — review before pasting a run-this config.
/plugin marketplace add zxkane/audio-transcriber /plugin install audio-transcriber@zxkane/audio-transcriber
git clone https://github.com/zxkane/audio-transcriber
// compatibility
| Platforms | cli, api |
|---|---|
| Operating systems | — |
| AI compatibility | claude |
| License | MIT |
| Pricing | open-source |
| Language | Python |
Audio Transcriber
Claude Code plugin for multi-speaker meeting and podcast transcription with automatic speaker diarization and LLM cleanup. Supports two ASR engine families:
- FunASR — Paraformer / SenseVoice / Whisper, fast and cheap, 99 languages, CPU or GPU
- MiMo-V2.5-ASR — 8B parameters, local GPU only, stronger on proper nouns, code-switching, and rare terms (opt-in since 1.7.0)
Both engines share the same VAD (FSMN) + speaker-clustering (CAM++) pipeline, so speaker diarization behavior is consistent across engines.
Features
- Meetings & podcasts — Handles large meetings (10+ speakers) and podcasts/interviews (2–3 speakers) with CAM++ speaker diarization,
--num-speakershint, real name mapping, and speaker context for LLM identification - Hotword biasing — SeACo-Paraformer accepts participant names and domain terms to improve recognition accuracy (+50% on tested Chinese terms, FunASR path only)
- Multi-language — Chinese (SeACo-Paraformer, CER 1.95%), English (Paraformer-en), auto-detect (SenseVoiceSmall: zh/en/ja/ko/yue), 99 languages (Whisper-large-v3-turbo), or Chinese + dialects + code-switching (MiMo-V2.5-ASR)
- Long recordings — Handles 6+ hour recordings without splitting (includes spectral clustering performance patch)
- LLM cleanup — Bedrock Claude / Anthropic / OpenAI-compatible — removes fillers, fixes ASR errors, polishes grammar
- GPU & CPU — FunASR auto-detects CUDA and falls back to CPU (low-memory guidance); MiMo requires ≥20 GB VRAM
- Resume support — Checkpoint at every phase for interrupted runs (FunASR
--skip-transcribe; MiMo--resume-mimowith audio-hash verification)
Installation
As Agent Skill (via skills.sh)
npx skills add zxkane/audio-transcriber
As Claude Code Plugin
Add as a marketplace, then install:
# In Claude Code
/plugin marketplace add zxkane/audio-transcriber
/plugin install audio-transcriber@zxkane-audio-transcriber-funasr
Note: the ClawHub package slug is still
zxkane-audio-transcriber-funasrfor backward compatibility with users who already have 1.6.x or earlier installed. The repository was renamed fromaudio-transcriber-funasrin 1.7.1 as the plugin now supports both FunASR and MiMo. Old GitHub URLs redirect.
Manual Usage
# 1. Set up environment (auto-detects GPU/CPU; requires Python 3.12 since 1.7.0)
bash plugins/audio-transcriber/skills/audio-transcribe/scripts/setup_env.sh
source .venv/bin/activate
# 2. (Optional) install MiMo-V2.5-ASR locally — ~34 GB download, ≥20 GB VRAM
INSTALL_MIMO=1 MIMO_WEIGHTS_PATH=/path/to/hf-cache \
bash plugins/audio-transcriber/skills/audio-transcribe/scripts/setup_env.sh
# 3. Convert audio to 16kHz mono FLAC (lossless, ~50% smaller than WAV)
ffmpeg -i recording.m4a -ar 16000 -ac 1 -sample_fmt s16 meeting.flac
# 4. Chinese meeting, 9 speakers (FunASR SeACo-Paraformer)
python3 plugins/audio-transcriber/skills/audio-transcribe/scripts/transcribe.py \
meeting.flac --lang zh --num-speakers 9
# 5. English meeting, with real names
python3 plugins/audio-transcriber/skills/audio-transcribe/scripts/transcribe.py \
meeting.flac --lang en --speakers "Alice,Bob,Carol,Dave"
# 6. Chinese podcast with proper-noun-heavy content (MiMo, local GPU)
python3 plugins/audio-transcriber/skills/audio-transcribe/scripts/transcribe.py \
episode.flac --lang mimo --num-speakers 2 --speakers "Host,Guest" \
--mimo-weights-path /path/to/hf-cache
# 7. Auto-detect language (zh/en/ja/ko/yue)
python3 plugins/audio-transcriber/skills/audio-transcribe/scripts/transcribe.py \
meeting.flac --lang auto --num-speakers 6
Plugin Structure
.
├── .claude-plugin/
│ └── marketplace.json # skills.sh marketplace registration
├── .claude/
│ └── skills/
│ └── audio-transcribe -> ../../plugins/audio-transcriber/skills/audio-transcribe
├── plugins/
│ └── audio-transcriber/
│ └── skills/
│ └── audio-transcribe/
│ ├── SKILL.md # Skill entry point
│ ├── references/
│ │ └── pipeline-details.md
│ └── scripts/
│ ├── transcribe.py # Main pipeline
│ ├── mimo_asr.py # MiMo-V2.5-ASR integration
│ ├── patch_clustering.py # Long-audio perf fix
│ ├── setup_env.sh # One-click env setup
│ └── setup_mimo.sh # Opt-in MiMo installer
├── CLAUDE.md
├── README.md
└── .gitignore
Pipeline
Audio (.m4a/.mp3) ─► ffmpeg ─► 16kHz WAV
│
Phase 1: ASR │
├─ FSMN-VAD (voice detection) │
├─ ASR engine: │
│ FunASR (Paraformer/ ├─► raw_transcript.json
│ SenseVoice/Whisper) │
│ OR MiMo-V2.5-ASR (local) │
├─ Hotword biasing (SeACo-zh) │
├─ Punctuation restoration │
└─ CAM++ (speaker clustering) │
│
Phase 2: Post-process │
├─ Merge consecutive utterances├─► merged segments
└─ Map speaker IDs to names │
│
Phase 3: LLM cleanup (optional) │
└─ Bedrock / Anthropic / └─► transcript.md
OpenAI-compatible
Performance
Benchmarked on a 4h14m, 9-speaker Chinese meeting recording (FunASR):
| Phase | GPU (L40S) | CPU |
|---|---|---|
| Model load | 14s | ~30s |
| Transcription | 169s | ~30-60 min |
| Clustering (patched) | ~10s | ~2-5 min |
| LLM cleanup (17 chunks) | ~35 min | ~35 min |
Without the clustering patch, speaker clustering on long audio takes 10+ hours.
The patch replaces O(N^3) scipy.linalg.eigh with O(N^2·k) scipy.sparse.linalg.eigsh.
FunASR vs MiMo at a glance (6h45m Chinese podcast, same GPU L40S)
| Engine | Wall | RTF | Cost / episode (Spot L4 est.) | Accuracy on proper nouns |
|---|---|---|---|---|
| FunASR (Paraformer) | ~6 min | 0.014 | ~$0.05 | Good |
| MiMo-V2.5-ASR | ~49 min | 0.12 | ~$0.94 | Visibly better |
See docs/superpowers/reports/2026-04-30-mimo-vs-funasr-perf-cost.md for the full comparison.
License
MIT
// faq
What is audio-transcriber?
Agent skill for multi-speaker meeting & podcast transcription — FunASR (Paraformer/SenseVoice/Whisper) + MiMo-V2.5-ASR + CAM++ speaker diarization + LLM cleanup. 99 languages, GPU & CPU. Works with Claude Code and any SKILL.md-compatible agent.. It is open-source on GitHub.
Is audio-transcriber free to use?
audio-transcriber is open-source under the MIT license, so it is free to use.
What category does audio-transcriber belong to?
audio-transcriber is listed under skills in the Claudeers registry of Claude-compatible tools.
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