Skill: yangqibin-caibi/omnivoice (version 1.0.0)

--- name: omnivoice description: "All-in-one voice identity toolkit: speaker identification, voice library management, voice cloning, and speech-to-text. The only OpenClaw skill with speaker identification — recognize WHO is speaking, not just WHAT they said. 10 operations: identify speakers, manage a voice library (CRUD), clone voices, transcribe audio, voice swap, and persona voice replies. Activate when user sends voice/audio, asks to identify a speaker, manage a voice library, clone someone's voice, transcribe audio, or wants voice-based Q&A in a specific person's voice. Triggers: voice, audio, transcribe, 转文字, 语音, identify speaker, who is speaking, 这是谁的声音, 声纹识别, voice clone, 克隆声音, 模仿声音, voice library, 声音库, voice swap, 声音换皮." --- # OmniVoice Ten operations across four capabilities: **identify** (认) · **manage** (存) · **transcribe** (听) · **clone** (说). ## Dependencies | Component | Install | Purpose | |-----------|---------|---------| | Whisper | `pip install openai-whisper` | Speech-to-text | | Speaker ID | `pip install transformers librosa` | Speaker identification (UniSpeech-SAT) | | CosyVoice2 | SiliconFlow API (`SF_API_KEY`) | Voice cloning | | ffmpeg | System package | Audio conversion | Voice references are stored in `voice-refs/` at workspace root. Metadata lives in `TOOLS.md` under a "Voice Library" section. See [references/voice-library-format.md](references/voice-library-format.md) for format spec. ## Operations ### Op 1 · Speaker Identification (声纹查询) Input: audio → Output: who is speaking (or "unknown") ```bash python3 scripts/voice_identify.py <audio_file> [--threshold 0.75] ``` Compares audio against all `voice-refs/*-ref*.*` using UniSpeech-SAT x-vector embeddings. First run downloads model (~360MB) to `/tmp/hf_models/`. **Accuracy:** Reliably separates male/female voices. Same-gender speakers need ≥5s audio for best results. Threshold 0.75 is default; raise to 0.85 for stricter matching. ### Op 2 · Add Voice to Library (声音入库) Input: audio + speaker name → stores in voice library 1. Copy audio to `voice-refs/<name>-ref1.<ext>` 2. Transcribe to get reference text: `whisper <audio> --model small --output_format txt --output_dir /tmp` 3. Add entry to `TOOLS.md` (see format in references/) 4. Register speaker in `voice_identify.py` `SPEAKER_MAP` **Good reference audio:** 10-15s clear speech, minimal noise, natural pace. 5s minimum. ### Op 3 · Voice Library CRUD (声音库管理) - **List:** Check `TOOLS.md` voice library section + `ls voice-refs/` - **Add:** See Op 2 - **Update:** Replace file in `voice-refs/`, update `TOOLS.md` entry - **Delete:** Remove file from `voice-refs/`, remove `TOOLS.md` entry, remove from `SPEAKER_MAP` ### Op 4 · Voice Clone (声音克隆) Input: text + library speaker → Output: audio in that speaker's voice ```bash set -a; source <env_file_with_SF_API_KEY>; set +a python3 scripts/cosyvoice_clone.py \ --text "Text to speak" \ --ref voice-refs/<speaker>-ref1.<ext> \ --ref-text "What is said in reference audio" \ --output /tmp/clone_output.wav ``` Long reference (>15s): truncate first with `ffmpeg -y -i <ref> -t 15 -ar 24000 -ac 1 /tmp/ref_trimmed.wav`. ### Op 5 · Transcribe (纯转文字) Input: audio → Output: text ```bash whisper <audio_file> --model small --output_format txt --output_dir /tmp --language <lang> ``` Languages: `zh` (Chinese), `en` (English), `ja` (Japanese). Omit for auto-detect. ### Op 6 · Transcribe + Identify (转文字+识别) Input: audio → Output: who said what Run Op 5 and Op 1 in parallel, report both results together. ### Op 7 · Speaker Verification (声纹验证) Input: two audio files → Output: same person or not ```bash python3 scripts/voice_identify.py <audio_1> --threshold 0.75 python3 scripts/voice_identify.py <audio_2> --threshold 0.75 ``` Compare the top-ranked speaker from both runs. If they match → same person. For direct pairwise comparison without a library, extract embeddings and compute cosine similarity (see voice_identify.py internals). ### Op 8 · Voice Swap (声音换皮) Input: audio + library speaker → Output: same words, different voice 1. Transcribe input audio (Op 5) 2. Clone with target speaker's voice (Op 4), using transcribed text ### Op 9 · Persona Voice Reply — from Audio (人格化语音回复·语音版) Input: audio question + library speaker → Output: AI answer in that speaker's voice 1. Transcribe the question (Op 5) 2. Generate answer text via LLM 3. Clone answer with target speaker's voice (Op 4) ### Op 10 · Persona Voice Reply — from Text (人格化语音回复·文字版) Input: text question + library speaker → Output: AI answer in that speaker's voice 1. Generate answer text via LLM 2. Clone answer with target speaker's voice (Op 4) ## Send Audio (Feishu) ```bash set -a; source <env_file>; set +a bash scripts/feishu_send_audio.sh <wav_file> <receive_id> ``` Converts wav → opus, uploads, sends as voice message. Requires `FEISHU_APP_ID` + `FEISHU_APP_SECRET` env vars. ## Extract Audio from Video ```bash ffmpeg -y -i <video_file> -vn -ar 24000 -ac 1 /tmp/extracted_audio.wav ```


Security scan verdicts — ClawScan: clean | static analysis: suspicious | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)