Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Audio Speaker Tools
Speaker separation, voice comparison, and audio processing tools. Use when working with multi-speaker audio, voice cloning, or speaker verification tasks inc...
skill openclawclawhub Free
Speaker separation, voice comparison, and audio processing tools. Use when working with multi-speaker audio, voice cloning, or speaker verification tasks inc...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/elevenlabs-cloning.md, references/scoring-guide.md, scripts/compare_voices.py, scripts/diarize_and_slice_mps.py, scripts/setup_venv.sh
Validation
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- Confirm the extracted package contains the expected setup assets.
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- cmfinlan
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Documentation
Audio Speaker Tools
Tools for speaker separation, voice comparison, and audio processing using Demucs, pyannote, and Resemblyzer.
Overview
This skill provides three main workflows: Speaker separation - Extract per-speaker audio from multi-speaker recordings Voice comparison - Measure speaker similarity between two audio files Audio processing - Segment extraction and voice isolation
Setup Virtual Environment
Run once to create the venv and install dependencies: bash scripts/setup_venv.sh Default venv location: ./.venv Requirements: Python 3.9+ ffmpeg (brew install ffmpeg) HuggingFace token (set as env var HF_TOKEN)
1. Speaker Separation: diarize_and_slice_mps.py
Separate speakers from multi-speaker audio: # Basic usage HF_TOKEN=<your-hf-token> \ /path/to/venv/bin/python scripts/diarize_and_slice_mps.py \ --input audio.mp3 \ --outdir /path/to/output \ --prefix MyShow # With speaker constraints HF_TOKEN=$TOKEN python scripts/diarize_and_slice_mps.py \ --input audio.mp3 \ --outdir ./out \ --min-speakers 2 \ --max-speakers 5 \ --pad-ms 100 Process: Converts input to 16kHz mono WAV Runs Demucs vocal/background separation (optional, for cleaner input) Runs pyannote speaker diarization (MPS-accelerated) Extracts concatenated per-speaker WAV files Output: <prefix>_speaker1.wav, <prefix>_speaker2.wav, etc. (one per detected speaker) diarization.rttm (time-stamped speaker segments) segments.jsonl (JSON segments metadata) meta.json (pipeline info and speaker index) Important: Always pass HF token via HF_TOKEN env var, never as CLI arg MPS first, CPU fallback - Script prefers Metal GPU, falls back to CPU if unavailable Default output: ./separated/
2. Voice Comparison: compare_voices.py
Measure similarity between two voice samples using Resemblyzer: # Basic comparison python scripts/compare_voices.py \ --audio1 sample1.wav \ --audio2 sample2.wav # JSON output python scripts/compare_voices.py \ --audio1 reference.wav \ --audio2 clone.wav \ --threshold 0.85 \ --json # Exit code = 0 if pass, 1 if fail Scores: < 0.75 = Different speakers 0.75-0.84 = Likely same speaker 0.85+ = Excellent match (ideal for voice cloning validation) Use cases: Voice clone quality assessment (compare clone vs. original) Speaker verification (authenticate speaker identity) Validate speaker separation (confirm separated speakers are distinct) See: references/scoring-guide.md for detailed interpretation
3. Audio Trimming
Use ffmpeg directly for segment extraction: # Extract 10-second segment starting at 5 seconds ffmpeg -i input.mp3 -ss 5 -t 10 -c copy output.mp3 # Extract vocals only with Demucs (before diarization) demucs --two-stems vocals --out ./separated input.mp3
Workflow 1: Extract Clean Voice Sample for Cloning
Goal: Get a clean, single-speaker sample for ElevenLabs voice cloning # 1. Separate speakers HF_TOKEN=<your-hf-token> python scripts/diarize_and_slice_mps.py \ --input podcast.mp3 --outdir ./out --prefix Podcast # 2. Review speaker files (out/Podcast_speaker1.wav, etc.) # 3. Select best sample (5-30s, clean speech) ffmpeg -i out/Podcast_speaker2.wav -ss 10 -t 20 -c copy sample.wav # 4. Upload to ElevenLabs as instant voice clone See: references/elevenlabs-cloning.md for best practices
Workflow 2: Validate Voice Clone Quality
Goal: Measure how well a cloned voice matches the original # 1. Generate test audio with ElevenLabs clone # (done via ElevenLabs web UI or API) # 2. Compare clone vs. reference python scripts/compare_voices.py \ --audio1 original_sample.wav \ --audio2 elevenlabs_clone.wav \ --threshold 0.85 \ --json # 3. Interpret score: # 0.85+ = excellent, publish-ready # 0.80-0.84 = acceptable, may need tweaking # < 0.80 = poor, try different sample or settings See: references/scoring-guide.md for troubleshooting low scores
Workflow 3: Multi-Speaker Conversation Analysis
Goal: Separate and identify speakers in a conversation # 1. Run diarization HF_TOKEN=$TOKEN python scripts/diarize_and_slice_mps.py \ --input meeting.mp3 --outdir ./out --prefix Meeting # 2. Check detected speakers (meta.json) cat out/meta.json # 3. Compare speaker pairs to confirm separation python scripts/compare_voices.py \ --audio1 out/Meeting_speaker1.wav \ --audio2 out/Meeting_speaker2.wav # Expected: < 0.75 if separation worked correctly
Device Acceleration
pyannote diarization: MPS (Metal) by default, CPU fallback Resemblyzer: CPU only (no GPU acceleration) Demucs: MPS by default when available To force CPU for diarization: --device cpu
Audio Formats
Input: Any format supported by ffmpeg (wav, mp3, flac, m4a, etc.) Processing: Internally converted to 16kHz mono WAV for diarization Output: WAV format (44.1kHz stereo preserved from source)
HuggingFace Token
Required for: pyannote speaker diarization Access: Must accept gated repo pyannote/speaker-diarization-3.1 on HF Storage: Any secure secrets manager Usage: Always pass via HF_TOKEN env var, never CLI arg
Sample Quality Tips
Shorter is better: 5-30s clean samples often score higher than 60+ second samples Clean audio: Remove background noise with Demucs --two-stems vocals Single speaker: Ensure isolated voice, not mixed conversation Good recording: Studio mic > phone mic for voice comparison accuracy
References
elevenlabs-cloning.md - Best practices for ElevenLabs instant voice cloning (model settings, sample selection, proven configurations) scoring-guide.md - How to interpret Resemblyzer similarity scores (thresholds, use cases, troubleshooting)
"Missing HF token" error
Export token before running: export HF_TOKEN=<your-token> Or pass inline: HF_TOKEN=<your-token> python script.py ...
Low voice comparison scores for same speaker
Try shorter, cleaner samples (5-30s) Use Demucs to isolate vocals: demucs --two-stems vocals input.mp3 Ensure consistent recording quality (same mic, environment) See references/scoring-guide.md troubleshooting section
Diarization not detecting all speakers
Adjust --min-speakers and --max-speakers flags Check audio quality (clear speech, minimal overlap) Try longer audio (30+ seconds) for better speaker modeling
MPS/Metal acceleration not working
Ensure PyTorch with MPS support: python -c "import torch; print(torch.backends.mps.is_available())" Fallback to CPU: --device cpu Re-run setup_venv.sh to reinstall PyTorch
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs3 Scripts
- SKILL.md
- references/elevenlabs-cloning.md
- references/scoring-guide.md
- scripts/compare_voices.py
- scripts/diarize_and_slice_mps.py
- scripts/setup_venv.sh