audio-metrics-cli 0.4.0

Voice Acoustic Analyzer - Professional audio metrics extraction

Audio Metrics CLI v4

🎙️ Industrial-Grade Speech Deep Analysis Platform

v4.0 Architecture: GPU-accelerated, chunked processing, Pydantic-validated industrial analysis

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🇨🇳 中国区用户 - 首次使用必读

🚀 一键下载模型（推荐）

Windows 用户: 双击运行 download_models.bat 脚本

或手动执行（PowerShell）:

$env:HF_ENDPOINT = "https://hf-mirror.com"
pip install huggingface-hub openai-whisper -i https://pypi.tuna.tsinghua.edu.cn/simple
cd C:\Users\clawbot\.cache\torch\hub
git clone https://ghproxy.com/https://github.com/snakers4/silero-vad.git silero-vad_master
huggingface-cli download pyannote/speaker-diarization-3.1 --local-dir "C:\Users\clawbot\.cache\huggingface\hub\models--pyannote--speaker-diarization-3.1"
python -c "import whisper; whisper.load_model('base')"

详细说明: 查看 docs/MODEL_DEPENDENCIES.md

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🚀 Quick Start

# Install from PyPI (recommended)
pip install audio-metrics-cli

# Full V4 analysis with GPU auto-detection
audio-metrics analyze audio.wav -o result.json

# Specify device manually
audio-metrics analyze audio.wav -d cuda -o result.json

# Long audio (>1h) with custom chunk size
audio-metrics analyze audio.wav -o result.json --chunk-size 900 --show-timings

GPU Acceleration

V4 auto-detects NVIDIA GPUs and runs Whisper + pyannote.audio on CUDA:

audio-metrics analyze audio.wav -d auto   # GPU if available, else CPU
audio-metrics analyze audio.wav -d cuda     # Force GPU
audio-metrics analyze audio.wav -d cpu     # Force CPU

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🏛️ Architecture v4

┌──────────────────────────────────────────────────────┐
│               CLI Layer (main_cli.py)                │
│   analyze | analyze-multi | voice-acoustic | serve   │
└──────────────────────────────────────────────────┬───┘
                                                   ↓
┌──────────────────────────────────────────────────────┐
│          V4 Pipeline (v4/pipeline.py)               │
│  DeviceManager → AudioHealth → Chunker → Analyzer  │
└──────────────────────────────────────────────────┬───┘
                                                   ↓
┌──────────────────────────────────────────────────────┐
│          Pydantic Schema (v4/schemas.py)             │
│  V4Result → SegmentModel → SpeakerModel → NER      │
└──────────────────────────────────────────────────────┘

Key Features

Feature	Description
GPU Auto-Detection	Automatic CUDA detection for Whisper + pyannote.audio
Chunked Processing	Handles 1h+ audio without OOM (1800s chunks, 60s overlap)
Word-Level Alignment	Precise timestamp alignment (replaces seg_duration*5 estimation)
30+ Prosody Metrics	Pitch, energy, spectral, voice quality, speech rate per segment
Fluency Analysis	Filler words (呃/嗯/那个) + unnatural pauses detection
NER	spaCy-based named entity recognition (commercial entities, persons, locations)
Topic Segmentation	Semantic topic chapters with Jaccard keyword similarity
Sentiment & Key Points	TextBlob/snownlp sentiment scoring, automatic key point detection
Pydantic Validation	All outputs validated against strict schema (100% constraint enforcement)
tqdm Progress Bars	Real-time feedback on VAD, Diarization, STT, metrics extraction

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📖 CLI Commands

analyze - V4 Full Analysis

Single audio file → V4 pipeline with full feature set.

audio-metrics analyze AUDIO_FILE [OPTIONS]

Options:
  -o, --output PATH           Output JSON file path
  -d, --device [auto|cuda|cpu]  Device for inference (default: auto)
  -m, --model TEXT            Whisper model (tiny/base/small/medium/large)
  --num-speakers INTEGER       Number of speakers (if known)
  --min-speakers INTEGER       Minimum number of speakers
  --max-speakers INTEGER       Maximum number of speakers
  --language TEXT              Language code (auto-detect if not specified)
  --chunk-size INTEGER         Chunk size in seconds for long audio (default: 1800)
  --no-emotion                 Skip emotion analysis
  --no-progress                Disable tqdm progress bars
  --show-timings               Show step timing information
  --show-progress              Show progress bars
  -f, --format [json|csv|html]  Output format (default: json)
  --parallel                   Use parallel processing (batch mode)
  --batch PATH                 Process all audio files in directory
  --glob TEXT                  Glob pattern for batch processing
  -j, --workers INTEGER        Number of parallel workers
  -v, --verbose                Verbose output

Examples:
  audio-metrics analyze meeting.wav -o result.json
  audio-metrics analyze meeting.wav -d cuda -o result.json --show-timings
  audio-metrics analyze long_recording.wav --chunk-size 900 --language zh

analyze-multi - Multi-Speaker Conversation

audio-metrics analyze-multi AUDIO_FILE [OPTIONS]

voice-acoustic - Acoustic Features Only

audio-metrics voice-acoustic AUDIO_FILE [OPTIONS]

transcribe - Whisper Transcription Only

audio-metrics transcribe AUDIO_FILE [-o OUTPUT] [-m MODEL] [--language LANG]

compare - Compare Two Audio Files

audio-metrics compare FILE1 FILE2 [--format text|json|markdown]

serve - Start API Server

audio-metrics serve [--host HOST] [-p PORT] [--reload]

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📊 V4 Output Schema

All outputs are Pydantic-validated JSON with strict constraints.

Top-Level Structure

{
  "meta": {
    "version": "4.0.0",
    "device_used": "cuda",
    "chunked_processing": false,
    "analysis_complete": true
  },
  "audio": { ... },
  "speakers": [ ... ],
  "segments": [ ... ],
  "prosody": { ... },
  "fluency": { ... },
  "conversation_dynamics": { ... },
  "vad": { ... },
  "emotion": { ... },
  "named_entities": { ... },
  "topic_segments": [ ... ],
  "transcript_text": "...",
  "transcript_language": "zh"
}

Segment Detail (Core Output Unit)

{
  "segment_index": 0,
  "start": 0.0,
  "end": 15.234,
  "duration": 15.234,
  "confidence": 0.95,
  "speaker": "speaker_0",
  "text": "今天我们讨论一下Aibee项目的进展情况。万象城的项目已经进入第三期。",
  "pitch_mean_hz": 175.3,
  "energy_mean": 0.0245,
  "speech_rate_wpm": 150.2,
  "filler_words": { "那个": 2, "嗯": 1 },
  "sentiment_score": 0.3,
  "is_key_point": true,
  "named_entities": ["Aibee", "万象城"],
  "topic": "project_update"
}

Named Entities

{
  "total_entities": 7,
  "commercial_entities": ["Aibee", "万象城", "中海地产", "保利", "SKP"],
  "persons": ["张三"],
  "organizations": ["Aibee", "中海地产"]
}

Topic Segmentation

{
  "num_topics": 3,
  "topics": [
    { "start": 0.0, "end": 1200.0, "topic_label": "project_update", "keywords": ["项目", "进度", "Aibee", "万象城"], "confidence": 0.85 },
    { "start": 1200.0, "end": 2400.0, "topic_label": "planning", "keywords": ["计划", "目标", "策略"], "confidence": 0.78 }
  ]
}

See standard_v4_sample.json for full reference.

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⚠️ Important: Dependencies

This tool requires pyannote.audio for accurate multi-speaker analysis.

Without pyannote.audio installed, the tool uses a fallback VAD-based method that:

• ❌ Cannot distinguish between different speakers
• ❌ Will show 50/50 speaking time even when one person talks 90% of the time

With pyannote.audio installed:

• ✅ Correctly identifies who spoke when
• ✅ Accurate speaker time statistics
• ✅ Works with any number of speakers

Installation

# CPU-only (faster install, recommended for testing)
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cpu
pip install pyannote.audio

# GPU (faster inference, requires CUDA)
pip install torch torchaudio
pip install pyannote.audio

Optional Dependencies for Full V4 Features

# NER + Sentiment (recommended)
pip install audio-metrics-cli[nlp]

# Individual
pip install audio-metrics-cli[ner]      # spaCy for named entity recognition
pip install audio-metrics-cli[emotion]   # SpeechBrain for emotion analysis
pip install audio-metrics-cli[api]       # FastAPI server

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💻 Development

# Clone repository
git clone https://github.com/i-whimsy/audio-metrics-cli.git
cd audio-metrics-cli

# Install with dev dependencies
pip install -e ".[dev]"

# Run V4 tests
pytest tests/v4/ -v

# Run all tests
pytest tests/ -v

# Format code
black src/
ruff check src/

Project Structure

audio-metrics-cli/
├── src/audio_metrics/
│   ├── main_cli.py              # V4 CLI entry point
│   ├── cli/
│   │   ├── __init__.py          # cli/__init__ → main_cli.py
│   │   └── cli.py               # Legacy v3 CLI (superseded)
│   ├── v4/
│   │   ├── __init__.py
│   │   ├── schemas.py           # Pydantic V4 models
│   │   ├── pipeline.py         # V4 orchestrator
│   │   └── generate_sample.py   # Sample generation
│   ├── analyzers/
│   │   ├── audio_health.py      # Audio validation/normalization
│   │   ├── speech_to_text.py    # Word-level timestamps
│   │   ├── speaker_diarization.py  # GPU device support
│   │   ├── prosody_analyzer.py  # 30+ prosody features
│   │   ├── filler_detector.py  # Filler word detection
│   │   ├── fluency_analyzer.py # Unnatural pauses
│   │   └── ...
│   ├── nlp/
│   │   ├── ner_analyzer.py      # spaCy NER
│   │   ├── topic_segmenter.py  # Topic segmentation
│   │   ├── sentiment_analyzer.py  # TextBlob + snownlp
│   │   └── ...
│   ├── core/
│   │   ├── device.py            # GPU/CPU detection
│   │   ├── chunker.py           # Long audio chunking
│   │   ├── warnings.py         # Warning suppression
│   │   └── ...
│   ├── conversation/
│   ├── metrics/
│   └── exporters/
├── tests/
│   └── v4/
│       ├── test_schema_validation.py  # 27 schema tests
│       └── test_edge_cases.py        # 17 boundary tests
├── standard_v4_sample.json     # Reference output
├── pyproject.toml
└── README.md

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🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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📝 License

MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• OpenAI Whisper - Speech-to-text
• Silero VAD - Voice activity detection
• pyannote - Speaker diarization
• Librosa - Audio analysis
• spaCy - Named entity recognition
• TextBlob - Sentiment analysis
• SnowNLP - Chinese sentiment

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📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]

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Built with ❤️ by OpenClaw Team v4.0 - Industrial-Grade Speech Deep Analysis