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Voice Response","email":"info@agentvoiceresponse.com","url":"https://github.com/agentvoiceresponse"},"license":"MIT","homepage":"https://github.com/agentvoiceresponse/avr-vad#readme","keywords":["vad","voice-activity-detection","silero","audio","speech","onnx"],"repository":{"type":"git","url":"git+https://github.com/agentvoiceresponse/avr-vad.git"},"description":"A Node.js library for Voice Activity Detection using Silero VAD","maintainers":[{"name":"gcareri","email":"info@gcareri.com"}],"readme":"# Agent Voice Response - AVR VAD - Silero Voice Activity Detection for Node.js \n\n[![Discord](https://img.shields.io/discord/1347239846632226998?label=Discord&logo=discord)](https://discord.gg/DFTU69Hg74)\n[![GitHub Repo stars](https://img.shields.io/github/stars/agentvoiceresponse/avr-vad?style=social)](https://github.com/agentvoiceresponse/avr-vad)\n[![npm version](https://img.shields.io/npm/v/avr-vad.svg)](https://www.npmjs.com/package/avr-vad)\n[![npm downloads](https://img.shields.io/npm/dm/avr-vad.svg)](https://www.npmjs.com/package/avr-vad)\n[![Ko-fi](https://img.shields.io/badge/Support%20us%20on-Ko--fi-ff5e5b.svg)](https://ko-fi.com/agentvoiceresponse)\n\n🎤 A Node.js library for Voice Activity Detection using the Silero VAD model.\n\n## ✨ Features\n\n- 🚀 **Based on Silero VAD**: Uses the pre-trained Silero ONNX model (v5 and legacy versions) for accurate results\n- 🎯 **Real-time processing**: Supports real-time frame-by-frame processing\n- ⚡ **Non-real-time processing**: Batch processing for audio files and streams\n- 🔧 **Configurable**: Customizable thresholds and parameters for different needs\n- 🎵 **Audio processing**: Includes utilities for resampling and audio manipulation\n- 📊 **Multiple models**: Support for both Silero VAD v5 and legacy models\n- 💾 **Bundled models**: Models are included in the package, no external downloads required\n- 📝 **TypeScript**: Fully typed with TypeScript\n\n## 🚀 Installation\n\n```bash\nnpm install avr-vad\n```\n\n## 📖 Quick Start\n\n### Real-time Processing\n\n```typescript\nimport { RealTimeVAD } from 'avr-vad';\n\n// Initialize the VAD with default options (Silero v5 model)\nconst vad = await RealTimeVAD.new({\n  model: 'v5', // or 'legacy'\n  positiveSpeechThreshold: 0.5,\n  negativeSpeechThreshold: 0.35,\n  preSpeechPadFrames: 1,\n  redemptionFrames: 8,\n  frameSamples: 1536,\n  minSpeechFrames: 3\n});\n\n// Process audio frames in real-time\nconst audioFrame = getAudioFrameFromMicrophone(); // Float32Array of 1536 samples at 16kHz\nconst result = await vad.processFrame(audioFrame);\n\nconsole.log(`Speech probability: ${result.probability}`);\nconsole.log(`Speech detected: ${result.msg === 'SPEECH_START' || result.msg === 'SPEECH_CONTINUE'}`);\n\n// Clean up when done\nvad.destroy();\n```\n\n### Non-Real-time Processing\n\n```typescript\nimport { NonRealTimeVAD } from 'avr-vad';\n\n// Initialize for batch processing\nconst vad = await NonRealTimeVAD.new({\n  model: 'v5',\n  positiveSpeechThreshold: 0.5,\n  negativeSpeechThreshold: 0.35\n});\n\n// Process entire audio buffer\nconst audioData = loadAudioData(); // Float32Array at 16kHz\nconst results = await vad.processAudio(audioData);\n\n// Get speech segments\nconst speechSegments = vad.getSpeechSegments(results);\nconsole.log(`Found ${speechSegments.length} speech segments`);\n\nspeechSegments.forEach((segment, i) => {\n  console.log(`Segment ${i + 1}: ${segment.start}ms - ${segment.end}ms`);\n});\n\n// Clean up\nvad.destroy();\n```\n\n## ⚙️ Configuration\n\n### Real-time VAD Options\n\n```typescript\ninterface RealTimeVADOptions {\n  /** Model version to use ('v5' | 'legacy') */\n  model?: 'v5' | 'legacy';\n  \n  /** Threshold for detecting speech start */\n  positiveSpeechThreshold?: number;\n  \n  /** Threshold for detecting speech end */\n  negativeSpeechThreshold?: number;\n  \n  /** Frames to include before speech detection */\n  preSpeechPadFrames?: number;\n  \n  /** Frames to wait before ending speech */\n  redemptionFrames?: number;\n  \n  /** Number of samples per frame (usually 1536 for 16kHz) */\n  frameSamples?: number;\n  \n  /** Minimum frames for valid speech */\n  minSpeechFrames?: number;\n}\n```\n\n### Non-Real-time VAD Options\n\n```typescript\ninterface NonRealTimeVADOptions {\n  /** Model version to use ('v5' | 'legacy') */\n  model?: 'v5' | 'legacy';\n  \n  /** Threshold for detecting speech start */\n  positiveSpeechThreshold?: number;\n  \n  /** Threshold for detecting speech end */\n  negativeSpeechThreshold?: number;\n}\n```\n\n### Default Values\n\n```typescript\n// Real-time VAD defaults\nconst defaultRealTimeOptions = {\n  model: 'v5',\n  positiveSpeechThreshold: 0.5,\n  negativeSpeechThreshold: 0.35,\n  preSpeechPadFrames: 1,\n  redemptionFrames: 8,\n  frameSamples: 1536,\n  minSpeechFrames: 3\n};\n\n// Non-real-time VAD defaults\nconst defaultNonRealTimeOptions = {\n  model: 'v5',\n  positiveSpeechThreshold: 0.5,\n  negativeSpeechThreshold: 0.35\n};\n```\n\n## 📊 Results and Messages\n\n### VAD Messages\n\nThe VAD returns different message types to indicate speech state changes:\n\n```typescript\nenum Message {\n  ERROR = 'ERROR',\n  SPEECH_START = 'SPEECH_START',\n  SPEECH_CONTINUE = 'SPEECH_CONTINUE', \n  SPEECH_END = 'SPEECH_END',\n  SILENCE = 'SILENCE'\n}\n```\n\n### Processing Results\n\n```typescript\ninterface VADResult {\n  /** Speech probability (0.0 - 1.0) */\n  probability: number;\n  \n  /** Message indicating speech state */\n  msg: Message;\n  \n  /** Audio data if speech segment ended */\n  audio?: Float32Array;\n}\n```\n\n### Speech Segments\n\n```typescript\ninterface SpeechSegment {\n  /** Start time in milliseconds */\n  start: number;\n  \n  /** End time in milliseconds */\n  end: number;\n  \n  /** Speech probability for this segment */\n  probability: number;\n}\n```\n\n## 🔧 Audio Utilities\n\nThe library includes various audio processing utilities:\n\n```typescript\nimport { utils, Resampler } from 'avr-vad';\n\n// Resample audio to 16kHz (required for VAD)\nconst resampler = new Resampler({\n  nativeSampleRate: 44100,\n  targetSampleRate: 16000,\n  targetFrameSize: 1536\n});\n\nconst resampledFrame = resampler.process(audioFrame);\n\n// Other utilities\nconst frameSize = utils.frameSize; // Get frame size for current sample rate\nconst audioBuffer = utils.concatArrays([frame1, frame2]); // Concatenate audio arrays\n```\n\n## 🎯 Advanced Examples\n\n### Real-time Speech Detection with Callbacks\n\n```typescript\nimport { RealTimeVAD, Message } from 'avr-vad';\n\nclass SpeechDetector {\n  private vad: RealTimeVAD;\n  private onSpeechStart?: (audio: Float32Array) => void;\n  private onSpeechEnd?: (audio: Float32Array) => void;\n\n  constructor(callbacks: {\n    onSpeechStart?: (audio: Float32Array) => void;\n    onSpeechEnd?: (audio: Float32Array) => void;\n  }) {\n    this.onSpeechStart = callbacks.onSpeechStart;\n    this.onSpeechEnd = callbacks.onSpeechEnd;\n  }\n\n  async initialize() {\n    this.vad = await RealTimeVAD.new({\n      positiveSpeechThreshold: 0.5,\n      negativeSpeechThreshold: 0.35\n      onSpeechStart: this.onSpeechStart,\n      onSpeechEnd: this.onSpeechEnd\n    });\n  }\n\n  async processFrame(audioFrame: Float32Array) {\n    const result = await this.vad.processFrame(audioFrame);\n    return result;\n  }\n\n  destroy() {\n    this.vad?.destroy();\n  }\n}\n\n// Usage\nconst detector = new SpeechDetector({\n  onSpeechStart: (audio) => console.log(`Speech started with ${audio.length} samples`),\n  onSpeechEnd: (audio) => console.log(`Speech ended with ${audio.length} samples`)\n});\n\nawait detector.initialize();\n```\n\n### Batch Processing Audio File\n\n```typescript\nimport { NonRealTimeVAD, utils } from 'avr-vad';\nimport * as fs from 'fs';\n\nasync function processAudioFile(filePath: string) {\n  // Load audio data (you'll need your own audio loading logic)\n  const audioData = loadWavFile(filePath); // Float32Array at 16kHz\n  \n  const vad = await NonRealTimeVAD.new({\n    model: 'v5',\n    positiveSpeechThreshold: 0.6,\n    negativeSpeechThreshold: 0.4\n  });\n\n  const results = await vad.processAudio(audioData);\n  const segments = vad.getSpeechSegments(results);\n\n  console.log(`Processing ${filePath}:`);\n  console.log(`Total audio duration: ${(audioData.length / 16000).toFixed(2)}s`);\n  console.log(`Speech segments found: ${segments.length}`);\n  \n  segments.forEach((segment, i) => {\n    const duration = ((segment.end - segment.start) / 1000).toFixed(2);\n    console.log(`  Segment ${i + 1}: ${segment.start}ms - ${segment.end}ms (${duration}s)`);\n  });\n\n  vad.destroy();\n  return segments;\n}\n```\n\n## 📝 Development\n\n### Requirements\n\n- Node.js >= 16.0.0\n- TypeScript >= 5.0.0\n\n### Build\n\n```bash\nnpm run build\n```\n\n### Test\n\n```bash\nnpm test\n```\n\n### Scripts\n\n```bash\nnpm run lint      # Run ESLint\nnpm run clean     # Clean build directory\nnpm run prepare   # Build before npm install (automatically run)\n```\n\n## 📁 Project Structure\n\n```\navr-vad/\n├── src/\n│   ├── index.ts                    # Main exports\n│   ├── real-time-vad.ts           # Real-time VAD implementation  \n│   └── common/\n│       ├── index.ts               # Common exports\n│       ├── frame-processor.ts     # Core ONNX processing\n│       ├── non-real-time-vad.ts  # Batch processing VAD\n│       ├── utils.ts               # Utility functions\n│       ├── resampler.ts           # Audio resampling\n├── dist/                          # Compiled JavaScript\n├── test/                          # Test files\n├── silero_vad_v5.onnx            # Silero VAD v5 model\n├── silero_vad_legacy.onnx        # Silero VAD legacy model\n└── package.json\n```\n\n## 🔧 Troubleshooting\n\n### Audio Format Requirements\n\nThe Silero VAD model requires:\n- **Sample rate**: 16kHz\n- **Channels**: Mono (single channel)\n- **Format**: Float32Array with values between -1.0 and 1.0\n- **Frame size**: 1536 samples (96ms at 16kHz)\n\n### Model Selection\n\n- **v5 model**: Latest version with improved accuracy\n- **legacy model**: Original model for compatibility\n\nUse the `Resampler` utility to convert audio to the required format:\n\n```typescript\nimport { Resampler } from 'avr-vad';\n\nconst resampler = new Resampler({\n  nativeSampleRate: 44100,    // Your audio sample rate\n  targetSampleRate: 16000,    // Required by VAD\n  targetFrameSize: 1536       // Required frame size\n});\n```\n\n### Performance Tips\n\n- Use appropriate thresholds for your use case\n- Consider using the legacy model for lower resource usage\n- For real-time applications, ensure your audio processing pipeline can handle 16kHz/1536 samples per frame\n- Use `redemptionFrames` to avoid choppy speech detection\n\n## Acknowledgments\n\n- [Silero Models](https://github.com/snakers4/silero-vad) for the excellent VAD model\n- [ONNX Runtime](https://onnxruntime.ai/) for model inference\n- The open source community for supporting libraries \n\n## Support & Community\n\n*   **Website:** [https://agentvoiceresponse.com](https://agentvoiceresponse.com) - Official website.\n*   **GitHub:** [https://github.com/agentvoiceresponse](https://github.com/agentvoiceresponse) - Report issues, contribute code.\n*   **Discord:** [https://discord.gg/DFTU69Hg74](https://discord.gg/DFTU69Hg74) - Join the community discussion.\n*   **Docker Hub:** [https://hub.docker.com/u/agentvoiceresponse](https://hub.docker.com/u/agentvoiceresponse) - Find Docker images.\n*   **NPM:** [https://www.npmjs.com/~agentvoiceresponse](https://www.npmjs.com/~agentvoiceresponse) - Browse our packages.\n*   **Wiki:** [https://wiki.agentvoiceresponse.com/en/home](https://wiki.agentvoiceresponse.com/en/home) - Project documentation and guides.\n\n## Support AVR\n\nAVR is free and open-source. If you find it valuable, consider supporting its development:\n\n<a href=\"https://ko-fi.com/agentvoiceresponse\" target=\"_blank\"><img src=\"https://ko-fi.com/img/githubbutton_sm.svg\" alt=\"Support us on Ko-fi\"></a>\n\n## License\n\nMIT License - see the [LICENSE.md](LICENSE.md) file for details.","readmeFilename":"README.md"}