{"version":3,"sources":["../../src/inference/vad.ts"],"sourcesContent":["// SPDX-FileCopyrightText: 2026 LiveKit, Inc.\n//\n// SPDX-License-Identifier: Apache-2.0\n\n/**\n * Voice Activity Detection backed by `@livekit/local-inference`.\n *\n * Provides the same streaming VAD shape as `plugins/silero` but routes\n * inference through the bundled native model so a default instance can be\n * auto-provisioned by `AgentSession` without an explicit plugin import.\n */\nimport { AudioFrame, AudioResampler, AudioResamplerQuality } from '@livekit/rtc-node';\nimport { log } from '../log.js';\nimport { VAD as BaseVAD, VADStream as BaseVADStream, VADEventType } from '../vad.js';\nimport { _getLocalInferenceModule } from './_warmup.js';\n\nconst SLOW_INFERENCE_THRESHOLD_MS = 200;\nconst MODEL_SAMPLE_RATE = 16000;\n\nexport type VADModels = 'silero';\n\nexport interface VADOptions {\n /** Minimum speech duration (ms) before reporting START_OF_SPEECH. */\n minSpeechDuration: number;\n /** Trailing silence (ms) before reporting END_OF_SPEECH. */\n minSilenceDuration: number;\n /** Pre-roll (ms) included in the speech buffer ahead of START_OF_SPEECH. */\n prefixPaddingDuration: number;\n /** Maximum (ms) of buffered speech per utterance. */\n maxBufferedSpeech: number;\n /** Sigmoid probability threshold for activation. */\n activationThreshold: number;\n /** Sigmoid probability threshold for deactivation (defaults to\n * `max(activationThreshold - 0.15, 0.01)`). */\n deactivationThreshold: number;\n}\n\nconst defaultVADOptions: VADOptions = {\n minSpeechDuration: 50,\n // 250ms (= MIN_SILENCE_DURATION_MS + 50) so the default satisfies the audio\n // end-of-turn detector's silence-window requirement out of the box.\n minSilenceDuration: 250,\n prefixPaddingDuration: 500,\n maxBufferedSpeech: 60_000,\n activationThreshold: 0.5,\n deactivationThreshold: 0.35,\n};\n\nexport class VAD extends BaseVAD {\n protected _opts: VADOptions;\n protected _model: VADModels;\n label = 'inference.VAD';\n // Live streams, tracked weakly so they don't outlive their consumers. JS\n // `WeakSet` isn't iterable, so we hold `WeakRef`s in a `Set` and prune dead\n // entries on iteration.\n #streams = new Set>();\n\n constructor(opts: Partial & { model?: VADModels } = {}) {\n super({ updateInterval: 32 });\n const model: VADModels = opts.model ?? 'silero';\n if (model !== 'silero') {\n throw new Error(`Unknown VAD model: ${String(model)}. Supported: 'silero'.`);\n }\n if (opts.deactivationThreshold !== undefined && opts.deactivationThreshold <= 0) {\n throw new Error('deactivationThreshold must be greater than 0');\n }\n this._model = model;\n const activation = opts.activationThreshold ?? defaultVADOptions.activationThreshold;\n this._opts = {\n ...defaultVADOptions,\n ...opts,\n activationThreshold: activation,\n deactivationThreshold: opts.deactivationThreshold ?? Math.max(activation - 0.15, 0.01),\n };\n }\n\n get model(): string {\n return this._model;\n }\n\n get provider(): string {\n return 'livekit-local-inference';\n }\n\n override get minSilenceDuration(): number {\n return this._opts.minSilenceDuration;\n }\n\n /** Update one or more knobs at runtime, propagating to live streams. */\n updateOptions(opts: Partial): void {\n this._opts = { ...this._opts, ...opts };\n for (const ref of this.#streams) {\n const stream = ref.deref();\n if (stream === undefined) {\n this.#streams.delete(ref);\n continue;\n }\n stream.updateOptions(opts);\n }\n }\n\n stream(): BaseVADStream {\n // Each stream owns its own options snapshot so its `updateOptions` can read\n // the prior `maxBufferedSpeech` before this VAD's copy is mutated.\n const stream = new InferenceVADStream(this, { ...this._opts });\n this.#streams.add(new WeakRef(stream));\n return stream;\n }\n}\n\nclass InferenceVADStream extends BaseVADStream {\n private _opts: VADOptions;\n private _logger = log();\n private _nativeVad:\n | ReturnType>['createVad']>\n | undefined;\n private _windowSamples: number;\n private _inputSampleRate = 0;\n private _resampler: AudioResampler | undefined;\n private _speechBuffer: Int16Array | null = null;\n private _speechBufferMaxReached = false;\n private _prefixPaddingSamples = 0;\n private _pumpTask: Promise;\n\n constructor(parent: VAD, opts: VADOptions) {\n super(parent);\n this._opts = opts;\n const mod = _getLocalInferenceModule();\n if (mod === undefined) {\n this._logger.warn(\n 'inference.VAD created without @livekit/local-inference; stream will be a no-op',\n );\n this._windowSamples = 512;\n } else {\n this._nativeVad = mod.createVad();\n this._windowSamples = mod.VAD_WINDOW_SAMPLES;\n }\n this._pumpTask = this._pump().catch((err) => {\n this._logger.error(\n { err: err instanceof Error ? err.message : String(err) },\n 'VAD pump failed',\n );\n });\n }\n\n /**\n * Apply updated options to this live stream. Once the input sample rate is\n * known, recomputes the prefix-padding pre-roll and resizes the speech\n * buffer in place, preserving any audio already accumulated.\n */\n updateOptions(opts: Partial): void {\n const oldMaxBufferedSpeech = this._opts.maxBufferedSpeech;\n this._opts = { ...this._opts, ...opts };\n\n if (this._inputSampleRate && this._speechBuffer !== null) {\n this._prefixPaddingSamples = Math.trunc(\n (this._opts.prefixPaddingDuration * this._inputSampleRate) / 1000,\n );\n const bufferSize =\n Math.trunc((this._opts.maxBufferedSpeech * this._inputSampleRate) / 1000) +\n this._prefixPaddingSamples;\n const resized = new Int16Array(bufferSize);\n resized.set(this._speechBuffer.subarray(0, Math.min(this._speechBuffer.length, bufferSize)));\n this._speechBuffer = resized;\n\n if (this._opts.maxBufferedSpeech > oldMaxBufferedSpeech) {\n this._speechBufferMaxReached = false;\n }\n }\n }\n\n private async _pump(): Promise {\n let pubSpeaking = false;\n let pubSpeechDurationMs = 0;\n let pubSilenceDurationMs = 0;\n let pubCurrentSample = 0;\n let pubTimestampMs = 0;\n let speechThresholdDurationMs = 0;\n let silenceThresholdDurationMs = 0;\n let inputFrames: AudioFrame[] = [];\n let inferenceFrames: AudioFrame[] = [];\n let inputCopyRemainingFrac = 0;\n let extraInferenceTime = 0;\n // Write cursor into `_speechBuffer`. The buffer holds:\n // [ ...prefix-padding (sliding pre-roll) ..., ...active speech... ]\n // and is reset on END_OF_SPEECH (and on silence while idle) so the next\n // turn starts from a fresh pre-roll window.\n let speechBufferIndex = 0;\n\n const resetWriteCursor = () => {\n if (this._speechBuffer === null) return;\n if (speechBufferIndex <= this._prefixPaddingSamples) return;\n // Slide the most-recent `prefixPaddingSamples` samples to the head\n // of the buffer so the next utterance has continuous pre-roll\n // context (the audio that immediately preceded START_OF_SPEECH).\n const paddingData = this._speechBuffer.subarray(\n speechBufferIndex - this._prefixPaddingSamples,\n speechBufferIndex,\n );\n this._speechBuffer.set(paddingData, 0);\n speechBufferIndex = this._prefixPaddingSamples;\n this._speechBufferMaxReached = false;\n };\n\n const resetState = () => {\n this._nativeVad?.reset();\n\n speechBufferIndex = 0;\n this._speechBufferMaxReached = false;\n this._speechBuffer?.fill(0);\n\n pubSpeaking = false;\n pubSpeechDurationMs = 0;\n pubSilenceDurationMs = 0;\n pubCurrentSample = 0;\n pubTimestampMs = 0;\n speechThresholdDurationMs = 0;\n silenceThresholdDurationMs = 0;\n\n inputFrames = [];\n inferenceFrames = [];\n inputCopyRemainingFrac = 0;\n extraInferenceTime = 0;\n\n this._resampler?.close?.();\n if (this._inputSampleRate && this._inputSampleRate !== MODEL_SAMPLE_RATE) {\n this._resampler = new AudioResampler(\n this._inputSampleRate,\n MODEL_SAMPLE_RATE,\n 1,\n AudioResamplerQuality.QUICK,\n );\n } else {\n this._resampler = undefined;\n }\n };\n\n const copySpeechBuffer = (): AudioFrame => {\n if (this._speechBuffer === null) {\n return new AudioFrame(new Int16Array(0), this._inputSampleRate, 1, 0);\n }\n return new AudioFrame(\n this._speechBuffer.subarray(0, speechBufferIndex),\n this._inputSampleRate,\n 1,\n speechBufferIndex,\n );\n };\n\n while (!this.closed) {\n const { done, value: frame } = await this.inputReader.read();\n if (done) break;\n if (typeof frame === 'symbol') {\n resetState();\n continue;\n }\n\n if (!this._inputSampleRate) {\n this._inputSampleRate = frame.sampleRate;\n this._prefixPaddingSamples = Math.trunc(\n (this._opts.prefixPaddingDuration * this._inputSampleRate) / 1000,\n );\n const bufferSize =\n Math.trunc((this._opts.maxBufferedSpeech * this._inputSampleRate) / 1000) +\n this._prefixPaddingSamples;\n this._speechBuffer = new Int16Array(bufferSize);\n if (this._inputSampleRate !== MODEL_SAMPLE_RATE) {\n this._resampler = new AudioResampler(\n this._inputSampleRate,\n MODEL_SAMPLE_RATE,\n 1,\n AudioResamplerQuality.QUICK,\n );\n }\n } else if (frame.sampleRate !== this._inputSampleRate) {\n this._logger.error('a frame with a different sample rate was already pushed');\n continue;\n }\n\n if (this._speechBuffer === null) continue;\n\n inputFrames.push(frame);\n if (this._resampler !== undefined) {\n inferenceFrames.push(...this._resampler.push(frame));\n } else {\n inferenceFrames.push(frame);\n }\n\n while (!this.closed) {\n const startTime = performance.now();\n const availableInferenceSamples = inferenceFrames.reduce(\n (acc, f) => acc + f.samplesPerChannel,\n 0,\n );\n if (availableInferenceSamples < this._windowSamples) break;\n\n const inputFrame = mergeFrames(inputFrames);\n const inferenceFrame = mergeFrames(inferenceFrames);\n const inferenceWindow = inferenceFrame.data.subarray(0, this._windowSamples);\n\n let p = 0.0;\n if (this._nativeVad !== undefined) {\n p = await this._nativeVad.predict(inferenceWindow);\n }\n\n const windowDurationMs = (this._windowSamples / MODEL_SAMPLE_RATE) * 1000;\n pubCurrentSample += this._windowSamples;\n pubTimestampMs += windowDurationMs;\n const resamplingRatio = this._inputSampleRate / MODEL_SAMPLE_RATE;\n const toCopy = this._windowSamples * resamplingRatio + inputCopyRemainingFrac;\n const toCopyInt = Math.trunc(toCopy);\n inputCopyRemainingFrac = toCopy - toCopyInt;\n\n // Append the input-rate samples we just consumed into the\n // speech buffer so START_OF_SPEECH / END_OF_SPEECH events can\n // hand downstream consumers (STT, transcription) the prefix-\n // padded audio they need.\n const availableSpace = this._speechBuffer.length - speechBufferIndex;\n const toCopyBuffer = Math.min(toCopyInt, availableSpace);\n if (toCopyBuffer > 0) {\n this._speechBuffer.set(inputFrame.data.subarray(0, toCopyBuffer), speechBufferIndex);\n speechBufferIndex += toCopyBuffer;\n } else if (!this._speechBufferMaxReached) {\n this._speechBufferMaxReached = true;\n this._logger.warn(\n 'maxBufferedSpeech reached, ignoring further data for the current speech input',\n );\n }\n\n const inferenceDuration = performance.now() - startTime;\n extraInferenceTime = Math.max(0, extraInferenceTime + inferenceDuration - windowDurationMs);\n // Guard on the per-window inference duration (not the accumulated\n // slack); the accumulated value is still surfaced as the delay.\n if (inferenceDuration > SLOW_INFERENCE_THRESHOLD_MS) {\n this._logger.warn(\n { extraInferenceTimeMs: extraInferenceTime },\n 'VAD inference is slower than realtime',\n );\n }\n\n if (pubSpeaking) pubSpeechDurationMs += windowDurationMs;\n else pubSilenceDurationMs += windowDurationMs;\n\n this.sendVADEvent({\n type: VADEventType.INFERENCE_DONE,\n samplesIndex: pubCurrentSample,\n timestamp: pubTimestampMs,\n silenceDuration: pubSilenceDurationMs,\n speechDuration: pubSpeechDurationMs,\n probability: p,\n inferenceDuration,\n frames: [\n new AudioFrame(\n inputFrame.data.subarray(0, toCopyInt),\n this._inputSampleRate,\n 1,\n toCopyInt,\n ),\n ],\n speaking: pubSpeaking,\n rawAccumulatedSilence: silenceThresholdDurationMs,\n rawAccumulatedSpeech: speechThresholdDurationMs,\n });\n\n if (\n p >= this._opts.activationThreshold ||\n (pubSpeaking && p > this._opts.deactivationThreshold)\n ) {\n speechThresholdDurationMs += windowDurationMs;\n silenceThresholdDurationMs = 0;\n if (!pubSpeaking && speechThresholdDurationMs >= this._opts.minSpeechDuration) {\n pubSpeaking = true;\n pubSilenceDurationMs = 0;\n pubSpeechDurationMs = speechThresholdDurationMs;\n this.sendVADEvent({\n type: VADEventType.START_OF_SPEECH,\n samplesIndex: pubCurrentSample,\n timestamp: pubTimestampMs,\n silenceDuration: pubSilenceDurationMs,\n speechDuration: pubSpeechDurationMs,\n probability: p,\n inferenceDuration,\n frames: [copySpeechBuffer()],\n speaking: true,\n rawAccumulatedSilence: 0,\n rawAccumulatedSpeech: 0,\n });\n }\n } else {\n silenceThresholdDurationMs += windowDurationMs;\n speechThresholdDurationMs = 0;\n // Keep a sliding pre-roll window while we're not in active\n // speech — without this the buffer would fill with idle\n // silence and the next START_OF_SPEECH would lose its\n // prefix-padding context.\n if (!pubSpeaking) resetWriteCursor();\n if (pubSpeaking && silenceThresholdDurationMs >= this._opts.minSilenceDuration) {\n pubSpeaking = false;\n pubSilenceDurationMs = silenceThresholdDurationMs;\n this.sendVADEvent({\n type: VADEventType.END_OF_SPEECH,\n samplesIndex: pubCurrentSample,\n timestamp: pubTimestampMs,\n silenceDuration: pubSilenceDurationMs,\n speechDuration: Math.max(0, pubSpeechDurationMs - silenceThresholdDurationMs),\n probability: p,\n inferenceDuration,\n frames: [copySpeechBuffer()],\n speaking: false,\n rawAccumulatedSilence: 0,\n rawAccumulatedSpeech: 0,\n });\n pubSpeechDurationMs = 0;\n resetWriteCursor();\n }\n }\n\n inputFrames = [];\n inferenceFrames = [];\n if (inputFrame.data.length > toCopyInt) {\n const data = inputFrame.data.subarray(toCopyInt);\n inputFrames.push(new AudioFrame(data, this._inputSampleRate, 1, Math.trunc(data.length)));\n }\n if (inferenceFrame.data.length > this._windowSamples) {\n const data = inferenceFrame.data.subarray(this._windowSamples);\n inferenceFrames.push(new AudioFrame(data, MODEL_SAMPLE_RATE, 1, Math.trunc(data.length)));\n }\n }\n }\n this._resampler?.close?.();\n }\n}\n\n/** Minimal frame-merging helper. The silero plugin uses `mergeFrames` from\n * the agents package — for the inference VAD we keep a local copy to avoid\n * an import cycle through `index.ts`. */\nfunction mergeFrames(frames: AudioFrame[]): AudioFrame {\n if (frames.length === 1) return frames[0]!;\n const sampleRate = frames[0]!.sampleRate;\n const channels = frames[0]!.channels;\n let total = 0;\n for (const f of frames) total += f.samplesPerChannel;\n const buf = new Int16Array(total * channels);\n let offset = 0;\n for (const f of frames) {\n buf.set(f.data, offset);\n offset += f.samplesPerChannel * channels;\n }\n return new AudioFrame(buf, sampleRate, channels, 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