{"version":3,"sources":["../src/vad.ts"],"sourcesContent":["// SPDX-FileCopyrightText: 2024 LiveKit, Inc.\n//\n// SPDX-License-Identifier: Apache-2.0\nimport {\n ExpFilter,\n VADEventType,\n VADStream as baseStream,\n VAD as baseVAD,\n log,\n mergeFrames,\n} from '@livekit/agents';\nimport { AudioFrame, AudioResampler, AudioResamplerQuality } from '@livekit/rtc-node';\nimport type { InferenceSession } from 'onnxruntime-node';\nimport type { SampleRate } from './onnx_model.js';\nimport { OnnxModel, newInferenceSession } from './onnx_model.js';\n\nconst SLOW_INFERENCE_THRESHOLD = 200; // late by 200ms\n\nexport interface VADOptions {\n /** Minimum duration of speech to start a new speech chunk */\n minSpeechDuration: number;\n /** At the end of each speech, wait this duration before ending the speech */\n minSilenceDuration: number;\n /** Duration of padding to add to the beginning of each speech chunk */\n prefixPaddingDuration: number;\n /** Maximum duration of speech to keep in the buffer */\n maxBufferedSpeech: number;\n /** Maximum duration of speech to keep in the buffer*/\n activationThreshold: number;\n /** Sample rate for the inference (only 8KHz and 16KHz are supported) */\n sampleRate: SampleRate;\n /** Force the use of CPU for inference */\n forceCPU: boolean;\n}\n\nconst defaultVADOptions: VADOptions = {\n minSpeechDuration: 50,\n minSilenceDuration: 550,\n prefixPaddingDuration: 500,\n maxBufferedSpeech: 60000, // 60 seconds\n activationThreshold: 0.5,\n sampleRate: 16000,\n forceCPU: true,\n};\n\nexport class VAD extends baseVAD {\n #session: InferenceSession;\n #opts: VADOptions;\n #streams: VADStream[];\n label = 'silero.VAD';\n\n constructor(session: InferenceSession, opts: VADOptions) {\n super({ updateInterval: 32 });\n this.#session = session;\n this.#opts = opts;\n this.#streams = [];\n }\n\n /**\n * Updates the VAD options with new values.\n *\n * @param opts - Partial options object containing the values to update\n * @remarks\n * This method will merge the provided options with existing options and update all active streams.\n * Only the properties specified in opts will be updated, other properties retain their current values.\n */\n updateOptions(opts: Partial): void {\n this.#opts = { ...this.#opts, ...opts };\n for (const stream of this.#streams) {\n stream.updateOptions(this.#opts);\n }\n }\n\n /**\n * Load and initialize the Silero VAD model.\n *\n * This method loads the ONNX model and prepares it for inference. When options are not provided,\n * sane defaults are used.\n *\n * @remarks\n * This method may take time to load the model into memory.\n * It is recommended to call this method inside your prewarm mechanism.\n *\n * @example\n * ```ts\n * export default defineAgent({\n * prewarm: async (proc: JobProcess) => {\n * proc.userData.vad = await VAD.load();\n * },\n * entry: async (ctx: JobContext) => {\n * const vad = ctx.proc.userData.vad! as VAD;\n * // the rest of your agent logic\n * },\n * });\n * ```\n *\n * @param options -\n * @returns Promise\\<{@link VAD}\\>: An instance of the VAD class ready for streaming.\n */\n static async load(opts: Partial = {}): Promise {\n const mergedOpts: VADOptions = { ...defaultVADOptions, ...opts };\n const session = await newInferenceSession(mergedOpts.forceCPU);\n return new VAD(session, mergedOpts);\n }\n\n override get minSilenceDuration(): number {\n return this.#opts.minSilenceDuration;\n }\n\n stream(): VADStream {\n const stream = new VADStream(\n this,\n this.#opts,\n new OnnxModel(this.#session, this.#opts.sampleRate),\n );\n this.#streams.push(stream);\n return stream;\n }\n}\n\nexport class VADStream extends baseStream {\n #opts: VADOptions;\n #model: OnnxModel;\n #inputSampleRate: number;\n #speechBuffer: Int16Array | null;\n #speechBufferMaxReached: boolean;\n #prefixPaddingSamples: number;\n #task: Promise;\n #expFilter = new ExpFilter(0.35);\n #extraInferenceTime = 0;\n #logger = log();\n\n constructor(vad: VAD, opts: VADOptions, model: OnnxModel) {\n super(vad);\n this.#opts = opts;\n this.#model = model;\n this.#inputSampleRate = 0;\n this.#speechBuffer = null;\n this.#speechBufferMaxReached = false;\n this.#prefixPaddingSamples = 0;\n\n let resampler: AudioResampler | null = null;\n const runInference = async () => {\n let inferenceData = new Float32Array(this.#model.windowSizeSamples);\n\n // a copy is exposed to the user in END_OF_SPEECH\n let speechBufferIndex = 0;\n\n // \"pub\" means public, these values are exposed to the users through events\n let pubSpeaking = false;\n let pubSpeechDuration = 0;\n let pubSilenceDuration = 0;\n let pubCurrentSample = 0;\n let pubTimestamp = 0;\n let speechThresholdDuration = 0;\n let silenceThresholdDuration = 0;\n\n let inputFrames: AudioFrame[] = [];\n let inferenceFrames: AudioFrame[] = [];\n\n // used to avoid drift when the sampleRate ratio is not an integer\n let inputCopyRemainingFrac = 0.0;\n\n const resetState = () => {\n this.#model.reset();\n this.#expFilter = new ExpFilter(0.35);\n\n speechBufferIndex = 0;\n this.#speechBufferMaxReached = false;\n this.#speechBuffer?.fill(0);\n\n pubSpeaking = false;\n pubSpeechDuration = 0;\n pubSilenceDuration = 0;\n pubCurrentSample = 0;\n pubTimestamp = 0;\n speechThresholdDuration = 0;\n silenceThresholdDuration = 0;\n\n inputFrames = [];\n inferenceFrames = [];\n inputCopyRemainingFrac = 0.0;\n this.#extraInferenceTime = 0;\n\n resampler?.close();\n resampler =\n this.#inputSampleRate && this.#opts.sampleRate !== this.#inputSampleRate\n ? new AudioResampler(\n this.#inputSampleRate,\n this.#opts.sampleRate,\n 1,\n AudioResamplerQuality.QUICK,\n )\n : null;\n };\n\n while (!this.closed) {\n const { done, value: frame } = await this.inputReader.read();\n if (done) {\n break;\n }\n\n if (typeof frame === 'symbol') {\n resetState();\n continue;\n }\n\n if (!this.#inputSampleRate || !this.#speechBuffer) {\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\n if (this.#opts.sampleRate !== this.#inputSampleRate) {\n // resampling needed: the input sample rate isn't the same as the model's\n // sample rate used for inference\n resampler = new AudioResampler(\n this.#inputSampleRate,\n this.#opts.sampleRate,\n 1,\n AudioResamplerQuality.QUICK, // VAD doesn't need high quality\n );\n }\n } else if (frame.sampleRate !== this.#inputSampleRate) {\n this.#logger.error('a frame with a different sample rate was already published');\n continue;\n }\n\n inputFrames.push(frame);\n if (resampler) {\n inferenceFrames.push(...resampler.push(frame));\n } else {\n inferenceFrames.push(frame);\n }\n\n while (!this.closed) {\n const startTime = process.hrtime.bigint();\n const availableInferenceSamples = inferenceFrames\n .map((x) => x.samplesPerChannel)\n .reduce((acc, x) => acc + x, 0);\n\n if (availableInferenceSamples < this.#model.windowSizeSamples) {\n break; // not enough samples to run inference\n }\n\n const inputFrame = mergeFrames(inputFrames);\n const inferenceFrame = mergeFrames(inferenceFrames);\n\n // convert data to f32\n inferenceData = Float32Array.from(\n inferenceFrame.data.subarray(0, this.#model.windowSizeSamples),\n (x) => x / 32767,\n );\n\n const p = await this.#model\n .run(inferenceData)\n .then((data) => this.#expFilter.apply(1, data));\n\n const windowDuration = (this.#model.windowSizeSamples / this.#opts.sampleRate) * 1000;\n pubCurrentSample += this.#model.windowSizeSamples;\n pubTimestamp += windowDuration;\n const resamplingRatio = this.#inputSampleRate / this.#model.sampleRate;\n const toCopy = this.#model.windowSizeSamples * resamplingRatio + inputCopyRemainingFrac;\n const toCopyInt = Math.trunc(toCopy);\n inputCopyRemainingFrac = toCopy - toCopyInt;\n\n // copy the inference window to the speech buffer\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 = Number((process.hrtime.bigint() - startTime) / BigInt(1000000));\n this.#extraInferenceTime = Math.max(\n 0,\n this.#extraInferenceTime + inferenceDuration - windowDuration,\n );\n if (this.#extraInferenceTime > SLOW_INFERENCE_THRESHOLD) {\n this.#logger\n .child({ delay: this.#extraInferenceTime })\n .warn('VAD inference is slower than realtime');\n }\n\n if (pubSpeaking) {\n pubSpeechDuration += windowDuration;\n } else {\n pubSilenceDuration += windowDuration;\n }\n\n if (\n !this.sendVADEvent({\n type: VADEventType.INFERENCE_DONE,\n samplesIndex: pubCurrentSample,\n timestamp: pubTimestamp,\n silenceDuration: pubSilenceDuration,\n speechDuration: pubSpeechDuration,\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: silenceThresholdDuration,\n rawAccumulatedSpeech: speechThresholdDuration,\n })\n ) {\n continue;\n }\n\n const resetWriteCursor = () => {\n if (!this.#speechBuffer) throw new Error('speechBuffer is empty');\n if (speechBufferIndex <= this.#prefixPaddingSamples) {\n return;\n }\n\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 copySpeechBuffer = (): AudioFrame => {\n if (!this.#speechBuffer) throw new Error('speechBuffer is empty');\n return new AudioFrame(\n this.#speechBuffer.subarray(0, speechBufferIndex),\n this.#inputSampleRate,\n 1,\n speechBufferIndex,\n );\n };\n\n if (p > this.#opts.activationThreshold) {\n speechThresholdDuration += windowDuration;\n silenceThresholdDuration = 0;\n if (!pubSpeaking && speechThresholdDuration >= this.#opts.minSpeechDuration) {\n pubSpeaking = true;\n pubSilenceDuration = 0;\n pubSpeechDuration = speechThresholdDuration;\n\n if (\n !this.sendVADEvent({\n type: VADEventType.START_OF_SPEECH,\n samplesIndex: pubCurrentSample,\n timestamp: pubTimestamp,\n silenceDuration: pubSilenceDuration,\n speechDuration: pubSpeechDuration,\n probability: p,\n inferenceDuration,\n frames: [copySpeechBuffer()],\n speaking: pubSpeaking,\n rawAccumulatedSilence: 0,\n rawAccumulatedSpeech: 0,\n })\n ) {\n continue;\n }\n }\n } else {\n silenceThresholdDuration += windowDuration;\n speechThresholdDuration = 0;\n\n if (!pubSpeaking) {\n resetWriteCursor();\n }\n\n if (pubSpeaking && silenceThresholdDuration > this.#opts.minSilenceDuration) {\n pubSpeaking = false;\n pubSpeechDuration = 0;\n pubSilenceDuration = silenceThresholdDuration;\n\n if (\n !this.sendVADEvent({\n type: VADEventType.END_OF_SPEECH,\n samplesIndex: pubCurrentSample,\n timestamp: pubTimestamp,\n silenceDuration: pubSilenceDuration,\n speechDuration: pubSpeechDuration,\n probability: p,\n inferenceDuration,\n frames: [copySpeechBuffer()],\n speaking: pubSpeaking,\n rawAccumulatedSilence: 0,\n rawAccumulatedSpeech: 0,\n })\n ) {\n continue;\n }\n\n resetWriteCursor();\n }\n }\n\n inputFrames = [];\n inferenceFrames = [];\n\n if (inputFrame.data.length > toCopyInt) {\n const data = inputFrame.data.subarray(toCopyInt);\n inputFrames.push(\n new AudioFrame(data, this.#inputSampleRate, 1, Math.trunc(data.length / 2)),\n );\n }\n if (inferenceFrame.data.length > this.#model.windowSizeSamples) {\n const data = inferenceFrame.data.subarray(this.#model.windowSizeSamples);\n inferenceFrames.push(\n new AudioFrame(data, this.#opts.sampleRate, 1, Math.trunc(data.length / 2)),\n );\n }\n }\n }\n };\n this.#task = runInference()\n .catch((error) => {\n this.#logger.error(error, 'Error in VAD inference task');\n if (!this.closed) this.close();\n })\n .finally(() => resampler?.close());\n }\n\n /**\n * Update the VAD options\n *\n * @param opts - Partial options object containing the values to update\n * @remarks\n * This method allows you to update the VAD options after the VAD object has been created\n */\n updateOptions(opts: Partial) {\n const oldMaxBufferedSpeech = this.#opts.maxBufferedSpeech;\n this.#opts = { ...this.#opts, ...opts };\n\n if (this.#inputSampleRate) {\n // Assert speech buffer exists\n if (this.#speechBuffer === null) throw new Error('speechBuffer is null');\n\n // Resize speech buffer\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 resizedBuffer = new Int16Array(bufferSize);\n resizedBuffer.set(\n this.#speechBuffer.subarray(0, Math.min(this.#speechBuffer.length, bufferSize)),\n );\n this.#speechBuffer = resizedBuffer;\n\n // Determine if max has been reached\n if (this.#opts.maxBufferedSpeech > oldMaxBufferedSpeech) {\n this.#speechBufferMaxReached = false;\n }\n }\n 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