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Generate one at\n> [developers.ai-coustics.com](https://developers.ai-coustics.com).\n\n## Installation\n\n```bash\nnpm install @ai-coustics/aic-sdk\n```\n\nPrebuilt binaries are published for macOS (x64, arm64), Linux (x64, arm64, glibc) and\nWindows (x64, arm64, MSVC). The native SDK is linked statically, so there is no separate\nlibrary to install or put on a search path.\n\n## Quick start\n\n```javascript\nconst { Model, Processor } = require('@ai-coustics/aic-sdk')\n\nasync function main() {\n  // Download a model once, or fetch one manually from https://artifacts.ai-coustics.io\n  const modelPath = await Model.download('quail-vf-2.2-s-16khz', './models')\n  const model = Model.fromFile(modelPath)\n\n  // Use the model's optimal configuration for the lowest delay\n  const sampleRate = model.getOptimalSampleRate()\n  const blockSize = model.getOptimalBlockSize(sampleRate)\n\n  const processor = new Processor(model, process.env.AIC_SDK_LICENSE)\n  processor.initialize(sampleRate, blockSize)\n\n  // Enhance a mono block in place\n  const audio = new Float32Array(blockSize)\n  processor.process(audio)\n}\n\nmain()\n```\n\nProcessing is mono. For multichannel audio, mix down to mono or use one processor per\nchannel. Initialize each instance before passing audio. Blocks must contain exactly\n`blockSize` samples. Set the third `initialize` argument, `variableBlockSize`, to `true`\nto allow shorter blocks; longer blocks are always rejected. This also applies to VAD\nprocessing and analyzer buffering.\n\nThe following snippets assume an enclosing `async` function. CommonJS files do not support\ntop-level `await`.\n\nRunnable scripts for enhancement, VAD, analysis and whole-file processing, synchronous and\nasync, are in [`examples/`](examples).\n\n## Models\n\nAvailable models and their IDs are listed at\n[artifacts.ai-coustics.io](https://artifacts.ai-coustics.io). Each class accepts the model types\nlisted below and rejects other types:\n\n| Class                         | Accepted models     |\n| ----------------------------- | ------------------- |\n| `Processor`, `ProcessorAsync` | enhancement, bypass |\n| `Vad`, `VadAsync`             | dedicated VAD       |\n| `Analyzer`                    | analysis            |\n\n`Model.download` runs on Node's libuv thread pool and returns a promise for the model's path.\n`Model.fromFile` memory-maps the file. Do not modify or delete it while the model or any\ninstance created from it is still alive.\n\n## Enhancement\n\nParameters can be changed while audio is being processed, through a context handle:\n\n```javascript\nconst { ProcessorParameter } = require('@ai-coustics/aic-sdk')\n\nconst context = processor.getContext()\n\ncontext.setParameter(ProcessorParameter.EnhancementLevel, 0.8)\ncontext.setParameter(ProcessorParameter.Bypass, 0)\n\nconsole.log(context.getParameter(ProcessorParameter.EnhancementLevel))\n\n// Get the audio delay in samples to align the output with other streams\nconsole.log(context.getAudioDelay())\n\n// Clear internal state on a stream discontinuity or seek\ncontext.reset()\n```\n\n## Async processing\n\n`ProcessorAsync` and `VadAsync` run initialization and processing on Node's libuv thread pool.\nAwait these calls to keep the event loop available for other work, such as network requests.\nConstruction and disposal remain synchronous.\n\nUse `Processor` or `Vad` on a dedicated worker thread, or in a batch script that can block\nthe calling thread. These classes process each block without a promise or input copy.\n\n```javascript\nconst { Model, ProcessorAsync } = require('@ai-coustics/aic-sdk')\n\nconst processor = await new ProcessorAsync(model, process.env.AIC_SDK_LICENSE).withConfig(sampleRate, blockSize)\n\n// The input remains unmodified; the promise resolves to the enhanced samples.\nconst audio = new Float32Array(blockSize)\nconst enhanced = await processor.process(audio)\n```\n\nBoth async classes copy the input before queuing work and return a new `Float32Array`.\n`ProcessorAsync.process` returns enhanced samples; `VadAsync.process` returns the original\nsamples. The caller's input remains unmodified.\n\nContext creation is asynchronous. The returned context's methods are synchronous and can\nbe used while processing runs:\n\n```javascript\nconst context = await processor.getContext()\ncontext.setParameter(ProcessorParameter.EnhancementLevel, 0.8)\n```\n\n### Running several streams\n\nUse one instance per stream and await each operation before submitting the next. Concurrent\ncalls on one instance are not guaranteed to execute in submission order. Separate instances\ncan process streams concurrently.\n\n```javascript\nconst processors = await Promise.all(\n  streams.map(() => new ProcessorAsync(model, licenseKey).withConfig(sampleRate, blockSize)),\n)\n\nconst enhanced = await Promise.all(processors.map((processor, i) => processor.process(blocks[i])))\n```\n\nWork runs on Node's libuv thread pool, which is four threads by default and shared with\n`fs`, `dns` and `crypto`. To run more streams in parallel, raise `UV_THREADPOOL_SIZE` before\nNode starts:\n\n```bash\nUV_THREADPOOL_SIZE=16 node server.js\n```\n\n`AIC_NUM_THREADS` does not apply to these bindings; async audio work uses libuv.\n\n## Voice activity detection\n\nVAD runs a dedicated VAD model and is driven independently of any processor.\n\n```javascript\nconst { Model, Vad, VadParameter } = require('@ai-coustics/aic-sdk')\n\nconst vadModel = Model.fromFile(await Model.download('vad-2.1-xxs-16khz', './models'))\nconst vad = new Vad(vadModel, process.env.AIC_SDK_LICENSE)\n\nconst sampleRate = vadModel.getOptimalSampleRate()\nvad.initialize(sampleRate, vadModel.getOptimalBlockSize(sampleRate))\n\nconst context = vad.getContext()\ncontext.setParameter(VadParameter.Sensitivity, 0.8)\n\nvad.process(block) // reads the block, does not modify it\n\nif (context.isSpeechDetected()) {\n  console.log('speech')\n}\n\n// Raw model output, before speech-hold and thresholding\nconsole.log(context.getRawVadProbability())\n```\n\n### Run the VAD on the original audio\n\nWhen enhancement and detection run together, feed the VAD the **original** input, not the\nprocessor's output. Enhancement is designed to change the signal, so detecting on its output\nmeans running the VAD on audio that no longer matches what its model expects, and it stacks\nthe processor's delay on top of the prediction delay. Because `vad.process` leaves its input\nuntouched, run it before enhancement. Configure both instances with the same sample rate and\nblock size:\n\n```javascript\nvad.process(block) // reads the block\nprocessor.process(block) // enhances it in place\n```\n\nThe two delays describe different things and are independent:\n\n```javascript\ncontext.getAudioDelay() // enhanced audio lags the input by this many samples\nvadContext.getPredictionDelay() // the VAD decision lags the same input by this many\n```\n\nThe prediction delay is not applied to the audio. Use it to align speech decisions with\nthe input audio.\n\n## Analysis\n\nAnalysis models score audio quality. Collect audio with `buffer`, then run the model with\n`analyzeAsync` or `analyze`.\n\n```javascript\nconst { Model, Analyzer } = require('@ai-coustics/aic-sdk')\n\nconst analysisModel = Model.fromFile(await Model.download('tyto-1.1-l-16khz', './models'))\nconst analyzer = new Analyzer(analysisModel, process.env.AIC_SDK_LICENSE)\n\nconst sampleRate = analysisModel.getOptimalSampleRate()\nanalyzer.initialize(sampleRate, analysisModel.getOptimalBlockSize(sampleRate))\n\nanalyzer.buffer(block)\n\n// Runs the model on a worker thread. `analyze()` does the same on the calling thread.\nconst result = await analyzer.analyzeAsync()\nconsole.log(result.riskScore, result.noise, result.speakerReverb)\n```\n\n`buffer` is synchronous and does not acquire the analyzer lock. Audio collection can\ncontinue while `analyzeAsync` runs on a worker thread. Use `analyze` when blocking the\ncalling thread is acceptable, such as in a CLI or dedicated worker.\n\nAnalysis uses a fixed duration of audio determined by the model. Older samples are discarded\nas new audio arrives. If insufficient audio has been collected, analysis pads the remaining\ninput with silence.\n\nCalls to `analyze`, `reset`, `updateBearerToken`, `terminateSession` and `dispose` acquire the\nanalyzer lock and may block while async analysis is running. `initialize` only configures\nthe collector.\n\nAll scores range from 0.0 to 1.0. For every field except `speakerLoudness`, lower values\nindicate less problematic audio. `riskScore` predicts the likelihood of failure in downstream\nmodels such as speech-to-text, VAD or turn-taking.\n\n## Telemetry\n\nTelemetry follows the runtime environment (e.g. `AIC_SDK_OTEL_ENABLE`). To override it for a\nsingle instance:\n\n```javascript\nconst processor = new Processor(model, licenseKey, {\n  enable: true,\n  sessionId: 'my-session',\n  exportIntervalMs: 60000,\n})\n```\n\nA telemetry session ends when its native object is destroyed. Use `terminateSession()` to\nrequest termination at a specific lifecycle event. Once termination is handled, processors\nand VADs can no longer process audio, and analyzers can no longer analyze buffered audio.\nOn `ProcessorAsync` and `VadAsync`, termination runs on a libuv worker thread and returns\na promise because it may block.\n\nIf your license key is a JWT, refresh it in place instead of rebuilding the object:\n\n```javascript\ncontext.updateBearerToken(renewedJwt)\n```\n\n## Memory management\n\n`Model`, `Processor`, `ProcessorAsync`, `Vad`, `VadAsync` and `Analyzer` hold large native\nallocations behind small JavaScript objects. The binding reports estimated native memory\nusage to V8 so the garbage collector can account for these allocations. This influences\ncollection frequency but does not guarantee when an object will be released.\n\nUse `dispose()` to release native resources at a specific point instead of waiting for\ngarbage collection:\n\n```javascript\nconst processor = new Processor(model, licenseKey)\ntry {\n  processor.initialize(sampleRate, blockSize)\n  processor.process(block)\n} finally {\n  processor.dispose()\n}\n```\n\nAfter `dispose()`, all methods except `dispose()` fail; repeated disposal has no effect.\nAsync methods reject their promises. Disposal blocks the calling thread if a worker holds\nthe native object's lock. Queued work that acquires the lock after disposal fails.\n\nDisposing a model releases its reference to the model data. Processors, VADs and analyzers\ncreated from it retain their own references and remain usable.\n\nCleanup timing also affects native memory usage:\n\n- Native cleanup runs on the event loop when the object is finalized, not synchronously at\n  garbage collection. Finalizers run on event-loop turns, so batches that create many of\n  these objects back to back hold native memory until the loop turns. An `await` on an\n  already-resolved promise (a microtask) is not enough; real async boundaries such as I/O,\n  `setTimeout` or `setImmediate` are. A tight synchronous loop that creates thousands of\n  objects accumulates their native memory for the duration of the loop; create these\n  objects per unit of work behind real async boundaries, or reuse a single instance.\n- RSS may remain elevated after disposal because the allocator can retain freed memory\n  for reuse. Peak memory use depends on the number of concurrently live instances,\n  including objects awaiting finalization.\n\n## Development\n\nBuilding, testing, benchmarking and releasing this package are covered in\n[DEVELOPMENT.md](DEVELOPMENT.md).\n\n## License\n\nThis Node wrapper is distributed under the Apache 2.0 license (`LICENSE`). The core SDK it\nlinks against is distributed under the proprietary AIC-SDK license\n(`LICENSE.AIC_SDK`).\n","readmeFilename":"README.md"}