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another Node binding of whisper.cpp to make same API with whisper.rn as much as possible.","maintainers":[{"name":"pepper.yen","email":"pepper.yen@gmail.com"},{"name":"jhen0409","email":"iainst0409@gmail.com"},{"name":"hans00","email":"jy29825377@gmail.com"},{"name":"bricks_deployment","email":"jhen@bricks.tools"},{"name":"shanecsw","email":"gwaieiae@gmail.com"}],"readme":"# whisper.node\n\n[![CI](https://github.com/mybigday/whisper.node/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/mybigday/whisper.node/actions/workflows/ci.yml)\n[![NPM Version](https://img.shields.io/npm/v/%40fugood%2Fwhisper.node)](https://www.npmjs.com/package/@fugood/whisper.node)\n![NPM Downloads](https://img.shields.io/npm/dw/%40fugood%2Fwhisper.node)\n\nAn another Node binding of [whisper.cpp](https://github.com/ggml-org/whisper.cpp) to make same API with [whisper.rn](https://github.com/mybigday/whisper.rn) as much as possible.\n\n- [whisper.cpp](https://github.com/ggerganov/whisper.cpp): Automatic speech recognition with multi-platform support\n- [whisper.rn](https://github.com/mybigday/whisper.rn): React Native binding of whisper.cpp\n\n## Platform Support\n\n- macOS\n - arm64: CPU and Metal GPU acceleration\n - x86_64: CPU only\n- Windows (x86_64 and arm64)\n - CPU\n - GPU acceleration via Vulkan\n - GPU acceleration via CUDA (x86_64)\n- Linux (x86_64 and arm64)\n - CPU\n - GPU acceleration via Vulkan\n - GPU acceleration via CUDA\n- Web\n - WASM\n - Optional WebGPU through `ggml-webgpu` when the WASM package is built with `GGML_WEBGPU=ON`\n\n## Installation\n\n```sh\nnpm install @fugood/whisper.node\n```\n\n## Usage\n\n### Basic Transcription\n\n```js\nimport { initWhisper } from '@fugood/whisper.node'\n\nconst context = await initWhisper({\n model: 'path/to/ggml-base.en.bin',\n useGpu: true,\n}, libVariant)\n\n// transcribeFile returns { stop, promise }\nconst { stop: stop1, promise: promise1 } = context.transcribeFile('audio1.wav', {\n language: 'en',\n temperature: 0.0,\n // ...\n})\n\nconst result1 = await promise1\n\n// transcribeData also returns { stop, promise }\nlet audioBuffer // PCM 16-bit, mono, 16kHz\nconst { stop: stop2, promise: promise2 } = context.transcribeData(audioBuffer, {\n language: 'en',\n temperature: 0.0,\n // ...\n})\n\nconst result2 = await promise2\n\n// You can also cancel transcription if needed\n// await stop1() // Cancels the first transcription\n// await stop2() // Cancels the second transcription\n//\n// stop() interrupts the decode; the job's promise then resolves with\n// `isAborted: true` and the segments decoded so far (it does not reject).\n// Native failures reject the promise. The context stays usable afterwards.\n\n// Always release the context when done\nawait context.release()\n```\n\n### NVIDIA Parakeet TDT\n\n`ParakeetContext` runs NVIDIA's Parakeet TDT 0.6B v3 model through the Parakeet API included in whisper.cpp. The v3 model supports English plus 24 other European languages.\n\nDownload a GGUF model from [ggml-org/parakeet-GGUF](https://huggingface.co/ggml-org/parakeet-GGUF) before initializing the context:\n\n| Model | Approximate size |\n| --- | ---: |\n| `ggml-parakeet-tdt-0.6b-v3-q4_0.bin` | 356 MB |\n| `ggml-parakeet-tdt-0.6b-v3-q4_k.bin` | 416 MB |\n| `ggml-parakeet-tdt-0.6b-v3-q8_0.bin` | 669 MB |\n| `ggml-parakeet-tdt-0.6b-v3-f16.bin` | 1.26 GB |\n\n```js\nimport { initParakeet } from '@fugood/whisper.node'\n\nconst parakeetContext = await initParakeet({\n filePath: 'path/to/ggml-parakeet-tdt-0.6b-v3-q4_0.bin',\n useGpu: true,\n}, libVariant)\n\n// transcribeFile / transcribeData return { stop, promise } like WhisperContext\nconst { stop, promise } = parakeetContext.transcribeFile('audio.wav', {\n maxThreads: 4,\n})\nconst { result, segments, isAborted } = await promise\n\n// Cancel an in-flight transcription when needed:\n// await stop()\n\nawait parakeetContext.release()\n```\n\n`transcribeData()` accepts raw signed 16-bit PCM as ArrayBuffer; raw audio must be mono at 16 kHz. Whisper-specific options (language, prompt, callbacks) are not supported, use `maxThreads` and `audioCtx` instead.\n\n### Voice Activity Detection (VAD)\n\n```js\nimport { initWhisperVad } from '@fugood/whisper.node'\n\n// Context-based VAD (for multiple detections)\nconst vadContext = await initWhisperVad({\n model: 'path/to/ggml-vad.bin',\n useGpu: true,\n nThreads: 2\n}, libVariant)\n\nconst result = await vadContext.detectSpeechFile('audio.wav')\n\nconst result2 = await vadContext.detectSpeechData(audioBuffer)\nawait vadContext.release()\n```\n\n**Note**: Audio data should be 16-bit PCM, mono, 16kHz format. The library expects ArrayBuffer containing raw audio data.\n\n### Native Logs\n\n```js\nimport {\n addNativeLogListener,\n isNativeLogEnabled,\n toggleNativeLog,\n} from '@fugood/whisper.node'\n\nconst logs = addNativeLogListener((level, text) => {\n console.log(`[whisper ${level}] ${text}`)\n})\n\nawait toggleNativeLog(true)\nconsole.log(isNativeLogEnabled())\n\n// ...\n\nawait toggleNativeLog(false)\nlogs.remove()\n```\n\nLog levels are emitted as lowercase `error`, `warn`, `info`, or `debug`\nstrings. The same helpers are available in Node.js and browser WASM builds.\n\n### Browser WASM\n\nThe browser package keeps the same promise-based `initWhisper`,\n`initWhisperVad`, and `initParakeet` entry points. In browsers, `filePath` is\ntreated as a URL and the model is fetched into the WASM filesystem.\n\n```js\nimport { initWhisper, initWhisperVad } from '@fugood/whisper.node'\n\nconst whisper = await initWhisper({\n filePath: 'https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.en.bin',\n maxModelBytes: 1536 * 1024 * 1024,\n useGpu: false,\n})\n\nconst { promise } = whisper.transcribeFile('https://raw.githubusercontent.com/ggml-org/whisper.cpp/master/samples/jfk.wav', {\n language: 'en',\n temperature: 0,\n})\n\nconsole.log(await promise)\nawait whisper.release()\n\nconst vad = await initWhisperVad({\n filePath: 'https://huggingface.co/ggml-org/whisper-vad/resolve/main/ggml-silero-v6.2.0.bin',\n useGpu: false,\n})\nconsole.log(await vad.detectSpeechFile('https://raw.githubusercontent.com/ggml-org/whisper.cpp/master/samples/jfk.wav'))\nawait vad.release()\n```\n\nThe browser package ships both single-thread and pthread WASM artifacts. On\ncross-origin isolated pages (`Cross-Origin-Opener-Policy: same-origin` and\n`Cross-Origin-Embedder-Policy: require-corp`), the loader uses the pthread\nartifact with `SharedArrayBuffer`; otherwise it falls back to the single-thread\nartifact automatically. Oversized model downloads fail before loading into\nMEMFS. Firefox is capped at 256 MiB by default; other browsers default to 75% of\nthe configured WASM maximum memory. Pass `maxModelBytes` only when you know the\ntarget browser can allocate the model. Whisper transcription defaults to up to 8\nthreads based on browser hardware concurrency when pthreads are available; pass\n`maxThreads` to override it. Browser WASM clamps `maxThreads` to the compiled\npool limit of 8, or 1 in the single-thread fallback. Browser pages run model\nloading, transcription, benchmarks, and VAD in a dedicated module worker by\ndefault so the UI thread can keep rendering. Use the main `whisper.node` package\nentrypoint in browser code too:\n\n```js\nimport { configureWasm, initWhisper } from '@fugood/whisper.node'\n```\n\nUse `configureWasm({ worker: false })` only when you explicitly need the\nin-thread runtime, `configureWasm({ threads: false })` to force the\nsingle-thread artifact, or pass `workerPath`, `jsPath`, and `wasmPath` when\nserving the package files from custom URLs. The older `workerUrl` and\n`runtimeScriptUrl` option names still work. Model\ndownloads are cached in browser Cache Storage by default. Pass\n`cacheModel: false` to disable persistent caching, `modelCacheName` to isolate\nthe cache namespace, or `modelCacheKey` when the fetch URL is a proxy or signed\nURL but should reuse the same cached model.\n\nBuild the browser package with:\n\n```sh\nnpm run build-wasm\n```\n\nOr build with the Emscripten Docker image:\n\n```sh\nnpm run build-wasm-docker\n```\n\n`npm run build-wasm` enables `GGML_WEBGPU=ON` by default and emits\n`wasm/whisper-node.js`, `wasm/whisper-node.wasm`,\n`wasm/whisper-node.threads.js`, and `wasm/whisper-node.threads.wasm`. Use\n`bash scripts/build-wasm.sh --no-webgpu` for a CPU-only WASM build, or\n`--no-threads` / `--threads` to build only one CPU threading variant. Pass\n`--single-file` only when you want the WASM binary embedded into each generated\nJS file. Modern Emscripten embeds the pthread worker bootstrap in the main JS\nfile, so a separate `whisper-node.worker.js` is not expected. The browser\npackage also ships its own module `worker.js` wrapper for non-blocking model load\nand inference. `npm run build-wasm-docker` uses `emscripten/emsdk:4.0.14-arm64`\non arm64 hosts such as Apple Silicon Macs, and `emscripten/emsdk:4.0.13` on\namd64 hosts. Override with `EMSCRIPTEN_IMAGE` or `EMSCRIPTEN_PLATFORM` when\nneeded. A local smoke page is available after building:\n\n```sh\nnode examples/wasm/server.mjs\n```\n\nIn the WASM package, `useGpu: true` enables WebGPU for whisper transcription\nwhen the browser supports `navigator.gpu`. VAD currently falls back to CPU in\nthe browser package because the Silero VAD graph hits unsupported WebGPU ops.\n\n## Lib Variants\n\n- [x] `default`: General usage, not support GPU except macOS (Metal)\n- [x] `vulkan`: Support GPU Vulkan (Windows/Linux), but some scenario might unstable\n- [x] `cuda`: Support GPU CUDA (Windows/Linux), but only for limited capability\n > Linux: (x86_64: 8.9, arm64: 8.7)\n > Windows: x86_64 - 12.0\n- [x] `snapdragon`: Qualcomm Snapdragon Hexagon NPU via ggml-hexagon (Linux arm64). The HTP skels ship in the platform package and `ADSP_LIBRARY_PATH` is set automatically; `GGML_HEXAGON_NDEV` defaults to 16\n\n## License\n\nMIT\n\n---\n\n

\n \n \n \n

\n Built and maintained by BRICKS.\n

\n

\n","readmeFilename":"README.md"}