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speech-to-text inference addon using NVIDIA Parakeet models for Bare runtime","maintainers":[{"name":"mafintosh","email":"mathiasbuus@gmail.com"},{"name":"prdn","email":"paolo.ardoino@gmail.com"}],"readme":"# transcription-parakeet\n\nThis library simplifies running NVIDIA Parakeet speech-to-text and Sortformer speaker-diarization inference within QVAC runtime applications. It provides an easy interface to load, execute, and manage Parakeet inference instances, supporting CTC, TDT, EOU, and Sortformer checkpoints from a single binding.\n\n## Table of Contents\n\n- [Supported Platforms](#supported-platforms)\n- [Installation](#installation)\n- [Development](#development)\n- [Usage](#usage)\n  - [1. Stage a Model](#1-stage-a-model)\n  - [2. Configure the Model](#2-configure-the-model)\n  - [3. Create Model Instance](#3-create-model-instance)\n  - [4. Load the Model](#4-load-the-model)\n  - [5. Run Inference](#5-run-inference)\n  - [6. Release Resources](#6-release-resources)\n- [Quickstart example](#quickstart-example)\n- [Model Variants](#model-variants)\n- [Other examples](#other-examples)\n- [Glossary](#glossary)\n- [Error Range](#error-range)\n- [Resources](#resources)\n- [License](#license)\n\n## Supported Platforms\n\n| Platform | Architecture | Min Version | Status | GPU Support |\n|----------|-------------|-------------|--------|-------------|\n| macOS | arm64, x64 | 14.0+ | Tier 1 | Metal |\n| iOS | arm64 | 17.0+ | Tier 1 | Metal |\n| Linux | arm64, x64 | Ubuntu-22+ | Tier 1 | Vulkan |\n| Android | arm64 | 12+ | Tier 1 | Vulkan / OpenCL |\n| Windows | x64 | 10+ | Tier 1 | Vulkan |\n\n**Dependencies:**\n- qvac-lib-inference-addon-cpp (latest): C++ addon framework\n- parakeet-cpp (latest): NVIDIA Parakeet ASR + Sortformer diarization engine\n- ggml-speech (latest): GGML flavour shared with the speech stack; library prefix `qvac-speech-` so it can coexist with the fabric/llm and diffusion ggml builds on the same Android device\n- Bare Runtime (latest): JavaScript runtime\n- Linux requires Clang/LLVM 22 with libc++\n\n## Installation\n\n### Prerequisites\n\nMake sure [Bare](#glossary) Runtime is installed:\n```bash\nnpm install -g bare bare-make\n```\n\n### Installing the Package\n\nInstall the latest version:\n```bash\nnpm install @qvac/transcription-parakeet@latest\n```\n\n## Development\n\n### Building the AddOn Locally\n\nFor local development, you'll need to build the native addon that interfaces with the Parakeet engine. Follow these steps:\n\n#### Prerequisites\n\nFirst, make sure you have the prerequisites from the [Installation](#installation) section.\n\n#### System Requirements\n\n**Supported Platforms:**\n- **Linux** (x64, ARM64) -- requires Clang/LLVM 22 with libc++\n- **macOS** (x64, ARM64)\n- **Windows** (x64)\n\n#### Required Tools\n\n**All Platforms:**\n- **CMake** (>= 3.25)\n- **Git**\n- **C++ Compiler** with C++20 support\n  - Linux: Clang 22+ with libc++\n  - macOS: Xcode 12+ (provides Clang 12+)\n  - Windows: Visual Studio 2019+ or MinGW-w64\n\n#### vcpkg Setup\n\nThis project uses [vcpkg](https://vcpkg.io/) for C++ dependency management. The `cmake-vcpkg` package pulls vcpkg in transparently during `npm install`, so most users don't need to set it up by hand. If you want a system-wide vcpkg checkout:\n\n```bash\ngit clone https://github.com/Microsoft/vcpkg.git\ncd vcpkg\n./bootstrap-vcpkg.sh           # or .\\bootstrap-vcpkg.bat on Windows\nexport VCPKG_ROOT=$(pwd)\n```\n\n#### Platform-Specific Setup\n\n**Linux:**\n```bash\n# Ubuntu/Debian -- includes Clang 22 and libc++ required by the native addon\nsudo apt update\nsudo apt install clang libc++-dev libc++abi-dev build-essential cmake git pkg-config\n```\n\n**macOS:**\n```bash\nxcode-select --install\nbrew install cmake git\n```\n\n**Windows:**\n- Install [Visual Studio 2019+](https://visualstudio.microsoft.com/) with C++ development tools\n- Install [CMake](https://cmake.org/download/) (3.25+)\n- Install [Git for Windows](https://git-scm.com/download/win)\n\n#### GPU Acceleration (Optional)\n\nGPU backends are selected at vcpkg install time via the `parakeet-cpp[metal|vulkan|opencl]` features, which forward to the matching `ggml-speech[...]` features. The `ggml-speech` port is a separate dependency from `parakeet-cpp` (it's the speech-stack flavour of ggml, with the `qvac-speech-` library prefix so it can coexist with the fabric/llm and diffusion ggml flavours on the same Android device); runtime falls back to CPU if the chosen backend doesn't initialise.\n\n- **Metal (macOS/iOS):** automatic; no setup required.\n- **Vulkan (Linux/Windows/Android):** install the [Vulkan SDK](https://vulkan.lunarg.com/sdk/home) and ensure GPU drivers support Vulkan 1.1+.\n  ```bash\n  # Ubuntu/Debian\n  sudo apt install vulkan-tools libvulkan-dev vulkan-utility-libraries-dev spirv-tools\n  ```\n\n#### Clone and Setup\n\n```bash\ngit clone https://github.com/tetherto/qvac.git\ncd qvac/packages/transcription-parakeet\nnpm install\n```\n\n#### Build the Native AddOn\n\n```bash\nnpm run build\n```\n\nThis runs:\n1. `bare-make generate` -- generates build configuration\n2. `bare-make build` -- compiles the native C++ addon\n3. `bare-make install` -- installs the prebuild\n\n#### Running Tests\n\n```bash\nnpm run test:unit                                    # JS unit tests (mocked)\nQVAC_TEST_GGUF_DIR=models npm run test:integration   # JS integration vs. real GGUFs\nnpm run test:cpp                                     # gtest C++ suite\n```\n\nThe integration suite locates each model type via `QVAC_TEST_GGUF_DIR=<path-with-staged-ggufs>` (or per-model overrides like `QVAC_TEST_GGUF_TDT=/full/path.gguf`). Tests skip cleanly when no GGUF is available, so CI without local models still passes.\n\n## Usage\n\nThe library wraps `qvac-parakeet.cpp`'s engine in the QVAC addon framework so you can transcribe audio files, run speaker diarization, or stream live mic input through the same shape: load a single `.gguf`, push audio chunks, drain segment callbacks.\n\n> **Heads up:** the package is intended to be used through the default export, `index.js`'s `TranscriptionParakeet` class. A lower-level `ParakeetInterface` is available only via the dedicated `@qvac/transcription-parakeet/parakeet.js` subpath (`require('@qvac/transcription-parakeet/parakeet.js')`) as an escape hatch for power users that need to drive the addon's job runner directly, but new code should default to `TranscriptionParakeet` -- it's what the bundled examples and integration tests use.\n\n### 1. Stage a Model\n\nThe ggml backend takes a single `.gguf` per checkpoint. The standard flow is \"provision a Python venv, download `.nemo` from HuggingFace, convert to `.gguf` via the in-tree converter\":\n\n```bash\nnpm run setup-models                       # venv + download + convert, all 5 models, q8_0\nnpm run setup-models -- -t tdt             # just TDT\nnpm run setup-models -- -t eou -q f16      # full-precision EOU\n```\n\n`setup-models` chains `setup-venv` -> `download-models` -> `convert-models`. The venv step is idempotent (skipped if `./venv` already has the required interpreter), so re-running `setup-models` after a successful first run only re-checks the downloads and conversions.\n\nOutput GGUFs land in `./models/`. The conversion is driven by `scripts/convert-nemo-to-gguf.py` (vendored from `qvac-parakeet.cpp`; resync on bump) and runs against the local `./venv`. The venv needs `gguf`, `numpy`, `torch`, `pyyaml`, and `sentencepiece` -- the converter reads the `.nemo` archive directly via `tarfile` + `torch.load` and does **not** depend on the heavy `nemo_toolkit` package despite the file extension. `sentencepiece` is required to decode the model's `tokenizer.model` proto into the GGUF's token / score / type arrays (without it, transcription output ends up as raw token IDs). Full requirement list lives at `scripts/requirements.txt`. To use a pre-existing interpreter instead of `./venv`, pass `--python /path/to/python` to either script (or set `PYTHON=...`).\n\nThe three underlying scripts are also flag-driven if you want to run them separately:\n\n```\nsetup-venv.sh      [--python <bin>] [--venv <path>] [--force] [--help]\ndownload-models.sh [--type ctc|tdt|eou|sortformer|sortformer-streaming-v2.1|all]\n                   [--output <dir>] [--force] [--help]\nconvert-nemo.sh    [--type ctc|tdt|eou|sortformer|sortformer-streaming-v2.1|all]\n                   [--quant f16|q8_0|q5_0|q4_0|f32]\n                   [--python <bin>]\n                   [--nemo-dir <dir>] [--output <dir>] [--force] [--help]\n```\n\n#### Source repositories\n\n| Model (`--type`) | HuggingFace `.nemo` |\n|-------|-----------------------------------|\n| `ctc` | [`nvidia/parakeet-ctc-0.6b`](https://huggingface.co/nvidia/parakeet-ctc-0.6b) |\n| `tdt` | [`nvidia/parakeet-tdt-0.6b-v3`](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3) |\n| `eou` | [`nvidia/parakeet_realtime_eou_120m-v1`](https://huggingface.co/nvidia/parakeet_realtime_eou_120m-v1) |\n| `sortformer` | [`nvidia/diar_sortformer_4spk-v1`](https://huggingface.co/nvidia/diar_sortformer_4spk-v1) |\n| `sortformer-streaming-v2.1` | [`nvidia/diar_streaming_sortformer_4spk-v2.1`](https://huggingface.co/nvidia/diar_streaming_sortformer_4spk-v2.1) |\n\nNVIDIA Open Model License -- see each repo's model card for terms.\n\n### 2. Configure the Model\n\nMost users interact with the package through `index.js`. From that entrypoint we surface a small, safe subset of options; the rest keep `parakeet-cpp` defaults.\n\n#### What `index.js` accepts\n\n| Section | Key | Description |\n| --- | --- | --- |\n| `files` | `model` | Absolute or relative path to the `.gguf` checkpoint |\n| `config.parakeetConfig` | `maxThreads` | CPU threads (default: `4`); `0` defers to the engine's `hardware_concurrency` |\n| | `useGPU` | Enable the linked ggml GPU backend (default: `false`) |\n| | `streaming` | Open a long-lived `StreamSession` / `SortformerStreamSession` so speaker IDs stay stable across appends and EOU `<EOU>` boundaries surface as segments. Cross-append state is preserved only within a single `run()` call -- separate `run()` invocations on the same instance start a fresh session. For continuous live capture, drive a single long-running `run()` from a pushable stream, or use the duplex `runStreaming()` API which owns one streaming session for the lifetime of the call. Default: `false` (offline `transcribe_samples` / `diarize_samples`). |\n| | `streamingChunkMs` | Streaming chunk cadence in ms (default: 2000) |\n| | `streamingHistoryMs` | Sortformer rolling-history window in ms (default: 30000) |\n| | `streamingEmitPartials` | Emit partials before chunk boundaries (default: `true`) |\n| | `streamingEnergyVad` | CTC/TDT energy-VAD events (default: `false`) |\n| | `streamingLeftContextMs` | ASR encoder left-context window in ms; `-1` keeps parakeet-cpp's default of 10000. ASR sessions only (Sortformer ignores it). |\n| | `streamingRightLookaheadMs` | ASR encoder right-lookahead window in ms; `-1` keeps parakeet-cpp's default of 2000. Adds directly to the per-segment latency floor (`chunk_ms + right_lookahead_ms`). ASR sessions only. |\n| | `streamingSpkCacheEnable` | AOSC: enable v2.1 Sortformer's speaker-cache streaming (default: `true`). Ignored on v1/v2 Sortformer GGUFs and on non-Sortformer models. Set `false` to force a v2.1 GGUF onto the v1 sliding-window path (A/B comparison). |\n| | `streamingSpkCacheLen` | AOSC: long-term speaker-cache rows (~15 s of encoder frames). Default: 188. |\n| | `streamingFifoLen` | AOSC: FIFO warmup buffer rows. Default: 188. |\n| | `streamingChunkLeftContextMs` | AOSC: encoder left-context window (ms; ~1 encoder frame). Default: 80. |\n| | `streamingChunkRightContextMs` | AOSC: encoder right-context window (ms; ~7 encoder frames). Default: 560. |\n| | `streamingSpkCacheUpdatePeriod` | AOSC: FIFO-overflow pop-out count. Default: 144. |\n| | `backendsDir` | Root directory for dynamically-loaded ggml backend `.so` files (Vulkan, OpenCL, per-arch CPU variants on Android). Defaults to the package's `prebuilds/` folder; the native addon appends `<bare-target>/<module-name>` before scanning. Pass an explicit path when prebuilds live elsewhere — e.g. Android `ApplicationInfo.nativeLibraryDir` when backend libs ship inside the APK. No-op on Apple (statically linked). |\n| | `openclCacheDir` | Persistent directory for ggml-opencl's compiled program-binary cache (`$GGML_OPENCL_CACHE_DIR`). Android-only; pass the host app's cache directory (e.g. `Context.getCacheDir()`) to skip cold `clBuildProgram` on every process start. Ignored on other platforms. |\n\nThe model type (CTC / TDT / EOU / Sortformer) is **auto-detected from the GGUF metadata**, so callers don't need to pass `modelType`. Other knobs (`captionEnabled`, `timestampsEnabled`, `seed`, `sampleRate`, `channels`) keep sensible defaults.\n\n**Sortformer Streaming Diarization (v2.1 + AOSC).** parakeet-cpp ships\ntwo streaming-diarization paths picked automatically by the GGUF:\n\n- **v1** uses a fixed-size sliding-history window inside the engine.\n  Once two voices have been seen, the per-chunk decisions are\n  permutation-invariant; if a speaker goes silent long enough to roll\n  out of the window, the slot can drift onto a different physical voice\n  when they return. Fine for short, stable clips; ships as\n  `sortformer-4spk-v1.q8_0.gguf`.\n- **v2.1** replaces the sliding window with AOSC (Audio-Online Speaker\n  Cache, ported from NVIDIA NeMo) which anchors each slot to its\n  accumulated embedding. Same physical speaker comes back to the same\n  `Speaker N` tag across silences. Default for live capture; ships as\n  `diar_streaming_sortformer_4spk-v2.1.q8_0.gguf`. The engine detects\n  v2.1 via the GGUF metadata tag\n  `parakeet.model_variant == \"sortformer-streaming-v2.1-aosc\"`; you\n  don't need to opt in via config.\n\nThe defaults in the `streamingSpkCache*` / `streamingFifo*` /\n`streamingChunk{Left,Right}ContextMs` table rows above are the NeMo-port\ntuning parakeet-cpp ships -- you almost always want to keep them. The\nknobs are exposed for A/B comparison (e.g. `--spk-cache-enable false`\nin `examples/live-mic-diarized-aosc.js` to force a v2.1 GGUF onto the\nv1 path) and for tuning unusual audio (longer cache, larger\nright-context window for higher latency tolerance, etc.).\n\nFor offline diarization (single batch over a finite clip) v1 remains\nthe recommended GGUF -- AOSC's slot-stability benefit only applies to\ncontinuous streaming and offers no measurable improvement when the\nentire clip is available at once.\n\n#### Configuration Example\n\n```javascript\nconst config = {\n  parakeetConfig: {\n    useGPU:    true,\n    streaming: false   // flip to true for live-mic / speaker-stable streaming\n  }\n}\n```\n\n### 3. Create Model Instance\n\n```javascript\nconst TranscriptionParakeet = require('@qvac/transcription-parakeet')\n\nconst model = new TranscriptionParakeet({\n  files: { model: './models/parakeet-tdt-0.6b-v3.q8_0.gguf' },\n  config: {\n    parakeetConfig: { useGPU: true }\n  }\n})\n```\n\n### 4. Load the Model\n\n```javascript\ntry {\n  await model.load()\n} catch (error) {\n  console.error('Failed to load model:', error)\n}\n```\n\n`load()` opens the `.gguf`, instantiates `qvac_parakeet::Engine`, and (if `streaming: true`) opens the relevant streaming session.\n\n### 5. Run Inference\n\nPass an audio stream (e.g. from `bare-fs.createReadStream` or a live PCM buffer) to either `run()` (offline / batched) or `runStreaming()` (duplex / live). Audio must be **16 kHz mono**, either Float32 or signed 16-bit little-endian PCM.\n\n> **Buffer cap (`run()` only):** the JS layer batches every chunk for a single `run()` call into one native `process()` invocation. Incoming audio is normalized to **Float32** (4 bytes/sample) before it is buffered, and the buffer is capped at **500 MiB** (`MAX_BUFFERED_BYTES` in `parakeet.js`); exceeding it raises `BUFFER_LIMIT_EXCEEDED`. At 16 kHz mono that's roughly 2.7 hours of continuous audio (regardless of whether the input arrived as int16 or Float32). For longer single-session captures, use `runStreaming()` (no per-call buffer cap -- audio is fed straight to the engine as it arrives) or split into sequential `run()` calls.\n\nThere are three ways to receive transcription results:\n\n#### Option 1: Real-time streaming with `onUpdate()`\n\n```javascript\ntry {\n  const audioStream = fs.createReadStream('path/to/audio.raw', {\n    highWaterMark: 16000\n  })\n  const response = await model.run(audioStream)\n\n  await response\n    .onUpdate(segments => {\n      // `segments` is `TranscriptionSegment[]`:\n      //   { text, start, end, toAppend, id?, isEndOfTurn?, startsWord? }\n      // - `isEndOfTurn` is true on EOU streaming chunks where the\n      //   model fired the `<EOU>` token; CTC / TDT / Sortformer\n      //   always leave it false.\n      // - `startsWord` is true when the segment begins a new\n      //   SentencePiece word (`▁`-marker token); concat verbatim\n      //   when false to rejoin chunk-boundary wordpiece splits like\n      //   [\"pun\", \"ctuation\"] -> \"punctuation\".\n      for (const seg of segments) console.log(seg.text)\n    })\n    .await()\n} catch (error) {\n  console.error('Transcription failed:', error)\n}\n```\n\n`run()` buffers the entire audio stream in JS memory and dispatches one\njob at end-of-stream, so segments only surface after the whole input is\nconsumed. For latencies bound by `chunk_ms + right_lookahead_ms` rather\nthan by total audio length, use `runStreaming()` (Option 3 below).\n\n#### Option 2: Complete result with `iterate()`\n\n```javascript\nconst response = await model.run(audioStream)\nfor await (const chunk of response.iterate()) {\n  console.log('Transcription chunk:', chunk)\n}\n```\n\n#### Option 3: Duplex streaming with `runStreaming()`\n\nFor live-mic and other low-latency use cases, `runStreaming()` opens a\nlong-lived `parakeet::StreamSession` (or `SortformerStreamSession`) on\nthe C++ side and feeds each pushed chunk straight in -- bypassing the\n`run()` path's batch-then-process lifecycle. Per-chunk segments surface\nthrough the regular `onUpdate(...)` channel as soon as the engine\nemits them. The session stays open across chunks, so the rolling\nencoder context, EOU detector, and Sortformer speaker history are all\npreserved (no chunk-boundary state resets).\n\n```javascript\n// Construct with `streaming: true` so the addon configures the\n// duplex-friendly defaults at load time:\nconst model = new TranscriptionParakeet({\n  files: { model: './models/parakeet-tdt-0.6b-v3.q8_0.gguf' },\n  config: {\n    parakeetConfig: {\n      streaming: true,\n      streamingChunkMs: 2000,\n      useGPU: true\n    }\n  }\n})\nawait model.load()\n\n// Provide an async-iterable of Buffer / Float32Array chunks. The\n// example uses a small `pushableStream()` helper from\n// `examples/utils.js` that lets you `.push(chunk)` from any sync\n// callback (e.g. `child_process.stdout.on('data', ...)`) and `.end()`\n// when capture is done.\nconst audio = pushableStream()\ncaptureProcess.stdout.on('data', chunk => audio.push(chunk))\ncaptureProcess.on('exit', () => audio.end())\n\nconst response = await model.runStreaming(audio, {\n  // optional per-call overrides; omitted fields fall back to the\n  // matching `parakeetConfig.streaming*` value used at load time\n  chunkMs: 2000\n})\n\nawait response\n  .onUpdate(segments => {\n    for (const seg of segments) {\n      if (seg.isEndOfTurn) console.log('--- end of turn ---')\n      else console.log(seg.text)\n    }\n  })\n  .await()\n```\n\nThe new lower-level entry points (`startStreaming` / `appendStreamingAudio` / `endStreaming` / `cancelStreaming`) are exposed on the `ParakeetInterface` (`parakeet.js`) for callers that want to drive the session manually; `runStreaming` is the high-level wrapper that takes an async-iterable, opens the session, pumps chunks, and synthesises a `JobEnded` when the iterable completes.\n\n**Key differences:**\n- `onUpdate()` on `run()` -- one batch of segments after the entire input has been buffered.\n- `iterate()` on `run()` -- collects all segments after the job finishes.\n- `onUpdate()` on `runStreaming()` -- segments arrive as the engine produces them, with stable session state across chunks. Default for live-mic.\n\nFor Sortformer GGUFs, the `Output` event carries `Speaker N: HH:MM:SS - HH:MM:SS` text per segment instead of an ASR transcript -- see `examples/diarized-transcribe.js` for offline parsing and `examples/live-mic-diarized.js` for the streaming flow.\n\n#### Backend info and runtime stats\n\nAfter `load()`, `getBackendInfo()` reports the compute backend the native engine actually resolved (post-fallback):\n\n```javascript\nconst info = model.getBackendInfo()\n// { backendDevice: 'GPU', backendId: 3, backendName: 'Vulkan0',\n//   backendDescription: 'NVIDIA GeForce RTX 3090' }\n```\n\nIt returns `null` before `load()` / after `unload()`. `backendId` follows the `BackendId` enum (`0` CPU, `1` Metal, `2` CUDA, `3` Vulkan, `4` OpenCL, `99` other; see `index.d.ts`).\n\nOnce a `run()` / `runStreaming()` job settles, `response.stats` carries a `RuntimeStats` object -- pipeline timings plus the post-fallback backend truth:\n\n```javascript\nconst response = await model.run(audioStream)\nawait response.onUpdate(() => {}).await()\n// response.stats.backendDevice   // 0 = CPU, 1 = GPU (after any runtime fallback)\n// response.stats.backendId       // BackendId code, as above\n// response.stats.gpuUnsupported  // 1 when a GPU was present but the engine ran\n//                                //   on CPU anyway (e.g. Mali Vulkan mis-compute,\n//                                //   or a vendor/tier declined by policy)\n// response.stats.audioDurationMs, encoderMs, decoderMs, melSpecMs, totalEncodedFrames, ...\n```\n\nA CPU `backendDevice` together with `gpuUnsupported === 1` is expected on GPUs the engine declines, not a regression. See `RuntimeStats` in `index.d.ts` for the full field list.\n\n### 6. Release Resources\n\n```javascript\ntry {\n  await model.unload()\n} catch (error) {\n  console.error('Failed to unload model:', error)\n}\n```\n\n## Quickstart example\n\n### 1. Clone the repo & install dependencies\n\n```bash\ngit clone https://github.com/tetherto/qvac.git\ncd qvac/packages/transcription-parakeet\nnpm install\nnpm run build\n```\n\n`npm install` fetches the JS dependencies. `npm run build` (`bare-make generate && bare-make build && bare-make install`) is what pulls the `parakeet-cpp` and `ggml-speech` vcpkg ports (the speech-stack ggml flavour, with the `qvac-speech-` library prefix), compiles the native addon, and installs `prebuilds/<platform>-<arch>/qvac__transcription-parakeet.bare`. Published npm releases ship these prebuilds, so consumers who `npm install @qvac/transcription-parakeet` skip the build step.\n\n### 2. Stage a model\n\n```bash\nnpm run setup-models -- -t tdt -q q8_0\n```\n\n### 3. Run the bundled examples\n\n```bash\n# Single-file transcription (any model type -- CTC / TDT / EOU / Sortformer)\nbare examples/transcribe.js \\\n     --model models/parakeet-tdt-0.6b-v3.q8_0.gguf \\\n     --audio examples/samples/sample-16k.wav\n\n# Combined ASR + diarization\nbare examples/diarized-transcribe.js \\\n     --asr-model  models/parakeet-tdt-0.6b-v3.q8_0.gguf \\\n     --diar-model models/sortformer-4spk-v1.q8_0.gguf \\\n     --audio      examples/samples/diarization-sample-16k.wav\n\n# Live mic transcription\nbare examples/live-mic.js --model models/parakeet-eou-120m-v1.q8_0.gguf --accumulate\n\n# Live mic + speaker tagging (recommended: v2.1 diar GGUF, AOSC auto-on)\nbare examples/live-mic-diarized.js \\\n     --asr-model  models/parakeet-tdt-0.6b-v3.q8_0.gguf \\\n     --diar-model models/diar_streaming_sortformer_4spk-v2.1.q8_0.gguf --accumulate\n\n# Same as above, with explicit AOSC tuning knobs exposed as CLI flags\nbare examples/live-mic-diarized-aosc.js \\\n     --asr-model  models/parakeet-tdt-0.6b-v3.q8_0.gguf \\\n     --diar-model models/diar_streaming_sortformer_4spk-v2.1.q8_0.gguf \\\n     --spk-cache-len 256 --chunk-right-context-ms 480 --accumulate\n```\n\n> If you use `npm run example:* -- ...` instead of `bare`, remember the `--` separator -- without it npm interprets `--model` as one of its own config flags.\n\nThe live-mic examples capture the default input device via `sox -d` (install: `brew install sox` / `apt install sox` / `choco install sox`). With `--accumulate`, transcripts append onto one line per turn and flush on silence, speaker change, or Ctrl-C.\n\n## Model Variants\n\n| Variant | Languages | Decoder | Default GGUF size (q8_0) | Notes |\n|---------|-----------|---------|-------------------------:|-------|\n| **CTC** | English | argmax CTC | ~ 700 MiB | Fast, no PnC. |\n| **TDT** | ~25 | RNN-T greedy + duration | ~ 715 MiB | Recommended default; PnC + auto-detect. |\n| **EOU** | English | RNN-T greedy + `<EOU>` | ~ 132 MiB | Streaming-trained; native end-of-turn token. |\n| **Sortformer v1** | n/a | Diarization head (sliding history) | ~ 141 MiB | 4-speaker. **Default for offline diarization.** |\n| **Sortformer v2.1 + AOSC** | n/a | Diarization head + speaker cache | ~ 141 MiB | 4-speaker. **Default for streaming diarization.** AOSC anchors speaker slots across silence/re-entry; auto-detected via GGUF metadata tag `parakeet.model_variant`. |\n\n## Other examples\n\n- [`examples/transcribe.js`](examples/transcribe.js) -- universal single-file transcribe / diarize (any GGUF, all model types).\n- [`examples/diarized-transcribe.js`](examples/diarized-transcribe.js) -- combined Sortformer + ASR pipeline (\"who said what\").\n- [`examples/live-mic.js`](examples/live-mic.js) -- live microphone transcription via `sox` and the streaming session.\n- [`examples/live-mic-diarized.js`](examples/live-mic-diarized.js) -- live mic with parallel Sortformer + ASR for speaker-tagged transcripts. Pass a v2.1 Sortformer GGUF to get AOSC speaker-cache streaming automatically.\n- [`examples/live-mic-diarized-aosc.js`](examples/live-mic-diarized-aosc.js) -- same as above but with CLI flags for the AOSC tuning knobs (`--spk-cache-len`, `--fifo-len`, `--chunk-right-context-ms`, `--spk-cache-enable`, etc.). Useful for A/B comparing AOSC vs the v1 sliding-window code path on the same v2.1 GGUF.\n- [`examples/decode-audio.js`](examples/decode-audio.js) -- decode + transcribe in one step. Same flag surface as `transcribe.js` but pipes the input through `@qvac/decoder-audio` (FFmpeg) first, so any container / codec FFmpeg supports (mp3, m4a, ogg, flac, mp4, ...) works -- not just 16 kHz mono `.wav` / raw s16le PCM.\n- [`examples/utils.js`](examples/utils.js) -- shared helpers used by the examples: `setupLogger` (native-log filtering), `readFileAsStream`, `parseWavFile`, `convertRawToFloat32`, `validatePaths`, `pushableStream` (live-mic feed), and `printResults`.\n\n## Glossary\n\n- **Bare** -- small, modular JavaScript runtime for desktop and mobile. [Learn more](https://docs.pears.com/bare-reference/overview).\n- **GGUF** -- single-file model format used by ggml-based runtimes; carries weights + tokenizer + hyperparameters in one file.\n- **QVAC** -- our open-source AI-SDK for building decentralized AI applications.\n\n## Error Range\n\nThrown errors are `QvacErrorAddonParakeet` instances (extending `QvacErrorBase`) and carry a numeric `.code` from this package's reserved range **24001 - 25000**, so callers can match programmatically on `err.code`. The codes are re-exported as `ERR_CODES` from the `./parakeet.js` subpath (`require('@qvac/transcription-parakeet/parakeet.js').ERR_CODES`).\n\n| Code | Name | Raised when |\n|------|------|-------------|\n| 24001 | `FAILED_TO_LOAD_WEIGHTS` | Weight streaming into the engine failed. |\n| 24002 | `FAILED_TO_CANCEL` | Cancelling an in-flight job failed. |\n| 24003 | `FAILED_TO_APPEND` | Appending audio / starting a job failed. |\n| 24004 | `FAILED_TO_GET_STATUS` | Reading the wrapper state failed. |\n| 24005 | `FAILED_TO_DESTROY` | Destroying the native instance failed. |\n| 24006 | `FAILED_TO_ACTIVATE` | Activating the model for inference failed. |\n| 24007 | `FAILED_TO_RESET` | Reset / reload / `endStreaming` teardown failed. |\n| 24008 | `FAILED_TO_PAUSE` | Pausing inference failed. |\n| 24009 | `MODEL_NOT_FOUND` | The configured `.gguf` path does not exist. |\n| 24010 | `INVALID_AUDIO_FORMAT` | Audio is not 16 kHz mono. |\n| 24015 | `INVALID_CONFIG` | A configuration value was rejected. |\n| 24016 | `JOB_ALREADY_RUNNING` | A job is already set or being processed. |\n| 24017 | `BUFFER_LIMIT_EXCEEDED` | Buffered audio for one `run()` exceeded `MAX_BUFFERED_BYTES` (500 MiB). |\n| 24018 | `INSTANCE_DESTROYED` | `load()` was called on a destroyed instance. |\n| 24019 | `JOB_CANCELLED` | The active job was cancelled. |\n\n## Resources\n\n- [NVIDIA Parakeet model cards](https://huggingface.co/collections/nvidia/parakeet-asr-models-66b50d5a37b9580ee4ba93c2) -- upstream `.nemo` checkpoints.\n\n## License\n\nThis project is licensed under the Apache-2.0 License -- see [LICENSE](LICENSE) for details. Model files are distributed under the **NVIDIA Open Model License**; see the upstream HuggingFace cards for the per-checkpoint terms.\n\nFor questions or issues, please open an issue on the GitHub repository.\n","readmeFilename":"README.md"}