whisper-cpp-node

whisper-cpp-node

Node.js bindings for whisper.cpp - fast speech-to-text with GPU acceleration.

Features

• Fast: Native whisper.cpp performance with GPU acceleration
• Cross-platform: macOS (Metal), Windows (Vulkan)
• Core ML: Optional Apple Neural Engine support for 3x+ speedup (macOS)
• OpenVINO: Optional Intel CPU/GPU encoder acceleration (Windows/Linux)
• Streaming VAD: Built-in Silero voice activity detection
• TypeScript: Full type definitions included
• Self-contained: No external dependencies, just install and use

Requirements

macOS:

• macOS 13.3+ (Ventura or later)
• Apple Silicon (M1/M2/M3/M4)
• Node.js 18+

Windows:

• Windows 10/11 (x64)
• Node.js 18+
• Vulkan-capable GPU (optional, for GPU acceleration)

Installation

npm install whisper-cpp-node
# or
pnpm add whisper-cpp-node

The platform-specific binary is automatically installed:

• macOS ARM64: @whisper-cpp-node/darwin-arm64
• Windows x64: @whisper-cpp-node/win32-x64

Quick Start

File-based transcription

import {
  createWhisperContext,
  transcribeAsync,
} from "whisper-cpp-node";

// Create a context with your model
const ctx = createWhisperContext({
  model: "./models/ggml-base.en.bin",
  use_gpu: true,
});

// Transcribe audio file
const result = await transcribeAsync(ctx, {
  fname_inp: "./audio.wav",
  language: "en",
});

// Result: { segments: [["00:00:00,000", "00:00:02,500", " Hello world"], ...] }
for (const [start, end, text] of result.segments) {
  console.log(`[${start} --> ${end}]${text}`);
}

// Clean up
ctx.free();

Buffer-based transcription

import {
  createWhisperContext,
  transcribeAsync,
} from "whisper-cpp-node";

const ctx = createWhisperContext({
  model: "./models/ggml-base.en.bin",
  use_gpu: true,
});

// Pass raw PCM audio (16kHz, mono, float32)
const pcmData = new Float32Array(/* your audio samples */);
const result = await transcribeAsync(ctx, {
  pcmf32: pcmData,
  language: "en",
});

for (const [start, end, text] of result.segments) {
  console.log(`[${start} --> ${end}]${text}`);
}

ctx.free();

Streaming transcription

Get real-time output as audio is processed. The on_new_segment callback fires for each segment as it's generated, while the final callback still receives all segments at completion (backward compatible):

import { createWhisperContext, transcribe } from "whisper-cpp-node";

const ctx = createWhisperContext({
  model: "./models/ggml-base.en.bin",
});

transcribe(ctx, {
  fname_inp: "./long-audio.wav",
  language: "en",

  // Called for each segment as it's generated
  on_new_segment: (segment) => {
    console.log(`[${segment.start}]${segment.text}`);
  },
}, (err, result) => {
  // Final callback still receives ALL segments at completion
  console.log(`Done! ${result.segments.length} segments`);
  ctx.free();
});

API

createWhisperContext(options)

Create a persistent context for transcription.

interface WhisperContextOptions {
  model: string;           // Path to GGML model file (required)
  use_gpu?: boolean;       // Enable GPU acceleration (default: true)
                           // Uses Metal on macOS, Vulkan on Windows
  use_coreml?: boolean;    // Enable Core ML on macOS (default: false)
  use_openvino?: boolean;  // Enable OpenVINO encoder on Intel (default: false)
  openvino_device?: string; // OpenVINO device: 'CPU', 'GPU', 'NPU' (default: 'CPU')
  openvino_model_path?: string; // Path to OpenVINO encoder model (auto-derived)
  openvino_cache_dir?: string;  // Cache dir for compiled OpenVINO models
  flash_attn?: boolean;    // Enable Flash Attention (default: false)
  gpu_device?: number;     // GPU device index (default: 0)
  dtw?: string;            // DTW preset for word timestamps
  no_prints?: boolean;     // Suppress log output (default: false)
}

transcribeAsync(context, options)

Transcribe audio (Promise-based). Accepts either a file path or PCM buffer.

// File input
interface TranscribeOptionsFile {
  fname_inp: string;       // Path to audio file
  // ... common options
}

// Buffer input
interface TranscribeOptionsBuffer {
  pcmf32: Float32Array;    // Raw PCM (16kHz, mono, float32, -1.0 to 1.0)
  // ... common options
}

// Common options (partial list - see types.ts for full options)
interface TranscribeOptionsBase {
  // Language
  language?: string;       // Language code ('en', 'zh', 'auto')
  translate?: boolean;     // Translate to English
  detect_language?: boolean; // Auto-detect language

  // Threading
  n_threads?: number;      // CPU threads (default: 4)
  n_processors?: number;   // Parallel processors

  // Audio processing
  offset_ms?: number;      // Start offset in ms
  duration_ms?: number;    // Duration to process (0 = all)

  // Output control
  no_timestamps?: boolean; // Disable timestamps
  max_len?: number;        // Max segment length (chars)
  max_tokens?: number;     // Max tokens per segment
  split_on_word?: boolean; // Split on word boundaries
  token_timestamps?: boolean; // Include token-level timestamps

  // Sampling
  temperature?: number;    // Sampling temperature (0.0 = greedy)
  beam_size?: number;      // Beam search size (-1 = greedy)
  best_of?: number;        // Best-of-N sampling

  // Thresholds
  entropy_thold?: number;  // Entropy threshold
  logprob_thold?: number;  // Log probability threshold
  no_speech_thold?: number; // No-speech probability threshold

  // Context
  prompt?: string;         // Initial prompt text
  no_context?: boolean;    // Don't use previous context

  // VAD preprocessing
  vad?: boolean;           // Enable VAD preprocessing
  vad_model?: string;      // Path to VAD model
  vad_threshold?: number;  // VAD threshold (0.0-1.0)
  vad_min_speech_duration_ms?: number;
  vad_min_silence_duration_ms?: number;
  vad_speech_pad_ms?: number;

  // Callbacks
  progress_callback?: (progress: number) => void;
  on_new_segment?: (segment: StreamingSegment) => void;  // Streaming callback
}

// Streaming segment (passed to on_new_segment callback)
interface StreamingSegment {
  start: string;           // Start timestamp "HH:MM:SS,mmm"
  end: string;             // End timestamp
  text: string;            // Transcribed text
  segment_index: number;   // 0-based index
  is_partial: boolean;     // Reserved for future use
  tokens?: StreamingToken[]; // Only if token_timestamps enabled
}

// Result
interface TranscribeResult {
  segments: TranscriptSegment[];
}

// Segment is a tuple: [start, end, text]
type TranscriptSegment = [string, string, string];
// Example: ["00:00:00,000", "00:00:02,500", " Hello world"]

createVadContext(options)

Create a voice activity detection context for streaming audio.

interface VadContextOptions {
  model: string;           // Path to Silero VAD model
  threshold?: number;      // Speech threshold (default: 0.5)
  n_threads?: number;      // Number of threads (default: 1)
  no_prints?: boolean;     // Suppress log output
}

interface VadContext {
  getWindowSamples(): number;  // Returns 512 (32ms at 16kHz)
  getSampleRate(): number;     // Returns 16000
  process(samples: Float32Array): number;  // Returns probability 0.0-1.0
  reset(): void;               // Reset LSTM state
  free(): void;                // Release resources
}

VAD Example

import { createVadContext } from "whisper-cpp-node";

const vad = createVadContext({
  model: "./models/ggml-silero-v6.2.0.bin",
  threshold: 0.5,
});

const windowSize = vad.getWindowSamples(); // 512 samples

// Process audio in 32ms chunks
function processAudioChunk(samples: Float32Array) {
  const probability = vad.process(samples);
  if (probability >= 0.5) {
    console.log("Speech detected!", probability);
  }
}

// Reset when starting new audio stream
vad.reset();

// Clean up when done
vad.free();

Core ML Acceleration (macOS)

For 3x+ faster encoding on Apple Silicon:

• Generate a Core ML model:

  pip install ane_transformers openai-whisper coremltools
  ./models/generate-coreml-model.sh base.en
  

• Place it next to your GGML model:

  models/ggml-base.en.bin
  models/ggml-base.en-encoder.mlmodelc/
  

• Enable Core ML:

  const ctx = createWhisperContext({
    model: "./models/ggml-base.en.bin",
    use_coreml: true,
  });
  

OpenVINO Acceleration (Intel)

For faster encoder inference on Intel CPUs and GPUs (requires build with OpenVINO support):

• Install OpenVINO and convert the model:

  pip install openvino openvino-dev
  python models/convert-whisper-to-openvino.py --model base.en
  

• The OpenVINO model files are placed next to your GGML model:

  models/ggml-base.en.bin
  models/ggml-base.en-encoder-openvino.xml
  models/ggml-base.en-encoder-openvino.bin
  

• Enable OpenVINO:

  const ctx = createWhisperContext({
    model: "./models/ggml-base.en.bin",
    use_openvino: true,
    openvino_device: "CPU",  // or "GPU" for Intel iGPU
    openvino_cache_dir: "./openvino_cache", // optional, speeds up init
  });
  

Note: OpenVINO support requires the addon to be built with -DADDON_OPENVINO=ON.

Models

Download models from Hugging Face:

# Base English model (~150MB)
curl -L -o models/ggml-base.en.bin \
  https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-base.en.bin

# Large v3 Turbo quantized (~500MB)
curl -L -o models/ggml-large-v3-turbo-q4_0.bin \
  https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-large-v3-turbo-q4_0.bin

# Silero VAD model (for streaming VAD)
curl -L -o models/ggml-silero-v6.2.0.bin \
  https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-silero-v6.2.0.bin

License

MIT