@lumen-labs-dev/whisper-node

Whisper-Node

Node.js bindings for OpenAI's Whisper. Transcription done local with VAD and Speaker Diarization.

Features

• Output transcripts to JSON (also .txt .srt .vtt)
• Optimized for CPU (Including Apple Silicon ARM)
• Timestamp precision to single word

Installation

• Add dependency to project

npm install @lumen-labs-dev/whisper-node

• Download a Whisper model [OPTIONAL]

npx whisper-node

Alternatively, the same downloader can be invoked as:

npx whisper-node download

Windows (precompiled binaries)

On Windows, whisper-node downloads precompiled Whisper binaries during install (or first use) and runs them directly — no local build tools are required.

• To choose a binary flavor before installing:

setx WHISPER_WIN_FLAVOR cpu
# or: blas | cublas-11.8 | cublas-12.4

• Ensure the Microsoft Visual C++ 2015–2022 Redistributable (x64) is installed. If you see error code 0xC0000135 when starting the binary, install the redistributable and retry.

• Optional: point to a custom Windows binary subfolder inside lib/whisper.cpp:

setx WHISPER_WIN_BIN_DIR Win64
# examples: Win64 | BlasWin64 | CublasWin64-11.8 | CublasWin64-12.4

Non-Windows platforms still build from source when needed.

If the package was installed without bundling lib/whisper.cpp, the downloader will automatically set up the upstream whisper.cpp assets inside node_modules/@lumen-labs-dev/whisper-node/lib/whisper.cpp. On Windows, this uses precompiled release archives; on non-Windows it may clone and build from source.

Usage

import { whisper } from '@lumen-labs-dev/whisper-node';

const transcript = await whisper("example/sample.wav");

console.log(transcript); // output: [ {start,end,speech} ]

Output (JSON)

[
  {
    "start":  "00:00:14.310", // time stamp begin
    "end":    "00:00:16.480", // time stamp end
    "speech": "howdy"         // transcription
  }
]

Full Options List

import { whisper } from '@lumen-labs-dev/whisper-node';

const filePath = "example/sample.wav"; // required

const options = {
  modelName: "base.en",       // default
  // modelPath: "/custom/path/to/model.bin", // use model in a custom directory (cannot use along with 'modelName')
  whisperOptions: {
    language: 'auto',          // default (use 'auto' for auto detect)
    gen_file_txt: false,      // outputs .txt file
    gen_file_subtitle: false, // outputs .srt file
    gen_file_vtt: false,      // outputs .vtt file
    // Enable per-word timestamps only if you really need them.
    // For typical sentence/segment output, leave this off.
    // When per-word is detected, whisper-node will automatically merge words into sentences.
    word_timestamps: false,
    no_timestamps: false,      // when true, Whisper prints only text (no [..] lines)
    // timestamp_size: 0      // cannot use along with word_timestamps:true
  },
  // Forwarded to shelljs.exec (defaults shown)
  shellOptions: {
    silent: true,
    async: false,
  }
}

const transcript = await whisper(filePath, options);

API

• Function: whisper(filePath: string, options?: { modelName?, modelPath?, whisperOptions?, shellOptions? }) => Promise<ITranscriptLine[]>
• Models: pass either modelName (one of the official names) or a modelPath pointing to a .bin file. Do not pass both.
• Return: array of { start, end, speech } objects parsed from Whisper's console output.

Notes:

• Setting no_timestamps: true changes Whisper's console output format. Since the JSON parser expects [start --> end] text lines, using no_timestamps: true will typically yield an empty array. Prefer timestamp_size (segment-level) or word_timestamps (word-level) when you need structured JSON.
• If you enable word_timestamps, whisper-node will auto-merge single-word lines into sentence-level segments using pause and punctuation heuristics. You can still access raw lines before merge by calling the underlying CLI yourself.
• You can still generate .txt/.srt/.vtt files via gen_file_* flags even if you don't use the JSON array.

Automatic audio conversion (fluent-ffmpeg)

whisper-node will automatically convert common audio/video inputs (e.g., mp3, m4a, wav, mp4) into 16 kHz mono WAV when needed using fluent-ffmpeg and the bundled ffmpeg-static/ffprobe-static binaries. The converted file is written next to your input as <name>.wav16k.wav and used for transcription.

If your input is already a 16kHz mono WAV, it is used as-is without conversion.

Optional: Speaker diarization (Node, naive)

You can enrich the transcript with speaker labels without Python using a lightweight, naive diarization:

• VAD by energy threshold
• K-means clustering over simple features

Usage:

import whisper, { DiarizationOptions } from '@lumen-labs-dev/whisper-node';

const transcript = await whisper('audio.mp3', {
  diarization: {
    enabled: true,
    numSpeakers: 2, // or omit to auto-guess a small K
  }
});

// Each transcript line may include speaker: 'S0', 'S1', ...

Notes:

• This is a basic approach and won’t handle overlapping speakers or noisy audio robustly. It is intended as a simple, CPU-only baseline.
• For production-grade results, consider integrating an advanced pipeline (e.g., WhisperX/pyannote) externally and mapping their segments back to ITranscriptLine.

Input File Format

Files must be .wav and 16 kHz

Example .mp3 file converted with an FFmpeg command: ffmpeg -i input.mp3 -ar 16000 output.wav

CLI (Model Downloader)

Run the interactive downloader (downloads into node_modules/@lumen-labs-dev/whisper-node/lib/whisper.cpp/models; non-Windows will build on first use if needed):

npx @lumen-labs-dev/whisper-node

You will be prompted to choose one of:

Model	Disk	RAM
tiny	75 MB	~273 MB
tiny.en	75 MB	~273 MB
base	142 MB	~388 MB
base.en	142 MB	~388 MB
small	466 MB	~852 MB
small.en	466 MB	~852 MB
medium	1.5 GB	~2.1 GB
medium.en	1.5 GB	~2.1 GB
large-v1	2.9 GB	~3.9 GB
large	2.9 GB	~3.9 GB

If you already have a model elsewhere, pass modelPath in the API and skip the downloader.

Configuration file

You can configure defaults without passing options in code by creating one of the following files in your project root:

• whisper-node.config.json
• whisper.config.json

Or set an explicit path via environment variable WHISPER_NODE_CONFIG=/abs/path/to/config.json.

Example config:

{
  "modelName": "base.en",
  "modelPath": "/custom/models/ggml-base.en.bin",
  "whisperOptions": {
    "language": "auto",
    "word_timestamps": true
  },
  "shellOptions": {
    "silent": true
  }
}

Notes:

• Options provided directly to the whisper() function always override values from the config file.
• The downloader CLI will use modelName from config to skip the prompt when valid.

Logging

Control verbosity via environment variable (defaults to INFO):

# ERROR | WARN | INFO | DEBUG
setx WHISPER_NODE_LOG_LEVEL DEBUG

Troubleshooting

• "'make' failed": Ensure build tools are installed.
  • Windows: install make (see link above) or use MSYS2/Chocolatey alternatives.
  • macOS: xcode-select --install.
  • Linux: sudo apt-get install build-essential (Debian/Ubuntu) or the equivalent for your distro.
• "'' not downloaded! Run 'npx whisper-node download'": Either run the downloader or provide a valid modelPath.
• Empty transcript array: Remove no_timestamps: true. The JSON parser expects timestamped lines like [00:00:01.000 --> 00:00:02.000] text.
• Paths with spaces: Supported. Paths are automatically quoted.
• Windows binary won't start (0xC0000135): Install the Microsoft Visual C++ 2015–2022 Redistributable (x64) and retry.
• Large inputs: Very long audio can use significant memory for conversion/diarization. Consider splitting into smaller chunks.

Project structure

src/
  cli/          # CLI entrypoints (e.g., download)
  config/       # constants and configuration
  core/         # domain logic (whisper command builder)
  infra/        # process/shell integration with whisper.cpp
  utils/        # helper utilities (e.g., transcript parsing)
  scripts/      # development/test scripts

Made with

• Whisper OpenAI
• ShellJS

Roadmap

• Support projects not using Typescript
• Allow custom directory for storing models
• Config files as alternative to model download cli
• Remove path, shelljs and prompt-sync package for browser, react-native expo, and webassembly compatibility
• fluent-ffmpeg to automatically convert to 16Hz .wav files as well as support separating audio from video
• Speaker diarization (basic Node baseline)
• Implement WhisperX as optional alternative model for diarization and higher precision timestamps (as alternative to C++ version)
• Add option for viewing detected language as described in Issue 16
• Include TypeScript types in d.ts file
• Add support for language option
• Add support for transcribing audio streams as already implemented in whisper.cpp

Modifying whisper-node

npm run build - runs tsc, outputs to /dist and gives sh permission to dist/cli/download.js

npm run test - runs the compiled example in dist/scripts/test.js

Acknowledgements

• Georgi Gerganov
• Ari
• Maximiliano Veiga