smart-whisper

Smart-Whisper is a native Node.js addon designed for efficient and streamlined interaction with the whisper.cpp, with automatic model offloading and reloading and model manager.

Features

• Node.js Native Addon Interaction: Directly interact with whisper.cpp, ensuring fast and efficient processing.
• Single Model Load for Multiple Inferences: Load the model once and perform multiple and parallel inferences, optimizing resource usage and reducing load times.
• Automatic Model Offloading and Reloading: Manages memory effectively by automatically offloading and reloading models as needed.
• Model Manager: Automates the process of downloading and updating models, ensuring that the latest models are always available.

Installation

The standard installation supports Windows, macOS, and Linux out of the box. And it also automatically enables the GPU and CPU acceleration on macOS.

npm i smart-whisper

Support Matrix:

OS and Arch	CPU	GPU
macOS Apple Silicon	✅ (Acceleration)	✅ (Metal)
macOS Intel	✅ (Acceleration)	BYOL
Linux / Windows	✅	BYOL

• ✅: Out of the box support with standard installation.
• BYOL: Bring Your Own Library, see Acceleration for more information.

Acceleration

Due to the complexity of the different acceleration methods for different devices. You need to compile the libwhisper.a or libwhisper.so from whisper.cpp yourself.

And then set the BYOL (Bring Your Own Library) environment variable to the path of the compiled library.

BYOL='/path/to/libwhisper.a' npm i smart-whisper

You may need to link other libraries like:

BYOL='/path/to/libwhisper.a -lopenblas' npm i smart-whisper

OpenBLAS

For Linux and Windows without GPU, the best acceleration method might be using OpenBLAS. After installing OpenBLAS, you can compile the libwhisper.a with the following command:

git clone https://github.com/ggerganov/whisper.cpp
cd whisper.cpp
WHISPER_OPENBLAS=1 make -j

CUDA and other acceleration methods

Check out the whisper.cpp repository for more information.

Documentation

The documentation is available at https://jacoblincool.github.io/smart-whisper/.

Example

See examples for more examples.

import { Whisper } from "smart-whisper";
import { decode } from "node-wav";
import fs from "node:fs";

const model = process.argv[2];
const wav = process.argv[3];

const whisper = new Whisper(model, { gpu: true });
const pcm = read_wav(wav);

const task = await whisper.transcribe(pcm, { language: "auto" });
console.log(await task.result);

await whisper.free();
console.log("Maunally freed");

function read_wav(file: string): Float32Array {
    const { sampleRate, channelData } = decode(fs.readFileSync(file));

    if (sampleRate !== 16000) {
        throw new Error(`Invalid sample rate: ${sampleRate}`);
    }
    if (channelData.length !== 1) {
        throw new Error(`Invalid channel count: ${channelData.length}`);
    }

    return channelData[0];
}

The transcribe method returns a task object that can be used to retrieve the result of the transcription, which also emits events for the progress of the transcription.

const task = await whisper.transcribe(pcm, { language: "auto" });
task.on("transcribed", (result) => {
    console.log("Transcribed", result);
});
console.log(await task.result);

Links

• GitHub Repository
• NPM Package
• Documentation

License

MIT