fluester – [ˈflʏstɐ]
Node.js bindings for OpenAI's Whisper. Hard-fork of whisper-node.
Features
- Output transcripts to JSON (also .txt .srt .vtt)
- Optimized for CPU (Including Apple Silicon ARM)
- Timestamp precision to single word
Installation
Requirements
make
and everything else listed as required to compile whisper.cpp- Node.js >= 20
- Add dependency to project
npm install @pr0gramm/fluester
- Download whisper model of choice
npx --package @pr0gramm/fluester download-model
- Compile whisper.cpp if you don't want to provide you own version:
npx --package @pr0gramm/fluester compile-whisper
Usage
Important: The API only supports WAV files (just like the original whisper.cpp). You need to convert any files to a supported format before.
You can do this using ffmpeg (example taken from the whisper project):
ffmpeg -i input.mp3 -ar 16000 -ac 1 -c:a pcm_s16le output.wav
Translation
import { createWhisperClient } from "@pr0gramm/fluester";
const client = createWhisperClient({
modelName: "base",
});
const transcript = await client.translate("example/sample.wav");
console.log(transcript);
Output (JSON)
[
{
"start": "00:00:14.310",
"end": "00:00:16.480",
"speech": "howdy"
}
]
Language Detection
import { createWhisperClient } from "@pr0gramm/fluester";
const client = createWhisperClient({
modelName: "base",
});
const result = await client.detectLanguage("example/sample.wav");
if(!result) {
console.log(`Detected: ${result.language} with probability ${result.probability}`);
} else {
console.log("Did not detect anything :(");
}
Tricks
This library is designed to work well in dockerized environments.
We took time and made some steps independent from each other, so they can be used in a multi-stage docker build.
FROM node:latest as dependencies
WORKDIR /app
COPY package.json package-lock.json ./
RUN npm ci
RUN npx --package @pr0gramm/fluester compile-whisper
RUN npx --package @pr0gramm/fluester download-model tiny
FROM node:latest
WORKDIR /app
COPY --from=dependencies /app/node_modules /app/node_modules
COPY ./ ./
This includes the model in the image. If you want to keep your image small, you can also download the model in your entrypoint using the commands above.
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