Defer, which supports ffmpeg natively, can be used to boost the speed of applications that run inside Bun. Because of its native support, Defer’s serverless background job processing retains the same unified developer experience, making the Defer/Bun combination a perfect environment for complex data processing.

One limitation of OpenAI Whisper API is its maximum file size of 25 MB, which is easily reached, for example, on average meeting recording duration. To overcome this limitation, you can leverage Defer’s native ffmpeg support to break the file into chunks of less than 24MB.

Use case—Audio subtitle generation

Here’s a NextJS app that generates audio subtitles with Web API’s Streams API. The createTranscript() background function downloads meeting audios and forwards them to the OpenAI Whisper API.

With the help of Defer’s defer(createTranscript,..)), the chunking of the audio file with ffmepg will perform in the background:

import { defer } from "@defer/client";
import { S3Client, GetObjectCommand } from "@aws-sdk/client-s3";
import ffmpeg from "fluent-ffmpeg";

import s3Config from "../s3Client";
import openAIClient from "../openAIClient";

async function createTranscript(meetingID: string) {
  // we fetch the meeting audio file as a stream
  const file: ReadableStream = s3
      Bucket: "meetings-recordings",
      Key: `meeting-recording-${meetingID}`,

  // we leverage ffmpeg to create audio chunks of 10min
    .addOutputOptions("-f segment")
    .addOutputOptions("-segment_time 10:00")
    .addOutputOptions("-reset_timestamps 1")

  // retrieve the created audio chunk files
  //   and forward them to the OpenAI Whisper API
  const files = await glob("audio_*.m4a");
  files.forEach((file) => {
        file: createReadStream(file),
        model: "whisper-1",

  await Promise.allSettled( =>{
      file: createReadStream(file),
      model: "whisper-1",

  // Finally: save the transcripts...
  //   ...

export default defer(createTranscript, { concurrency: 5, retry: 2 });

As you can see, Defer native supports for ffmepg enable processing of large audio files at scale, without timeout limitations. And Bun makes it easy to deal with large streams of data thanks to its Web API native support and more efficient memory usage.