How to Split Long Audio Files Under 25MB for the Whisper API in Node.js

Below is an approach that others have found useful when dealing with audio files that exceed the Whisper API’s 25MB limit.

Recommended Approach

1. Split the Audio File
   Instead of sending one large conversation file, split the audio into smaller segments that are each below the 25MB limit. This minimizes upload issues while still letting you process long conversations.

2. Preserve Context Between Chunks
   To avoid losing the flow of conversation when splitting, you might consider overlapping the ending of one segment with the beginning of the next. This overlap can help maintain context, especially if the transcription results are later stitched together.

3. Using Node.js Tools
   While some examples in the documentation use Python’s PyDub library, in a Node.js environment you can achieve similar functionality with FFmpeg. A popular Node.js package is fluent-ffmpeg, which provides a convenient API to work with FFmpeg commands.

Example Using Fluent-ffmpeg

Below is a sample code snippet that demonstrates how you could split an audio file into 10-minute segments using fluent-ffmpeg in Node.js. Adjust the segment length as needed to ensure each chunk is below 25MB.

JAVASCRIPTCopied
const ffmpeg = require('fluent-ffmpeg');
const path = require('path');

const audioPath = path.resolve(__dirname, 'your_audio_file.mp3');
const outputDir = path.resolve(__dirname, 'chunks');

// Define duration for segment (e.g., 10 minutes = 600 seconds)
const segmentDuration = 600;

ffmpeg(audioPath)
  // Set the segment options: segments of segmentDuration seconds
  .outputOptions([
    '-f segment',
    `-segment_time ${segmentDuration}`,
    '-c copy'
  ])
  .on('end', () => {
    console.log('Audio file has been split successfully.');
  })
  .on('error', (err) => {
    console.error('Error while splitting audio:', err.message);
  })
  .save(`${outputDir}/chunk_%03d.mp3`);

Additional Best Practices

• Dynamic Segment Sizing:
  If your audio files vary widely in bitrate or are compressed differently, you may need to adjust chunk durations dynamically to ensure that each chunk remains under 25MB.

• Stitching Transcriptions:
  Once you have the transcription responses for each segment, consider merging them intelligently based on the overlapping parts to maintain the continuity of the conversation.

• Error Handling & Retries:
  Ensure your process is robust by adding error handling for failed transcriptions and possibly queuing retries.

Community Insights

Many developers have successfully tackled this challenge by combining FFmpeg-based chunking with extra logic to maintain context across segments. The key is to balance segment duration with file size constraints without sacrificing the conversation flow.

This approach should work well with your Node.js, React, and Express stack and is in line with guidance from the Whisper API documentation regarding handling large audio files.

Feel free to ask if you have further questions on integrating this solution with your transcription workflow!