Subtitle Generator

Generate subtitle files (SRT, VTT, TXT) from audio files using OpenAI's Whisper model. Automatically downloads the binary and model files for your operating system.

Features

• 🎬 Supports multiple subtitle formats (SRT, VTT, TXT)
• 🤖 Uses OpenAI's Whisper for accurate transcription
• 🔧 Multiple model sizes (small, medium, large)
• 🖥️ Cross-platform support (Windows, macOS, Linux)
• ⚡ Configurable thread count for performance tuning
• 🌍 Multi-language support with auto-detection

Installation

npm install @codearcade/subtitle-generator

Quick Start

1. Initialize the Package

Run the initialization command to download the Whisper binary and model:

npx subtitle-generator init

The init command will:

• Detect your operating system
• Download the appropriate Whisper binary
• Prompt you to choose a model size:
  • small - Fast transcription with good accuracy
  • medium - Balanced performance and accuracy
  • large - Highest accuracy (requires more resources)

2. Use in Your Code

Create a Node.js script to transcribe audio files:

import path from "path";
import { Whisper } from "@codearcade/subtitle-generator";

const whisper = new Whisper({
  modelPath: path.join(process.cwd(), "models", "ggml-small.bin"),
  threads: 8,
  outputFormat: "srt", // "srt" | "txt" | "vtt"
  srtLang: "English",
  // audioLang: "English" // leave undefined for auto-detection
});

const inputFile = path.join(process.cwd(), "input", "audio.mp3");
const outputFile = path.join(process.cwd(), "output", "audio.srt");

async function main() {
  await whisper.transcribe(inputFile, outputFile);
  console.log("Transcription finished!");
}

main();

API Reference

Whisper Constructor

Create a new Whisper instance with configuration options:

const whisper = new Whisper(options);

Options

Methods

transcribe(inputPath, outputPath)

Transcribe an audio file and save the output to a subtitle file.

Parameters:

• inputPath (string): Path to the audio file (supports mp3, wav, m4a, flac, etc.)
• outputPath (string): Path where the subtitle file will be saved

Returns: Promise

Example:

await whisper.transcribe("./audio/interview.mp3", "./subtitles/interview.srt");

Supported Audio Formats

• MP3
• WAV
• M4A
• FLAC
• OGG
• WEBM
• And other common audio formats

Supported Output Formats

SRT (SubRip)

Standard subtitle format with timecodes and subtitle index:

1
00:00:00,000 --> 00:00:05,000
First subtitle line

2
00:00:05,000 --> 00:00:10,000
Second subtitle line

VTT (WebVTT)

Web video text track format:

WEBVTT

00:00:00.000 --> 00:00:05.000
First subtitle line

00:00:05.000 --> 00:00:10.000
Second subtitle line

TXT (Plain Text)

Simple text transcription with timestamps:

[00:00:00] First subtitle line
[00:00:05] Second subtitle line

Configuration

Model Selection

Models are stored in a models directory. After running init, choose your model size:

• small (~140MB) - Good for fast transcription with reasonable accuracy
• medium (~380MB) - Better accuracy with moderate performance impact
• large (~1.4GB) - Highest accuracy, requires more RAM and processing time

Thread Count

Adjust the threads option based on your CPU:

const whisper = new Whisper({
  modelPath: "./models/ggml-small.bin",
  threads: 16, // Use more threads on high-core-count CPUs
  outputFormat: "srt",
});

Examples

Batch Processing Multiple Files

import path from "path";
import { Whisper } from "@codearcade/subtitle-generator";
import fs from "fs";

const whisper = new Whisper({
  modelPath: path.join(process.cwd(), "models", "ggml-small.bin"),
  threads: 8,
  outputFormat: "srt",
});

async function processAudioFiles() {
  const inputDir = "./input";
  const outputDir = "./output";

  const files = fs.readdirSync(inputDir);

  for (const file of files) {
    if (!file.endsWith(".mp3")) continue;

    const inputPath = path.join(inputDir, file);
    const outputPath = path.join(outputDir, file.replace(".mp3", ".srt"));

    console.log(`Processing ${file}...`);
    await whisper.transcribe(inputPath, outputPath);
    console.log(`Completed: ${file}`);
  }
}

processAudioFiles();

Auto-Detect Language

const whisper = new Whisper({
  modelPath: "./models/ggml-small.bin",
  threads: 8,
  outputFormat: "srt",
  // audioLang is undefined - language will be auto-detected
});

await whisper.transcribe("./audio/unknown-language.mp3", "./output/result.srt");

Specify Language

const whisper = new Whisper({
  modelPath: "./models/ggml-small.bin",
  threads: 8,
  outputFormat: "srt",
  audioLang: "es", // Spanish
});

await whisper.transcribe("./audio/spanish.mp3", "./output/spanish.srt");

Project Structure

After using the package, your project structure might look like:

project/
├── models/
│   └── ggml-small.bin        # Downloaded Whisper model
├── input/
│   ├── audio1.mp3
│   └── audio2.mp3
├── output/
│   ├── audio1.srt
│   └── audio2.srt
├── transcribe.js             # Your transcription script
└── package.json

Troubleshooting

"Model file not found"

Make sure you ran npx subtitle-generator init first to download the model.

"Binary not found for your OS"

The package only supports Windows, macOS, and Linux. Check that you're on a supported operating system.

Slow transcription

• Increase the threads option (if your CPU has more cores)
• Use a smaller model (small instead of large)
• Ensure your system has adequate RAM available

Out of memory errors

• Use a smaller model size
• Reduce the threads count
• Process files one at a time instead of in parallel

Performance Tips

1. Model Size: Start with small model for speed, upgrade to medium or large if accuracy isn't sufficient
2. Thread Count: Set to number of CPU cores for optimal performance
3. Batch Processing: Process multiple files sequentially to avoid memory issues
4. Audio Quality: Clear audio produces better results than noisy recordings

License

MIT © CodeArcade

Support

For issues, questions, or contributions, visit the GitHub repository.