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To utilize OpenAI's Whisper model for speech recognition with word-level timestamps in a Node.js environment, you can employ the nodejs-whisper package. This package provides Node.js bindings for the Whisper model and supports word-level timestamping.

Key Takeaways

The nodejs-whisper package enables easy word-level timestamp integration in Node.js.
Word-level timestamps improve transcription accuracy and timing precision for subtitles and analysis.
Whisper offers flexible model and output options to suit different project needs.

Step-by-Step Guide

Install Build Tools:

Ensure that your system has the necessary build tools installed. On Debian-based systems, you can install them using:
```
sudo apt update
sudo apt install build-essential
```
For Windows users, installing MinGW-w64 or MSYS2 is recommended. After installation, ensure that mingw32-make or make is available in your system's PATH.

Install the nodejs-whisper Package:

Use npm to install the package:

npm install nodejs-whisper

Download the Whisper Model:

After installing the package, download the desired Whisper model:
```
npx nodejs-whisper download
```
The available models include:
- tiny
- tiny.en
- base
- base.en
- small
- small.en
- medium
- medium.en
- large-v1
- large
- large-v3-turbo
Choose a model that balances performance and accuracy based on your requirements.

Transcribe Audio with Word-Level Timestamps:

Create a JavaScript or TypeScript file (e.g., transcribe.js) and add the following code:

const path = require('path');
const { nodewhisper } = require('nodejs-whisper');

// Provide the exact path to your audio file
const filePath = path.resolve(__dirname, 'YourAudioFileName.wav');

(async () => {
  await nodewhisper(filePath, {
    modelName: 'base.en', // Specify the downloaded model name
    autoDownloadModelName: 'base.en', // (Optional) Auto-download the model if not present
    removeWavFileAfterTranscription: false, // (Optional) Remove WAV file after transcription
    withCuda: false, // (Optional) Use CUDA for faster processing if available
    logger: console, // (Optional) Logging instance, defaults to console
    whisperOptions: {
      outputInCsv: false, // Output result in CSV file
      outputInJson: false, // Output result in JSON file
      outputInJsonFull: false, // Output result in JSON file with detailed information
      outputInLrc: false, // Output result in LRC file
      outputInSrt: true, // Output result in SRT file
      outputInText: false, // Output result in TXT file
      outputInVtt: false, // Output result in VTT file
      outputInWords: true, // Output result in WTS file for karaoke
      translateToEnglish: false, // Translate from source language to English
      wordTimestamps: true, // Enable word-level timestamps
      timestamps_length: 20, // Amount of dialogue per timestamp pair
      splitOnWord: true, // Split on word rather than on token
    },
  });
})();

Replace 'YourAudioFileName.wav' with the path to your audio file. This script will process the audio and generate an SRT file with word-level timestamps.

Additional Notes

Audio Format: The nodejs-whisper package automatically converts audio files to WAV format with a 16000 Hz frequency to support the Whisper model.
CUDA Support: If you have a compatible NVIDIA GPU and CUDA installed, you can set withCuda: true for faster processing.
Output Formats: The whisperOptions allow you to specify various output formats, including CSV, JSON, LRC, SRT, TXT, VTT, and WTS. Adjust these options based on your needs.