Node.js에서 Word-Level 타임스탬프 Whisper를 실행하는 방법

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.

Step-by-Step Guide

1. 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.

2. Install the nodejs-whisper Package:

   Use npm to install the package:

   npm install nodejs-whisper

3. 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.

4. 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.

Conclusion

By following these steps, you can effectively perform speech recognition with word-level timestamps in a Node.js environment using the Whisper model.

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