Simple Test: Using WhisperDesktop for Speech-to-Text

TLDR

• WhisperDesktop is an offline tool that allows you to run OpenAI Whisper on Windows without needing a Python environment.
• It is recommended to prioritize the ggml-medium.bin model, as it offers the best balance between accuracy and processing speed.
• Users with dedicated graphics cards should use ggml-medium.bin; users with integrated graphics should use ggml-small.bin for daily tasks and ggml-medium.bin for important content.
• Conversion performance is highly correlated with model size and hardware specifications (VRAM). The ggml-large model may cause conversion failures or empty outputs on certain hardware.
• The developer has not updated WhisperDesktop for a long time. It is recommended to switch to the more actively maintained and faster Subtitle Edit with Faster-Whisper solution.

WARNING

The WhisperDesktop developer has not updated the software for a long time. It is currently recommended to switch to Subtitle Edit with Faster-Whisper, which is more actively maintained and faster. Please refer to: Using Subtitle Edit with Faster-Whisper for Local Speech-to-Text.

Software Installation and Model Configuration

WhisperDesktop provides a graphical interface that allows users to run Whisper models without setting up a Python environment.

• Download: Go to the WhisperDesktop GitHub Releases page and download WhisperDesktop.zip.
• Model Download: Download the corresponding .bin model files from Huggingface Whisper.
• Model Selection Recommendations:
  • tiny / base: Suitable for environments with extremely limited hardware resources, but accuracy is lower.
  • small: The baseline for daily use on integrated graphics.
  • medium: Recommended model, offering the most balanced performance in terms of accuracy and speed.
  • large: Highest accuracy, but requires significant VRAM (approx. 10GB) and may fail on some hardware.

Performance and Hardware Requirement Analysis

When do you encounter performance bottlenecks? When processing long audio files or using models that are too large, hardware specifications (especially VRAM) will directly determine the conversion speed and success rate.

Test Data Comparison

The following tests are based on a 5-minute and 16-second mp3 file:

• Dedicated Graphics Card (RTX 4070 Ti Super 16GB):
  • Using ggml-medium.bin: Only 11 seconds.
  • Using ggml-large-v3.bin: Took 22 minutes and 01 seconds, and may result in empty files in practice.
• Integrated Graphics (i7-12700H):
  • Using ggml-tiny.bin: 41 seconds.
  • Using ggml-small.bin: 4 minutes and 19 seconds.
  • Using ggml-medium.bin: 13 minutes and 5 seconds.

Usage Recommendations and Conclusion

For different hardware configurations, the following strategies are recommended:

• Users with dedicated graphics cards: Use the ggml-medium.bin model directly to balance efficiency and quality.
• Users with integrated graphics or older graphics cards:
  • Daily transcription: It is recommended to use ggml-small.bin, as the accuracy of ggml-tiny.bin is usually insufficient for general needs.
  • High-accuracy requirements: You can choose ggml-medium.bin, but allow for longer processing times.

Changelog

• 2025-03-24 Initial document created.
• 2026-01-31 Added recommendation link to the new Faster-Whisper solution.