Secure and scalable speech transcription for local and HPC

Pipeline

Bernabeu, P. (2025) — Version 1.0.0 [Computer software]. Zenodo software

Abstract

Cloud-based speech-to-text services are convenient, but they often have file size limits, lack transparency for reproducible research and can pose privacy risks under regulations such as the General Data Protection Regulation (GDPR). This production-ready workflow runs OpenAI's Whisper models locally, so audio and transcripts stay on the user's own infrastructure. It scales to batch jobs on high-performance computing (HPC) clusters with GPU acceleration. For quality control and privacy, it can enhance the audio, attribute speech to speakers (diarisation), remove spurious repetitions produced by Whisper and mask personal names with context-aware filtering. Built around a single Python script, the workflow offers a reproducible and secure alternative for academic and enterprise transcription.

How it works

graph TD A["Audio input"] --> B["Audio enhancement pipeline"] B --> C["Whisper transcription
with GPU acceleration"] H["Batch operations
on HPC clusters"] --> C C --> D["Remove AI-generated
repetitions"] C --> E["Context-aware name masking"] C --> F["Speaker diarisation"] D --> G["Reproducible,
privacy-preserving transcript"] E --> G F --> G

Reference

If you use this workflow in your research, please cite:

Bernabeu, P. (2025). Secure and scalable speech transcription for local and HPC (Version 1.0.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.17624830

The recommended BibTeX entry is:

@misc{secure_local_HPC_speech_transcription,
  author    = {Bernabeu, Pablo},
  title     = {Secure and scalable speech transcription for local and {HPC}},
  year      = {2025},
  publisher = {Zenodo},
  version   = {1.0.0},
  doi       = {10.5281/zenodo.17624830},
  url       = {https://doi.org/10.5281/zenodo.17624830}
}

Podcast

Created using NotebookLM, with all the benefits and blind spots of human editing.

Related references

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software speech-to-text speech recognition transcription Whisper machine learning huggingface Python OpenAI natural language processing audio processing GDPR data protection data privacy high-performance computing privacy

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