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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A GUI tool for offline transcription of speech recordings, including speaker diarization, utilizing state-of-the-art machine learning models.
| Date | Stars |
|---|---|
| 2026-07-31 | 1190 |
| 2026-08-06 | 1193 |
Today
+3 stars today
This week
— stars this week
This month
— stars this month
Momentum
12.0
growth rate 0.00%/day
<img src="https://github.com/BANDAS-Center/aTrain/blob/main/docs/images/logo.svg" width="300" alt="Logo">
## Accessible Transcription of Interviews
aTrain is a tool for automatically transcribing speech recordings utilizing state-of-the-art machine learning models without uploading any data. It was developed by researchers at the Business Analytics and Data Science-Center at the University of Graz and tested by researchers from the Know-Center Graz.
## Get aTrain
<p>
<a href="https://flathub.org/apps/io.github.juergenfleiss.aTrain">
<img height="58" alt="Get it on Flathub" src="https://flathub.org/api/badge?locale=en"></a>
<a href="https://apps.microsoft.com/detail/9N15Q44SZNS2?mode=direct">
<img width="220" alt="Get it from Microsoft" src="https://get.microsoft.com/images/en-us%20dark.svg"></a>
</p>
aTrain is published on Flathub for Linux and the Microsoft Store for Windows. Additional download types [can be found here](https://business-analytics.uni-graz.at/de/forschung/atrain/download/).
## About aTrain
aTrain offers the following benefits:
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**Fast and accurate 🚀**
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aTrain provides a user friendly access to the [faster-whisper](https://github.com/guillaumekln/faster-whisper) implementation of OpenAI’s [Whisper model](https://github.com/openai/whisper), ensuring best in class transcription quality (see [Wollin-Geiring et al. 2023](https://www.static.tu.berlin/fileadmin/www/10005401/Publikationen_sos/Wollin-Giering_et_al_2023_Automatic_transcription.pdf)) paired with higher speeds on your local computer. Transcription when selecting the highest-quality model takes only around three times the audio length on current mobile CPUs typically found in middle-class business notebooks (e.g., Core i5 12th Gen, Ryzen Series 6000).
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**Speaker detection 🗣️**
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aTrain has a speaker detection mode based on [pyannote.audio](https://github.com/pyannote/pyannote-audio) and can analyze each text segment to determine which speaker it belongs to.
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**Privacy Preservation and GDPR compliance 🔒**
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aTrain processes the provided speech recordings completely offline on your own device and does not send recordings or transcriptions to the internet. This helps researchers to maintain data privacy requirements arising from ethical guidelines or to comply with legal requirements such as the GDPR.
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**Multi-language support 🌍**
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aTrain-core can process speech recordings a total of 99 languages, including Afrikaans, Arabic, Armenian, Azerbaijani, Belarusian, Bosnian, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Greek, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Kannada, Kazakh, Korean, Latvian, Lithuanian, Macedonian, Malay, Marathi, Maori, Nepali, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tagalog, Tamil, Thai, Turkish, Ukrainian, Urdu, Vietnamese, and Welsh. A full list can be found [here](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py). Note that transcription quality varies with language; word error rates for the different languages can be found [here](https://github.com/openai/whisper?tab=readme-ov-file#available-models-and-languages).
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**MAXQDA, ATLAS.ti and nVivo compatible output 📄**
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aTrain-core provides transcription files that are seamlessly importable into the most popular tools for qualitative analysis, ATLAS.ti, MAXQDA and nVivo. This allows you to directly play audio for the corresponding text segment by clicking on its timestamp. Go to the [tutorial](https://github.com/BANDAS-Center/aTrain/wiki/Tutorials) for MAXQDA.
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**Nvidia GPU support 🖥️**
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aTrain can either run on the CPU or an NVIDIA GPU (CUDA toolkit installation required). A [CUDA-enabled NVIDIA GPU](https://developer.nvidia.com/cuda-gpus) significantly improves the speed of transcriptions and Excerpt of 8,319 characters
Read on GitHub
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Jürgen Fleiß · University of Graz · Austria
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Ikko Eltociear Ashimine · Japan
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4d9d1650592bdcca, desc:transcription, desc:speaker diarization