A free to use, offline working, high quality german TTS voice should be available for every project without any license struggling.
I contribute my voice as a person believing in a world where all people are equal. No matter of gender, sexual orientation, religion, skin color and geocoordinates of birth location. A global world where everybody is warmly welcome on any place on this planet and open and free knowledge and education is available to everyone. 🌍 (Thorsten Müller)
Please keep in mind, that i am no professional voice talent. I'm just a normal guy sharing his voice with the world.
Feel free to contact me on social media 🤗.
| Platform | Link |
|---|---|
| Youtube | ThorstenVoice on Youtube |
| Thorsten Müller on LinkedIn | |
| ThorstenVoice on Twitter | |
| Huggingface | ThorstenVoice on Huggingface |
| ThorstenVoice on Instagram |
All my "Thorsten-Voice" datasets are listed and downloadable on Zenodo. Qoutation is highly appreciated in case you use them in your projects, products or papers.
| Dataset | DOI Link |
|---|---|
| Thorsten-Voice Dataset 2021.02 (Neutral) |  |
| Thorsten-Voice Dataset 2021.06 (Emotional) |  |
| Thorsten-Voice Dataset 2022.10 (Neutral) |  |
| Thorsten-Voice Dataset 2023.09 (Hessisch) |  |
@dataset{muller_2021_5525342,
author = {Müller, Thorsten and
Kreutz, Dominik},
title = {Thorsten-Voice Dataset 2021.02},
month = sep,
year = 2021,
note = {{Please use it to make the world a better place for
whole humankind.}},
publisher = {Zenodo},
version = {3.0},
doi = {10.5281/zenodo.5525342},
url = {https://doi.org/10.5281/zenodo.5525342}
}
- Recorded by Thorsten Müller
- Optimized by Dominik Kreutz
- LJSpeech file and directory structure
- 22.668 recorded phrases (wav files)
- More than 23 hours of pure audio
- Samplerate 22.050Hz
- Mono
- Normalized to -24dB
- Phrase length (min/avg/max): 2 / 52 / 180 chars
- No silence at beginning/ending
- Avg spoken chars per second: 14
- Sentences with question mark: 2.780
- Sentences with exclamation mark: 1.840
As described in the PDF document (evolution of thorsten dataset) this dataset consists of three recording phases.
- Phase 1: Recorded with a cheap usb microphone (low quality)
- Phase 2: Recorded with a good microphone (good quality)
- Phase 3: Recorded with same good microphone but longer phrases (> 100 chars) (good quality)
If you want to use a dataset subset you can see which files belong to which recording phase in recording quality csv file.
@dataset{muller_2021_5525023,
author = {Müller, Thorsten and
Kreutz, Dominik},
title = {Thorsten-Voice Dataset 2021.06 emotional},
month = sep,
year = 2021,
note = {{Please use it to make the world a better place for
whole humankind.}},
publisher = {Zenodo},
version = {2.0},
doi = {10.5281/zenodo.5525023},
url = {https://doi.org/10.5281/zenodo.5525023}
}
All emotional recordings where recorded by myself and i tried to feel and pronounce that emotion even if the phrase context does not match that emotion. Example: I pronounced the sleepy recordings in the tone i have shortly before falling asleep.
- Recorded by Thorsten Müller
- Optimized by Dominik Kreutz
- 300 sentences * 8 emotions = 2.400 recordings
- Mono
- Samplerate 22.050Hz
- Normalized to -24dB
- No silence at beginning/ending
- Sentence length: 59 - 148 chars
🗣️ Listen to some audio recordings from this dataset here.
@dataset{muller_2022_7265581,
author = {Müller, Thorsten and
Kreutz, Dominik},
title = {Thorsten-Voice Dataset 2022.10},
month = nov,
year = 2022,
publisher = {Zenodo},
version = {1.0},
doi = {10.5281/zenodo.7265581},
url = {https://doi.org/10.5281/zenodo.7265581}
}
@dataset{muller_2024_10511260,
author = {Müller, Thorsten and
Kreutz, Dominik},
title = {Thorsten-Voice Dataset 2023.09 Hessisch},
month = jan,
year = 2024,
publisher = {Zenodo},
doi = {10.5281/zenodo.10511260},
url = {https://doi.org/10.5281/zenodo.10511260}
}
Based on these opensource voice datasets several TTS (text to speech) models have been trained using AI / machine learning technology.
There are multiple german models available trained and used by by the projects Coqui AI, Piper TTS and Home Assistant. You can find more information on how to use them, audio samples and video tutorials on the Thorsten-Voice project website.
Listen to audio samples and installation / usage instructions here (🇩🇪):
In addition Silero, Monatis and ZDisket used my voice datasets for model training too. More samples and details can be found on Silero Thorsten-Voice audio samples. See this colab notebook for more details.
ZDisket made a tool called TensorVox for setting up an TTS environment on Windows and included a german TTS model trained by monatis. Thanks for sharing that. See it in action on Youtube.
If you like my voice contribution and would like to support my effort for an opensource voice technology future, you can support me, if you like:
- Subscribe and share my Thorsten-Voice youtube channel and follow me on my social media profiles
- Buy me a tea using Ko-Fi or Github sponsor
I want to say thank you to great people who supported me on this journey with nice words, support and compute power: Thanks El-Tocino, Eren Gölge, Gras64, Kris Gesling, Nmstoker, Othiele, Repodiac, SanjaESC, Synesthesiam.
Special thanks to my dear colleague, Sebastian Kraus, for supporting me with audio recording equipment and for being the creative mastermind behind the logo design and of course to the dear Dominik (@domcross) for him being so close by my side on this amazing journey.
On my Thorsten-Voice youtube channel you can find step by step (cooking recipes) tutorial on opensource voice technology. If you're interested i'd be happy to welcome you as new subscriber on my wonderful youtube community.TS** on my little .
I really like to talk about the importance of an opensource voice technology future. If you would like me to be a speaker on a conference or event i'd happy to be contacted using the Thorsten-Voice website contact form. See some of my speaker references on Thorsten-Voice website.
