| .github | ||
| docs | ||
| helperScripts | ||
| samples/thorsten-21.06-emotional | ||
| CITATION.cff | ||
| EvolutionOfThorstenDataset.pdf | ||
| LICENSE | ||
| Logo_Thorsten-Voice-kleiner.jpg | ||
| Logo_Thorsten-Voice.png | ||
| README.md | ||
| RecordingQuality.csv | ||
Motivation for Thorsten-Voice project 🗣️ 💬
A free to use, offline working, high quality german TTS voice should be available for every project without any license struggling.
Some personal words before using Thorsten-Voice
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.
Voice-Datasets
Voice datasets are listed on Zenodo:
| Dataset | DOI Link |
|---|---|
| Thorsten-21.02-neutral |  |
| Thorsten-21.06-emotional |  |
| Thorsten-22.05-neutral | soon to come |
Thorsten-21.02-neutral
@dataset{muller_thorsten_2021_5525342,
author = {Müller, Thorsten and
Kreutz, Dominik},
title = {Thorsten-Voice - "Thorsten-21.02-neutral" Dataset},
month = feb,
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}
}
🗣️ Listen to some audio recordings from this dataset here.
Dataset summary
- 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
Dataset evolution
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.
Thorsten-21.06-emotional
@dataset{muller_thorsten_2021_5525023,
author = {Müller, Thorsten and
Kreutz, Dominik},
title = {{Thorsten-Voice - "Thorsten-21.06-emotional"
Dataset}},
month = jun,
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.
Samples
Listen to the phrase "Mist, wieder nichts geschafft." in following emotions.
Dataset summary
- 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
Thorsten-22.05-neutral
🗣️ Listen to some audio recordings from this dataset here.
Soon to come
TTS Models
Thorsten-21.04-Tacotron2-DCA
This TTS-model has been trained on Thorsten-21.02-neutral dataset. The recommended trained Fullband-MelGAN Vocoder can be downloaded here.
Run the model:
- pip install TTS==0.5.0
- tts-server --model_name tts_models/de/thorsten/tacotron2-DCA
Thorsten-22.05-VITS
Trained on dataset Thorsten-22.05-neutral.
TODO
Thorsten-22.05-Tacotron2-DDC
Trained on dataset Thorsten-22.05-neutral.
🗣️ Listen to synthesized samples here.
Soon to come.
Other models
Silero
You can use a free A-GPL licensed models trained on Thorsten-21.02-neutral dataset via the silero-models project.
ZDisket
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.
Public talks
I really want to bring the topic "Open Voice For An Open Future" to a bigger public attention.
- I've been part of a Linux User Group podcast about Mycroft AI and talked on my TTS efforts on that in (May 2021).
- I was invited by Yusuf from Turkish tensorflow community to talk on "How to make machines speak with your own voice". This talk has been streamed live on Youtube and is available here. If you're interested on the showed slides, feel free to download my presentation here (June 2021) )
- I've been invited as speaker on VoiceLunch language & linguistics on 03.01.2022. Here are my slides (January 2022).
Youtube channel
In summer 2021 i've started to share my lessons learned and experiences on open voice tech, in special TTS on my little Youtube channel. If you check out and like my videos i'd happy to welcome you as subscriber and member of my little Youtube community.
Feel free to file an issue if you ...
- Use my TTS voice in your project(s)
- Want to share your trained "Thorsten" model
- Get to know about any abuse usage of my voice
Thanks section
Cool projects
Cool people
Even more special people
Additionally, a really nice thanks for my dear colleague, Sebastian Kraus, for supporting me with audio recording equipment and for being the creative mastermind behind the logo design.
And last but not least i want to say a huge, huge thank you to a special guy who supported me on this journey as a partner right from the beginning. Not just with nice words, but with his time, audio optimization knowhow and finally GPU power.
Thank you so much, dear Dominik (@domcross) for being my partner on this journey.
Thorsten (Twitter: @ThorstenVoice)