Thorsten-Voice: A free to use, offline working, high quality german TTS voice should be available for every project without any license struggling.
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Thorsten - Open German Voice Dataset

Introduction to "Thorsten-Voice" 🗣️ 💬 🦥

A free to use, offline working, high quality german TTS voice should be available for every project without any license struggling.

CC-0 license Maintaner follow on Twitter Open Source Audio comparison page

A summary of my open german voice dataset is available on Wikipedia

https://de.wikipedia.org/wiki/Thorsten_(Stimme) 😃

Speaking tech devices and voice based smart assistants are very popular ourdays. But for providing nice sounding TTS lot of projects depend on big tech cloud services for synthezing voice. While quality is quite good, there remain critical aspects like privacy concerns and missing offline availablitiy.

True, but what is this all about

I want to (hopefully) fill that german TTS gap and make the most personal contribution i can give.
I contribute my personal voice! 💚

This contribution is split into three parts:

  • "Thorsten" neutral dataset
  • "Thorsten" emotional dataset
  • Pretrained TTS models based on "Thorsten" dataset

Please read some personal words before using dataset / TTS models

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. 🌍

So hopefully my voice is used in this manner to make this world a better place for all of us 😃.

tl;dr Please don't use for evil!

Datasets

For both datasets please keep in mind, that i am no professional voice talent. I'm just a normal guy sharing his voice with you.

Dataset "Thorsten" neutral

Samples of my neutral voice

To get an impression what my voice sounds to decide if it fits to your project i published some sample recordings, so no need to download complete dataset first.

Dataset information 🎤

  • ljspeech-1.1 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

text length vs. mean audio duration text length vs. median audio duration text length vs. STD text length vs. number instances signal noise ratio bokeh

Dataset evolution

As described in the pdf document (evolution of thorsten dataset) this dataset consists of three recording phases.

  • phase1: Recorded with a cheap usb microphone
  • phase2: Recorded with a good microphone
  • phase3: Recorded with same good microphone but longer phrases (> 100 chars)

If you wanna use just a dataset subset (phase1 and/or phase2 and/or phase3) you can see which files belong to which recording phase in recording quality csv file.

Neutral dataset download information

Download size: 2,7GB

Version Description Date Link
thorsten-de-v01 Initial version 2020-06-28 Google Drive Download v01
thorsten-de-v02 Normalized to -24dB and split metadata.csv into shuffeled metadata_train.csv and metadata_val.csv 2020-08-22 Google Drive Download v02
thorsten-de-v03 Based on v02 dataset, but with increased speed by 10% (using ffmpeg atempo=1.1). 2021-02-10 Google Drive Download v03

Dataset "Thorsten" emotional

Emotional dataset information and samples 🎤

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.

  • 300 sentences * 8 emotions = 2.400 recordings
  • recorded by Thorsten Müller (optimized by Dominik Kreutz)
  • mono
  • samplerate 22.050Hz
  • normalized to -24dB
  • no silence at beginning/ending
  • sentence length: 59 - 148 chars

Btw. i mentioned, that i'm no professional voice talent, did i?

"Mist, wieder nichts geschafft."

Emotion Minutes Sample
Neutral 🙂 19 min. neutral sample
Disgusted 🤢 23 min. disgusted sample
Angry 😠 20 min. angry sample
Amused 😀 18 min. amused sample
Surprised 😲 18 min. surprised sample
Sleepy 😔 30 min. sleepy sample
Drunk (i was "not" drunk while recording!) 😵 25 min. drunk sample
Whispering 🤫 22 min. whispering sample

Emotional dataset download information

Download size: 350MB

Version Description Date Link
thorsten-de-emotional-v01 Initial version 2021-04-03 Google Drive Download v01
thorsten-de-emotional-v02 Added emotions "drunk" and "whispering" version 2021-06-13 Google Drive Download v02

Pretrained TTS models

If you trained a model on "Thorsten" dataset please file an issue with some information on it. Sharing a trained model is highly appreciated.

My personal training sessions are based on TTS repo code (originally initiated by Mozilla) and now maintained through https://www.coqui.ai (🐸)

Coqui models

Easy pip install

For all "Thorsten" coqui models i recommend setting up a virtual environment (venv).

  • mkdir ThorstenVoice
  • cd ThorstenVoice
  • python3 -m venv .
  • source ./bin/activate
  • pip install -U pip
  • pip install -U tts
  • tts --list

tts-server --model_name tts_models/de/thorsten/tacotron2-DCA

or

tts-server --model_name tts_models/de/thorsten/tacotron2-DCA --vocoder_name vocoder_models/universal/libri-tts/fullband-melgan

Check details here: > see: https://github.com/coqui-ai/TTS/releases/tag/v0.0.11

Download Coqui trained checkpoints / config

Model name Coqui Repo branch / commit Release date Google Drive Download Link
Thorsten Tacotron2 DCA master / 0ee3eeefb553678d56c49534f3972a426a254649 2021-04-02 Google Drive Thorsten Taco2 DCA
Thorsten Vocoder WaveGrad master / 0ee3eeefb553678d56c49534f3972a426a254649 2021-04-02 Google Drive Thorsten Vocoder WaveGrad
Thorsten Vocoder Fullband-MelGAN master / 0ee3eeefb553678d56c49534f3972a426a254649 training-in-progress training-in-progress
Thorsten Vocoder HifiGAN planned planned
Thorsten Vocoder WaveRNN planned planned

Silero-models

You can use a free A-GPL licensed models trained on this dataset via the silero-models project. The full list of models including their older version is available via this yaml file.

Speaker Gender Language Examples Colab
thorsten_8khz m de 8000 / 16000 Open In Colab
thorsten_16khz m de 8000 / 16000 Open In Colab

Public talks

I really want to bring the topic "OpenVoice" to a bigger public attention, so i am happy to be invited as a speaker on that.

I have been part of a Linux User Group podcast about Mycroft AI and talked on my TTS efforts on that in May 2021. I'll publish a link to that talk when it's released to the public.

In addition to that i was invited by Yusuf from Turkish tensorflow community to talk on "How to make machines speak with your own voice" on june 2nd, 2021. 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

Whenever i've something about open voice in mind what i like to share my thoughts on i post a video on Youtube.

Feel free to file an issue if you ...

  • have improvements on dataset
  • 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

Recommended projects

Special thanks

I want to thank all open source communities for providing great projects.

Just to name some nice guys who joined me on this TTS roadtrip:

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 thank you to a special guy who supported me on this journey right from the beginning. Not just with nice words, but with his time, audio optimization knowhow and finally his gpu computing power.

Without his amazing support this dataset (in it's current way) would not exists.

Thank you Dominik (@domcross / https://github.com/domcross/)

Additional links

We'll hear us in future 🗣️

Thorsten (https://twitter.com/ThorstenVoice)