Tortoise-TTS-de

Tortoise TTS AR model fine-tuned for German

Trained on 3 speakers; 2 LibriVox readers, and Thorsten Mueller's dataset https://github.com/thorstenMueller/Thorsten-Voice

THE NEWEST VERSIONS: v# indicates the number of training sessions, #e is how many epochs. 9/5 training session uploaded

Requires the tokenizer file placed in the tokenizers/ directory

Voice latents are pre-computed in voices/ for some uploaded versions. Voice samples to recompute latents are uploaded.

For use in MRQ Voice Cloning WebUI:

Requires the tokenizer used in training, and code changes to disable text cleaners. At minimum, change english_cleaners to basic_cleaners.

Code changes: modules\tortoise-tts\tortoise\utils\tokenizer.py Change Line 201: txt = english_cleaners(txt) and replace it with txt = basic_cleaners(txt)

modules\tortoise-tts\build\lib\tortoise\utils\tokenizer.py Change Line 201: txt = english_cleaners(txt) and replace it with txt = basic_cleaners(txt)

\modules\dlas\dlas\data\audio\paired_voice_audio_dataset.py Line 133: return text_to_sequence(txt, ['english_cleaners']) and replace it with: return text_to_sequence(txt, ['basic_cleaners'])

modules\dlas\dlas\data\audio\voice_tokenizer.py Line 14: from dlas.models.audio.tts.tacotron2.text.cleaners import english_cleaners to: from dlas.models.audio.tts.tacotron2.text.cleaners import english_cleaners, basic_cleaners Line 85: txt = english_cleaners(txt) to txt = basic_cleaners(txt) Line 134: word = english_cleaners(word) to basic_cleaners(word)

Copy and paste German text into the tokenizer tester on the utilities tab, and you should see it tokenized with all of the special characters, and no [UNK].

license: other language:

• de

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