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Cross-Lingual-Voice-Cloning

DISCLAIMER :- Based on the paper Learning to Speak Fluently in a Foreign Language:Multilingual Speech Synthesis and Cross-Language Voice Cloning
Differences from original fork:

• cleaned tensorflow requirement;
• added russian language support;
• various code improvements

The model needs to be provided 2 text files 1 for the purpose of training and 1 for validation. Each line of the txt file should follow the following format :-

<path-to-wav-file>|<text-corresponding-to-speech-in-wav>|<speaker-no>|<lang-no>

<speaker-no> goes from 0 to n-1, where n is the number of speakers.

<lang-no> goes from 0 to m-1 , where m is the number of languages.
Lnaguage-id table:

• en : 0
• ru : 1

hparams.training_files, hparams.validation_files need to be set to the path to the txt files of previous section.

hparams.n_speakers, hparams.dim_yo need to be changed to the number of speakers.

hparams.n_langs must be set to number of languages.

To change the languages, add/remove unicode characters in _letters variable of text/symbols.py .

For inference using the model , run clvc-infer-gh.ipynb with appropriate speaker and language number.