Model Description

<!-- Provide a longer summary of what this model is. --> This model is an end-to-end deep-learning-based Kinyarwanda Text-to-Speech (TTS). Due to its zero-shot learning capabilities, new voices can be introduced with 1min speech. The model was trained using the Coqui's TTS library, and the YourTTS[1] architecture. It was trained on 67 hours of Kinyarwanda bible data, for 100 epochs.

Data Sources

<!-- Provide the basic links for the model. -->

• Audio data: [www.faithcomesbyhearing.com, version -> Common Language Version audio Old Testament]
• Text data: [www.bible.com, version -> Bibiliya Ijambo ry'imana(BIR)(only the Old Testament was used)]

Usage

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> Install the Coqui's TTS library:

pip install git+https://github.com/coqui-ai/TTS@0910cb76bcd85df56bf43654bb31427647cdfd0d#egg=TTS

Download the files from this repo, then run:

tts --text "text" --model_path model.pth --encoder_path SE_checkpoint.pth.tar --encoder_config_path config_se.json --config_path config.json --speakers_file_path speakers.pth --speaker_wav conditioning_audio.wav --out_path out.wav

Where the conditioning audio is a wav file(s) to condition a multi-speaker TTS model with a Speaker Encoder, you can give multiple file paths. The d_vectors is computed as their average.

References

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information should go in this section. --> [1] YourTTS paper