readme: ---
  language:
  - rw
  pipeline_tag: text-to-speech
  ---
  
  
  
  ## 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 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](https://arxiv.org/pdf/2112.02418.pdf)
  