readme: # Lingala Text-to-Speech
  
  This model was trained on the OpenSLR's 71.6 hours aligned lingala bible dataset.
  
  ## Model description
  
  A Conditional Variational Autoencoder with Adversarial Learning(VITS), which is an end-to-end approach to the text-to-speech task. To train the model, we used the espnet2 toolkit.
  
  
  ## Usage
  
  First install espnet2
  ``` sh
  pip install espnet
  ```
  Download the model and the config files from this repo.
  To generate a wav file using this model, run the following:
  ``` sh
  from espnet2.bin.tts_inference import Text2Speech
  import soundfile as sf
  
  text2speech = Text2Speech(train_config="config.yaml",model_file="train.total_count.best.pth")
  wav = text2speech("oyo kati na Ye ozwi lisiko mpe bolimbisi ya masumu")["wav"]
  sf.write("outfile.wav", wav.numpy(), text2speech.fs, "PCM_16")
  
  ```
  
  
  
  