| # 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") | |
| ``` | |