Create README.md

README.md

@@ -0,0 +1,29 @@
+# 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")
+
+```
+
+
+