Create README.md

README.md

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+---
+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)