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--- |
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language: |
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- rw |
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pipeline_tag: text-to-speech |
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license: cc |
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tags: |
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- TTS |
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- Kinyarwanda |
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- Text to speech |
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--- |
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## Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This model is an end-to-end deep-learning-based Kinyarwanda Text-to-Speech (TTS). The model was trained using the Coqui's TTS library, and the YourTTS[1] architecture. |
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# Usage |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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Install the Coqui's TTS library: |
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``` |
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pip install git+https://github.com/coqui-ai/TTS@0910cb76bcd85df56bf43654bb31427647cdfd0d#egg=TTS |
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``` |
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Download the files from this repo, then run: |
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``` |
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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 |
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``` |
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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. |
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# References |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information should go in this section. --> |
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[1] [YourTTS paper](https://arxiv.org/pdf/2112.02418.pdf) |
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[2] [Kinyarwanda TTS: Using a multi-speaker dataset to build a Kinyarwanda TTS model](https://openreview.net/pdf?id=1gLgrqWnHF) |
