


## 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 best_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)


{"language": ["rw"], "pipeline_tag": "text-to-speech"}
transformers text-to-speech rw arxiv:2112.02418 endpoints_compatible region:us
TAGS
#transformers #text-to-speech #rw #arxiv-2112.02418 #endpoints_compatible #region-us 




## Model Description


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



- Audio data: [URL, version -> Common Language Version audio Old Testament]
- Text data: [URL, version -> Bibiliya Ijambo ry'imana(BIR)(only the Old Testament was used)]

# Usage


Install the Coqui's TTS library:

Download the files from this repo, then run: 


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


[1] YourTTS paper


## Model Description


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



- Audio data: [URL, version -> Common Language Version audio Old Testament]
- Text data: [URL, version -> Bibiliya Ijambo ry'imana(BIR)(only the Old Testament was used)] # Usage


Install the Coqui's TTS library:

Download the files from this repo, then run: 


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


[1] YourTTS paper
TAGS
#transformers #text-to-speech #rw #arxiv-2112.02418 #endpoints_compatible #region-us 
 ## Model Description


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



- Audio data: [URL, version -> Common Language Version audio Old Testament]
- Text data: [URL, version -> Bibiliya Ijambo ry'imana(BIR)(only the Old Testament was used)] # Usage


Install the Coqui's TTS library:

Download the files from this repo, then run: 


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


[1] YourTTS paper
TAGS
#transformers #text-to-speech #rw #arxiv-2112.02418 #endpoints_compatible #region-us 
## Model Description


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



- Audio data: [URL, version -> Common Language Version audio Old Testament]
- Text data: [URL, version -> Bibiliya Ijambo ry'imana(BIR)(only the Old Testament was used)]# Usage


Install the Coqui's TTS library:

Download the files from this repo, then run: 


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


[1] YourTTS paper