readme: ---
  language: "rw"
  thumbnail:
  pipeline_tag: automatic-speech-recognition
  tags:
  - Coqui
  - Deepspeech
  - LSTM
  license: "apache-2.0"
  datasets:
  - commonvoice
  metrics:
  - wer
  ---
  
  **Model card - Kinyarwanda coqui STT model**
  
  **Model details**
  - Kinyarwanda Speech to text model
  - Developed by [Digital Umuganda](digitalumuganda.com)
  - Model based from: Baidu Deepspeech end to end RNN model
  - paper: [deepspeech end to end STT](https://arxiv.org/pdf/1412.5567.pdf)
  - Documentation on model: [deepspeech documentation](https://deepspeech.readthedocs.io/)
  - License: Mozilla 2.0 License
  - Feedback on the model: samuel@digitalumuganda.com
  
  **Intended use cases**
  - Intended to be used for 
    - simple keyword spotting
    - simple transcribing
    - transfer learning for better kinyarwanda and african language models
  - Intended to be used by:
    - App developpers
    - various organizations who want to transcribe kinyarwanda recordings
    - ML researchers
    - other researchers in Kinyarwanda and tech usage in kinyarwanda (e.g. Linguists, journalists)
  - Not intended to be used as:
    - a fully fledged voice assistant
    - voice recognition application 
    - Multiple languages STT
    - language detection 
    
  **Factors**
  - Anti-bias: these are bias that can influence the accuracy of the model
    - Gender
    - accents and dialects
    - age
  - Voice quality: factors that can influence the accuracy of the model
    - Background noise
    - short sentences
  - Voice format: voices must be converted to the wav format
    - wav format
    
  **Metrics**
  - word error rate on the Common Voice Kinyarwanda test set
  
  |Test Corpus|WER|
  |-----------|---|
  |Common Voice|39.1\%|
  
  **Training data**
  - [common voice crowdsource website](https://commonvoice.mozilla.org/en/datasets)
  
  **Evaluation data**
  - [common voice crowdsource website](https://commonvoice.mozilla.org/en/datasets)
  