metadata
language: rw
thumbnail: null
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
- Model based from: Baidu Deepspeech end to end RNN model
- paper: deepspeech end to end STT
- Documentation on model: deepspeech documentation
- 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
Evaluation data