
**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)

{"language": "rw", "license": "apache-2.0", "tags": ["Coqui", "Deepspeech", "LSTM"], "datasets": ["commonvoice"], "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition"}
tflite Coqui Deepspeech LSTM automatic-speech-recognition rw dataset:commonvoice arxiv:1412.5567 license:apache-2.0 has_space region:us
TAGS
#tflite #Coqui #Deepspeech #LSTM #automatic-speech-recognition #rw #dataset-commonvoice #arxiv-1412.5567 #license-apache-2.0 #has_space #region-us 

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@URL


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



Training data


* common voice crowdsource website


Evaluation data


* common voice crowdsource website



TAGS
#tflite #Coqui #Deepspeech #LSTM #automatic-speech-recognition #rw #dataset-commonvoice #arxiv-1412.5567 #license-apache-2.0 #has_space #region-us 

passage: TAGS
#tflite #Coqui #Deepspeech #LSTM #automatic-speech-recognition #rw #dataset-commonvoice #arxiv-1412.5567 #license-apache-2.0 #has_space #region-us 
