French STT v0.6
French STT v0.6 (commonvoice-fr)
Jump to section:
- Model details
- Intended use
- Performance Factors
- Metrics
- Training data
- Evaluation data
- Ethical considerations
- Caveats and recommendations
Model details
- Person or organization developing model: Originally trained and released by the commonvoice-fr project
- Model date: Accessed from Github on March 31, 2021
- Model type:
Speech-to-Text
- Model version:
v0.6
- Compatible with 🐸 STT version:
v0.9.3
- Code: commonvoice-fr
- License: MPL 2.0
- Citation details:
@misc{commonvoice-fr, author = {commonvoice-fr Contributors}, title = {Common Voice STT Model}, publisher = {Github}, journal = {GitHub repository}, howpublished = {\url{https://github.com/common-voice/commonvoice-fr/releases/tag/fr-v0.6}}, commit = {5a0f61baf112620286b30319eb7000c57d8a20d0} }
- Where to send questions or comments about the model: You can leave an issue on
STT-model
issues, open a new discussion onSTT-model
discussions, or chat with us on Gitter.
Intended use
Speech-to-Text for the French Language on 16kHz, mono-channel audio.
Performance Factors
Factors relevant to Speech-to-Text performance include but are not limited to speaker demographics, recording quality, and background noise. Read more about STT performance factors here.
Metrics
STT models are usually evaluated in terms of their transcription accuracy, deployment Real-Time Factor, and model size on disk.
Transcription Accuracy
The following Word Error Rates (WER) are reported on Github.
Test Corpus | WER | CER |
---|---|---|
African_Accented_French_test.csv | 44.9% | 24.2% |
M-AILABS | 9.7% | 2.7% |
trainingspeech | 20.0% | 6.0% |
Common Voice | 30.1% | 14.3% |
LinguaLibre | 5.9% | 1.8% |
CCPMF | 48.7% | 30.4% |
Real-Time Factor
Real-Time Factor (RTF) is defined as proccesing-time / length-of-audio
. The exact real-time factor of an STT model will depend on the hardware setup, so you may experience a different RTF.
Recorded average RTF on laptop CPU: ``
Model Size
model.pbmm
: 181M
model.tflite
: 46M
Approaches to uncertainty and variability
Confidence scores and multiple paths from the decoding beam can be used to measure model uncertainty and provide multiple, variable transcripts for any processed audio.
Training data
This French STT model was trained on the following corpora:
- Lingua Libre (~40h)
- Common Voice FR (v2) (~490h, en autorisant jusqu'à 32 duplicatas)
- Training Speech (~180h)
- African Accented French (~15h)
- M-AILABS French (~315h)
- Centre de Conférence Pierre Mendès France (~300h)
Total : ~1340h
Evaluation data
The model was tested on the following corpora.
- Lingua Libre
- Common Voice FR (v2)
- Training Speech
- African Accented French
- M-AILABS French
- Centre de Conférence Pierre Mendès France
Ethical considerations
Deploying a Speech-to-Text model into any production setting has ethical implications. You should consider these implications before use.
Demographic Bias
You should assume every machine learning model has demographic bias unless proven otherwise. For STT models, it is often the case that transcription accuracy is better for men than it is for women. If you are using this model in production, you should acknowledge this as a potential issue.
Surveillance
Speech-to-Text may be mis-used to invade the privacy of others by recording and mining information from private conversations. This kind of individual privacy is protected by law in may countries. You should not assume consent to record and analyze private speech.
Caveats and recommendations
Machine learning models (like this STT model) perform best on data that is similar to the data on which they were trained. Read about what to expect from an STT model with regard to your data here.
In most applications, it is recommended that you train your own language model to improve transcription accuracy on your speech data.