Czech STT v0.3.0

Czech STT v0.3.0

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: Trained by Vojtěch Drábek.
• Model language: Czech / čeština / cs
• Model date: May 31, 2022
• Model type: Speech-to-Text
• Model version: v0.3.0
• Compatible with 🐸 STT version: v0.9.3
• License: CC-BY-NC 4.0
• Citation details: @misc{czech-stt, author = {Drábek, Vojtěch}, title = {Czech STT 0.3}, publisher = {comodoro}, journal = {deepspeech-cs}, howpublished = {\url{https://github.com/comodoro/deepspeech-cs}} }
• Where to send questions or comments about the model: You can leave an issue on the model release page or STT-model issues, open a new discussion on STT-model discussions, or chat with us on Gitter or Matrix channel coqui-ai/STT.

Intended use

Speech-to-Text for the Czech 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

More information reported on Github.

Test Corpus	WER	CER
Acoustic model
Czech Common voice 6.1	40.6%	10.7%
Vystadial 2016	50.6%	19.6%
Parliament Plenary Hearings	21.3%	5.3%
ParCzech 3.0	21%	6.2%
With the attached scorer
Czech Common voice 6.1	15.3%	6.8%
Vystadial 2016	35.7%	20.1%
Parliament Plenary Hearings	9.7%	3.7%
ParCzech 3.0	10.1%	4.5%

Real-Time Factor

Real-Time Factor (RTF) is defined as processing-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: 0.73

Model Size

model.tflite: 46M
scorer: 461M

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 model was trained on the following corpora:

1. Vystadial 2016 – Czech data
2. OVM – Otázky Václava Moravce
3. Czech Parliament Meetings
4. Large Corpus of Czech Parliament Plenary Hearings
5. Common Voice Czech
6. Some private recordings and parts of audiobooks

Evaluation data

The model was evaluated on Common Voice Czech, Large Corpus of Czech Parliament Plenary Hearings, Vystadial 2016 – Czech data and ParCzech 3.0 test sets.

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 many 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.