Model card for Yolox贸chitl Mixtec STT
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- 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 by Joe Meyer.
- Model language: Yolox贸chitl Mixtec / /
xty - Model date: April 17, 2022
- Model type:
Speech-to-Text - Model version:
v0.1.0 - Compatible with 馃惛 STT version:
v1.0.0 - License: CC BY-NC-SA 3.0
- Citation details:
@techreport{xty-stt, author = {Meyer,Joe}, title = {Yolox贸chitl Mixtec STT 0.1}, institution = {Coqui}, address = {\url{https://github.com/coqui-ai/STT-models}} year = {2022}, month = {April}, number = {STT-SLR89-XTY-0.1} } - Where to send questions or comments about the model: You can leave an issue on
STT-modelissues, open a new discussion onSTT-modeldiscussions, or chat with us on Gitter.
Intended use
Speech-to-Text for the Yolox贸chitl Mixtec 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 and Character Error Rates are reported for a modified data set from OpenSLR SLR89. The official validated.tsv had rows removed which had errors processing, and the data was re-processed by Cmmon Voice Utils to convert to 16kHz mono-channel PCM .wav files.
| Test Corpus | WER | CER |
|---|---|---|
| OpenSLR | 48.85% | 18.04% |
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: ``
Model Size
model.pbmm: M
model.tflite: M
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 a modified data set from OpenSLR SLR89. The official validated.tsv had rows removed which had errors processing, and the data was re-processed by Cmmon Voice Utils to convert to 16kHz mono-channel PCM .wav files.
Evaluation data
This model was evaluated on a modified data set from OpenSLR SLR89. The official validated.tsv had rows removed which had errors processing, and the data was re-processed by Cmmon Voice Utils to convert to 16kHz mono-channel PCM .wav files.
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.