{title}
Link to documentation on mfa-models
Jump to section:
- Model details
- Intended use
- Performance Factors
- Metrics
- Training data
- Ethical considerations
- Troubleshooting issues
Model details
- Maintainer: {maintainer}
- Language: {language_link}
- Model type:
Language model - Architecture:
{architecture} - Model version:
v{version} - Trained date:
{date} - Compatible MFA version:
v{mfa_version} - License: {license_link}
- Citation:
{citation}
- If you have comments or questions about this model, you can check previous MFA model discussion posts or create a new one.
Installation
Install from the MFA command line:
mfa model download language_model {model_name}
Or download from the release page.
Intended use
This model is intended for very basic language modeling {language_link} transcripts.
These ngram models are far from ideal and trained on the same corpus as the acoustic models, and are provided only for completeness and in the off chance that they're useful in bootstrapping corpus development.
This language model trained with words from the pronunciation dictionaries above.
Performance Factors
MFA language model archives contain the main large ngram model, along with two pruned versions that are used in initial decoding for performance reasons, and then later the large model is used to rescore. If the initial decoding with the small version is causing perfomance issues, you can train a new language model with more aggressive pruning.
You should also consider training a language model on your own domain, as that will be much more representative and useful to use in decoding.
Metrics
The model was trained on 90% of the dictionary and evaluated on a held-out 10% and evaluated with utterance error rate and character error rate of tokenized transcripts.
Training data
This model was trained on the following data set:
{training_details}
Evaluation
This model was evaluated on the following data set:
{evaluation_details}
Ethical considerations
Deploying any model involving language 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 this tokenizer, this model was trained on a very specific subset of {language} at the time it was collected that will typically not represent spontaneous speech in the current time. Do not use this model in production, but if you do so, you should acknowledge bias as a potential issue.
Surveillance
Speech-to-Text technologies may be misused 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.