Model Card for LatAm Accent Determination
Wav2Vec2 Model to classify audio based on the accent of the speaker as Puerto Rican, Colombian, Venezuelan, Peruvian, or Chilean
Table of Contents
Model Details
Model Description
Wav2Vec2 Model to classify audio based on the accent of the speaker as Puerto Rican, Colombian, Venezuelan, Peruvian, or Chilean
- Developed by: Henry Savich
- Shared by [Optional]: Henry Savich
- Model type: Language model
- Language(s) (NLP): es
- License: openrail
- Parent Model: Wav2Vec2 Base
- Resources for more information:
Uses
Direct Use
Classify an audio clip as Puerto Rican, Peruvian, Venezuelan, Colombian, or Chilean Spanish
Out-of-Scope Use
The model was trained on speakers reciting pre-chosen sentences, thus it does not reflect any knowledge of lexical differences between dialects.
Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g.,
Sheng et al. (2021) and
Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
Training Details
Training Data
OpenSLR 71,72,73,74,75,76
Training Procedure
Preprocessing
Data was Train-Test split on speakers, so as to prevent the model from achieving high test accuracy by matching voices.
Speeds, Sizes, Times
Trained on ~3000 5-second audio clips, Training is lightwegiht taking < 1 hr on using Google Colaboratory Premium GPUs
Evaluation
Testing Data, Factors & Metrics
Testing Data
Factors
Audio Quality - training and testing data was higher quality than can be expected from found audio
Metrics
Accuracy
Results
~85% depending on random train-test split
Model Examination
Even splitting on speakers, our model achieves excellent accuracy on the testing set. This is interesting because it indicates that accent classification, at least at this granularity, is an easier task than voice identification, which could have just as easily met the training objective.
The confusion matrix shows that Basque is the most easily distinguished, which should be expecting as it is the only language that isn't Spanish. Puerto Rican was the hardest to identify in the testing set, but I think this is more having to do with PR having the least data moreso than something about the accent itself.
I think if this same size of dataset was used for this same experiment, but there were more speakers (and so not as much fitting on individual voices), we could expect near perfect accuracy.
Technical Specifications
Model Architecture and Objective
Wav2Vec2
Compute Infrastructure
Google Colaboratory Pro+
Hardware
Google Colaboratory Pro+ Premium GPUS
Software
Pytorch via huggingface
Model Card Authors
Henry Savich
Model Card Contact
