Instructions to use dima806/english_accents_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/english_accents_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="dima806/english_accents_classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("dima806/english_accents_classification") model = AutoModelForAudioClassification.from_pretrained("dima806/english_accents_classification") - Notebooks
- Google Colab
- Kaggle
Returns common English accent given a voice audio sample.
See https://www.kaggle.com/code/dima806/common-voice-accent-classification for more details.
Classification report:
precision recall f1-score support
us 0.3956 0.0150 0.0290 4788
england 0.5255 0.9121 0.6668 18082
indian 0.5883 0.4586 0.5154 5656
australia 0.4962 0.0381 0.0707 5124
canada 0.3714 0.1760 0.2389 5169
accuracy 0.5220 38819
macro avg 0.4754 0.3200 0.3042 38819
weighted avg 0.4942 0.5220 0.4304 38819
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