Instructions to use dima806/speech-accent-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/speech-accent-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="dima806/speech-accent-classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("dima806/speech-accent-classification") model = AutoModelForAudioClassification.from_pretrained("dima806/speech-accent-classification") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| metrics: | |
| - accuracy | |
| language: | |
| - en | |
| pipeline_tag: audio-classification | |
| base_model: | |
| - facebook/wav2vec2-base-960h | |
| See the https://dima806.substack.com/p/diving-deep-into-speech-accent-classification and https://www.kaggle.com/code/dima806/speech-accent-classification for more details | |
| --- | |
| license: apache-2.0 | |
| metrics: | |
| - accuracy | |
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