Instructions to use dima806/multiple_accent_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/multiple_accent_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="dima806/multiple_accent_classification")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("dima806/multiple_accent_classification") model = AutoModelForAudioClassification.from_pretrained("dima806/multiple_accent_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0400637cd892b0ae50c863a15a3a18c17d013562c377ff7e0f17be4ae4d5743c
- Size of remote file:
- 378 MB
- SHA256:
- debad56d172408abcf00087e6986ee5c665909706f2f0197bb7381d890926d4a
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