Instructions to use FidelOdok/doa_model_TL4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FidelOdok/doa_model_TL4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="FidelOdok/doa_model_TL4")# pip install -U transformers accelerate # Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("FidelOdok/doa_model_TL4") model = AutoModelForAudioClassification.from_pretrained("FidelOdok/doa_model_TL4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.45ffd0c
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This is the model card for a Sound Localisation Model estimating the direction of arrival (DOA) from stational resound sources. The model was trained and evaluated using a SOFA dataset. This model was fine-tuned using the AST Model. (MIT/ast-finetuned-audioset-10-10-0.4593). Developed by Fidelis Odok (University of Hertfordshire)714e5cb -
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