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
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Download README.md from FidelOdok/doa_model_TL4: direct link, hf CLI and curl.
- Browser
- Download file 639 Bytes
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https://huggingface.co/FidelOdok/doa_model_TL4/resolve/main/README.md
- Command line
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hf download hf://FidelOdok/doa_model_TL4/README.md
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curl -L -o README.md https://huggingface.co/FidelOdok/doa_model_TL4/resolve/main/README.md
639 Bytes
metadata
license: creativeml-openrail-m
datasets:
- FidelOdok/SOFA_DOA_10_deg
language:
- en
metrics:
- f1
library_name: transformers
pipeline_tag: audio-classification
Model Description
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)
- Developed by: Fidelis Odok
- Finetuned from model [optional]: (MIT/ast-finetuned-audioset-10-10-0.4593)