Audio Classification
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use iamhack/fb_DH_o_m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iamhack/fb_DH_o_m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="iamhack/fb_DH_o_m")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("iamhack/fb_DH_o_m") model = AutoModelForAudioClassification.from_pretrained("iamhack/fb_DH_o_m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from iamhack/fb_DH_o_m: direct link, hf CLI and curl.
- Browser
- Download file 4.22 kB
-
https://huggingface.co/iamhack/fb_DH_o_m/resolve/main/training_args.bin
- Command line
-
hf download hf://iamhack/fb_DH_o_m/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/iamhack/fb_DH_o_m/resolve/main/training_args.bin
4.22 kB
- Xet hash:
- de48128faf0e27201293312641232caace42c5c948ea66673454b293c9b600d4
- Size of remote file:
- 4.22 kB
- SHA256:
- 0edb52a6f921cb4186565da0691105c9cb4ac16ad0b1fd1c55adc006a19ebe11
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