Audio Classification
Transformers
PyTorch
TensorBoard
Safetensors
audio-spectrogram-transformer
Generated from Trainer
Eval Results (legacy)
Instructions to use moonseok/wav2vec_final_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moonseok/wav2vec_final_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="moonseok/wav2vec_final_output")# pip install -U transformers accelerate # Load model directly from transformers import AutoFeatureExtractor, AutoModelForAudioClassification extractor = AutoFeatureExtractor.from_pretrained("moonseok/wav2vec_final_output") model = AutoModelForAudioClassification.from_pretrained("moonseok/wav2vec_final_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from moonseok/wav2vec_final_output: direct link, hf CLI and curl.
- Browser
- Download file 345 MB
-
https://huggingface.co/moonseok/wav2vec_final_output/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://moonseok/wav2vec_final_output/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/moonseok/wav2vec_final_output/resolve/main/pytorch_model.bin
345 MB
- Xet hash:
- c1fe24e2c49c0b94a625571ca2cae0a433a8bc8c7f5fe323a59c5613311ae4bb
- Size of remote file:
- 345 MB
- SHA256:
- 77ceb088ca8c561df0aa19c43b98d9f41b319c79f350ba2ce92d52791fa3e943
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