Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
hubert
Generated from Trainer
Eval Results (legacy)
Instructions to use kksukk/hubert_zeroth_gpu_scratch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kksukk/hubert_zeroth_gpu_scratch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="kksukk/hubert_zeroth_gpu_scratch")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("kksukk/hubert_zeroth_gpu_scratch") model = AutoModelForCTC.from_pretrained("kksukk/hubert_zeroth_gpu_scratch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kksukk/hubert_zeroth_gpu_scratch: direct link, hf CLI and curl.
- Browser
- Download file 3.39 kB
-
https://huggingface.co/kksukk/hubert_zeroth_gpu_scratch/resolve/main/training_args.bin
- Command line
-
hf download hf://kksukk/hubert_zeroth_gpu_scratch/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kksukk/hubert_zeroth_gpu_scratch/resolve/main/training_args.bin
3.39 kB
- Xet hash:
- 33e70ed5c7fbc17182d10e4fb24f3a2d8304c2659e8276d950c4d502d386b084
- Size of remote file:
- 3.39 kB
- SHA256:
- a8901c91189a7470e40d14e1b05b8f6e5be762c3e36aef29bd9433bfc84faee8
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