Instructions to use namkyeong/batch_test_wav2vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use namkyeong/batch_test_wav2vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="namkyeong/batch_test_wav2vec2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("namkyeong/batch_test_wav2vec2") model = AutoModelForCTC.from_pretrained("namkyeong/batch_test_wav2vec2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from namkyeong/batch_test_wav2vec2: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/namkyeong/batch_test_wav2vec2/resolve/main/training_args.bin
- Command line
-
hf download hf://namkyeong/batch_test_wav2vec2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/namkyeong/batch_test_wav2vec2/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- e1de40ffc5e3db0b051b06076e17630f633cbc8b40bc72159e3cb215a651f916
- Size of remote file:
- 3.06 kB
- SHA256:
- 31c15b9fed92d87df4c865511458a93cb7164e6a55392447e6894efa2387b4e8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.