Instructions to use hsali/wav2vec2_testing2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hsali/wav2vec2_testing2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, Wav2Vec2ForSpeechClassification processor = AutoProcessor.from_pretrained("hsali/wav2vec2_testing2") model = Wav2Vec2ForSpeechClassification.from_pretrained("hsali/wav2vec2_testing2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hsali/wav2vec2_testing2: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/hsali/wav2vec2_testing2/resolve/main/training_args.bin
- Command line
-
hf download hf://hsali/wav2vec2_testing2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hsali/wav2vec2_testing2/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- 66666cc7c212af744b4e12afa251b21530f91ead94749172c594b8eda4d86b86
- Size of remote file:
- 3.52 kB
- SHA256:
- 7df39633fb4d05b385abb019c123fa5175a296b84d49d943b844cae18c00014e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.