Instructions to use hf-tiny-model-private/tiny-random-Wav2Vec2ForXVector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Wav2Vec2ForXVector with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioXVector processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ForXVector") model = AutoModelForAudioXVector.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ForXVector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-Wav2Vec2ForXVector: direct link, hf CLI and curl.
- Browser
- Download file 184 kB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-Wav2Vec2ForXVector/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-Wav2Vec2ForXVector/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-Wav2Vec2ForXVector/resolve/main/pytorch_model.bin
184 kB
- Xet hash:
- 16059cbbfda684b3e89298831dda8fd59dfe19ea79340a5353680e116f285502
- Size of remote file:
- 184 kB
- SHA256:
- ef3404fc21ef1ffb9167dd4bf93a1e13104cd4b6ae6dacbd3adfa8917f5eb565
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.