Instructions to use patrickvonplaten/wav2vec2_tiny_random_robust with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use patrickvonplaten/wav2vec2_tiny_random_robust with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="patrickvonplaten/wav2vec2_tiny_random_robust")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2_tiny_random_robust") model = AutoModel.from_pretrained("patrickvonplaten/wav2vec2_tiny_random_robust", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from patrickvonplaten/wav2vec2_tiny_random_robust: direct link, hf CLI and curl.
- Browser
- Download file 1.23 MB
-
https://huggingface.co/patrickvonplaten/wav2vec2_tiny_random_robust/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://patrickvonplaten/wav2vec2_tiny_random_robust/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/patrickvonplaten/wav2vec2_tiny_random_robust/resolve/main/pytorch_model.bin
1.23 MB
- Xet hash:
- 354373741588b48a43aa3c65a1aea84c2512e0497f8ac9aa0f3dfdf23e32ac01
- Size of remote file:
- 1.23 MB
- SHA256:
- 40bff8e06f2ac9a5931ed60695d028ef72168eab7933427900a90ffb6ec76a03
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