Instructions to use mgtoxd/wav2vec2test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mgtoxd/wav2vec2test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mgtoxd/wav2vec2test")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("mgtoxd/wav2vec2test") model = AutoModelForCTC.from_pretrained("mgtoxd/wav2vec2test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mgtoxd/wav2vec2test: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/mgtoxd/wav2vec2test/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mgtoxd/wav2vec2test/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mgtoxd/wav2vec2test/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 15e4a40d9de667a9ee2c74fec02f149d2b73480a87e0e92c35a36d3940d48583
- Size of remote file:
- 378 MB
- SHA256:
- 52826559d27e5d9d5c3ecd6df99e4c44ecc1e042b9f57f232348fb2baf71a6ec
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