Instructions to use Gabtni/model-wav2vec with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gabtni/model-wav2vec with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Gabtni/model-wav2vec")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Gabtni/model-wav2vec") model = AutoModelForCTC.from_pretrained("Gabtni/model-wav2vec", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Gabtni/model-wav2vec: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/Gabtni/model-wav2vec/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Gabtni/model-wav2vec/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Gabtni/model-wav2vec/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- af854be89dd045145569ca0993c14c66b096e029866b626e4bcd1808ae86026e
- Size of remote file:
- 378 MB
- SHA256:
- 76101f2f49c8047c0d5fa71ebfb48495577f52c9b1d37091c1f92b5e88ab8374
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