Instructions to use Giseok/wav2vec2-base-STTTest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Giseok/wav2vec2-base-STTTest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Giseok/wav2vec2-base-STTTest")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Giseok/wav2vec2-base-STTTest") model = AutoModelForCTC.from_pretrained("Giseok/wav2vec2-base-STTTest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Giseok/wav2vec2-base-STTTest: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/Giseok/wav2vec2-base-STTTest/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Giseok/wav2vec2-base-STTTest/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Giseok/wav2vec2-base-STTTest/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 479f414abdefd36d67274d21b4d26890a69283907e5f7214a7e950b407700116
- Size of remote file:
- 378 MB
- SHA256:
- 53363b5430a0c2a42d2ca0a87805ebff9ac3c28a3cf5efc132821a5ed6c845aa
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