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 training_args.bin from Giseok/wav2vec2-base-STTTest: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/Giseok/wav2vec2-base-STTTest/resolve/main/training_args.bin
- Command line
-
hf download hf://Giseok/wav2vec2-base-STTTest/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Giseok/wav2vec2-base-STTTest/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- e34b974f15a9af8fb48599bb7485c702776657c728087170180157e79425e571
- Size of remote file:
- 3.06 kB
- SHA256:
- ceb670fbf18e038ed8674b689ad3f7e9c36e1cee4a18b3171036f5dab98697fc
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