Instructions to use scasutt/wav2vec2-base_toy_train_data_augmented with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scasutt/wav2vec2-base_toy_train_data_augmented with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="scasutt/wav2vec2-base_toy_train_data_augmented")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("scasutt/wav2vec2-base_toy_train_data_augmented") model = AutoModelForCTC.from_pretrained("scasutt/wav2vec2-base_toy_train_data_augmented", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from scasutt/wav2vec2-base_toy_train_data_augmented: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/scasutt/wav2vec2-base_toy_train_data_augmented/resolve/main/training_args.bin
- Command line
-
hf download hf://scasutt/wav2vec2-base_toy_train_data_augmented/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/scasutt/wav2vec2-base_toy_train_data_augmented/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- 69fe2e9674ebaad4d8aae279d51aab8381059f591e25ee01924d1d55b3f6d10f
- Size of remote file:
- 3.06 kB
- SHA256:
- fe89d851c50774b90f9e45116cd28ec16f751eb95517bb5ae79cae020278b966
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