Instructions to use scasutt/wav2vec2-base_toy_train_data_only_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_only_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_only_augmented")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("scasutt/wav2vec2-base_toy_train_data_only_augmented") model = AutoModelForCTC.from_pretrained("scasutt/wav2vec2-base_toy_train_data_only_augmented", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from scasutt/wav2vec2-base_toy_train_data_only_augmented: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/scasutt/wav2vec2-base_toy_train_data_only_augmented/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://scasutt/wav2vec2-base_toy_train_data_only_augmented/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/scasutt/wav2vec2-base_toy_train_data_only_augmented/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 79b4bfb371e0465b74e788c859dd70c53b394772a7785ad93a4bd658e9f0010e
- Size of remote file:
- 378 MB
- SHA256:
- 257023b50b4f2a2473733dd08f66d15efeb8300bb5b26cd73f25baae8f7bc762
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.