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 training_args.bin from scasutt/wav2vec2-base_toy_train_data_only_augmented: direct link, hf CLI and curl.
- Browser
- Download file 3.12 kB
-
https://huggingface.co/scasutt/wav2vec2-base_toy_train_data_only_augmented/resolve/main/training_args.bin
- Command line
-
hf download hf://scasutt/wav2vec2-base_toy_train_data_only_augmented/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/scasutt/wav2vec2-base_toy_train_data_only_augmented/resolve/main/training_args.bin
3.12 kB
- Xet hash:
- e2debc4ae8d626d9bd0c8eda6e3f4274ed4d021174bb566972bf25af5490ffb4
- Size of remote file:
- 3.12 kB
- SHA256:
- 6eb0a86cfd1afe90013c1201eef942da88927b4b4d94ba68c6b6f31a82be486d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.