Instructions to use scasutt/wav2vec2-base_toy_train_double_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scasutt/wav2vec2-base_toy_train_double_data 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_double_data")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("scasutt/wav2vec2-base_toy_train_double_data") model = AutoModelForCTC.from_pretrained("scasutt/wav2vec2-base_toy_train_double_data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from scasutt/wav2vec2-base_toy_train_double_data: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/scasutt/wav2vec2-base_toy_train_double_data/resolve/main/training_args.bin
- Command line
-
hf download hf://scasutt/wav2vec2-base_toy_train_double_data/training_args.bin
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curl -L -o training_args.bin https://huggingface.co/scasutt/wav2vec2-base_toy_train_double_data/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- 18738db86bd540bb221b25f90f825c9a59fce4e70a8e3289d94d52eda2055f84
- Size of remote file:
- 3.06 kB
- SHA256:
- 755c5c414cc699e8e8a6f5f86f716902daf7838cb3c83ec30c4e9d3f32e1072a
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