Instructions to use scasutt/wav2vec2-base_toy_train_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_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_data")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("scasutt/wav2vec2-base_toy_train_data") model = AutoModelForCTC.from_pretrained("scasutt/wav2vec2-base_toy_train_data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from scasutt/wav2vec2-base_toy_train_data: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/scasutt/wav2vec2-base_toy_train_data/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://scasutt/wav2vec2-base_toy_train_data/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/scasutt/wav2vec2-base_toy_train_data/resolve/main/pytorch_model.bin
378 MB
- Xet hash:
- 89c136b057ea12587faee4845fb219a7ab159b2b9c924443c581da47ee57aca6
- Size of remote file:
- 378 MB
- SHA256:
- 186eec423e727cc8f61682380ec43b909d63641e06dbfec8138806b2792f5c5f
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