Instructions to use scasutt/wav2vec2-large-xlsr-53_train_data_full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scasutt/wav2vec2-large-xlsr-53_train_data_full with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="scasutt/wav2vec2-large-xlsr-53_train_data_full")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("scasutt/wav2vec2-large-xlsr-53_train_data_full") model = AutoModelForCTC.from_pretrained("scasutt/wav2vec2-large-xlsr-53_train_data_full", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from scasutt/wav2vec2-large-xlsr-53_train_data_full: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/scasutt/wav2vec2-large-xlsr-53_train_data_full/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://scasutt/wav2vec2-large-xlsr-53_train_data_full/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/scasutt/wav2vec2-large-xlsr-53_train_data_full/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- d1321af5a83c9ff7329af02e916d949013b8fc3a28d9f8aa9110b5d3d13d08c1
- Size of remote file:
- 1.26 GB
- SHA256:
- 1984113080c6b691336c4417793adc9d5222a59bb7ee2b4ef2a10d72c471fb46
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