Instructions to use scasutt/wav2vec2-large-xlsr-53_final_train1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scasutt/wav2vec2-large-xlsr-53_final_train1 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_final_train1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("scasutt/wav2vec2-large-xlsr-53_final_train1") model = AutoModelForCTC.from_pretrained("scasutt/wav2vec2-large-xlsr-53_final_train1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from scasutt/wav2vec2-large-xlsr-53_final_train1: direct link, hf CLI and curl.
- Browser
- Download file 1.26 GB
-
https://huggingface.co/scasutt/wav2vec2-large-xlsr-53_final_train1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://scasutt/wav2vec2-large-xlsr-53_final_train1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/scasutt/wav2vec2-large-xlsr-53_final_train1/resolve/main/pytorch_model.bin
1.26 GB
- Xet hash:
- db27cdb9251292fdb020dde90fa94fe2e8eef56d184382fb6cb78b03a1477aa4
- Size of remote file:
- 1.26 GB
- SHA256:
- fec13620ac62c0d4daa41b5b2e44b11d632109fa7b4ee9bed09bd1469e1b13ec
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