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 training_args.bin from scasutt/wav2vec2-large-xlsr-53_final_train1: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/scasutt/wav2vec2-large-xlsr-53_final_train1/resolve/main/training_args.bin
- Command line
-
hf download hf://scasutt/wav2vec2-large-xlsr-53_final_train1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/scasutt/wav2vec2-large-xlsr-53_final_train1/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- 2df6f33231eace36d997784af10de03e7c336094e85a7ff15426f89d8bf11e11
- Size of remote file:
- 3.06 kB
- SHA256:
- 177f44ee265b774c7a80ec5b4d1d3d5d5d88c5dfcdcbf6a309b55de1ceeeff00
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