Instructions to use anton-l/wav2vec2-base-ft-common-language with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anton-l/wav2vec2-base-ft-common-language with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="anton-l/wav2vec2-base-ft-common-language")# Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("anton-l/wav2vec2-base-ft-common-language") model = AutoModelForAudioClassification.from_pretrained("anton-l/wav2vec2-base-ft-common-language", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from anton-l/wav2vec2-base-ft-common-language: direct link, hf CLI and curl.
- Browser
- Download file 2.93 kB
-
https://huggingface.co/anton-l/wav2vec2-base-ft-common-language/resolve/main/training_args.bin
- Command line
-
hf download hf://anton-l/wav2vec2-base-ft-common-language/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/anton-l/wav2vec2-base-ft-common-language/resolve/main/training_args.bin
2.93 kB
- Xet hash:
- c9d181fa17aea26ad16480414fbc485e46d5d3d1b4a7c32fb961f8304c66ca4f
- Size of remote file:
- 2.93 kB
- SHA256:
- bd2fb3401106a0f7db3743c809f26d9af23e060d44af8eed714585ebd6816bcb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.