Instructions to use TheAIchemist13/kannada_beekeeping_wav2vec2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheAIchemist13/kannada_beekeeping_wav2vec2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="TheAIchemist13/kannada_beekeeping_wav2vec2")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("TheAIchemist13/kannada_beekeeping_wav2vec2") model = AutoModelForCTC.from_pretrained("TheAIchemist13/kannada_beekeeping_wav2vec2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from TheAIchemist13/kannada_beekeeping_wav2vec2: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/TheAIchemist13/kannada_beekeeping_wav2vec2/resolve/main/training_args.bin
- Command line
-
hf download hf://TheAIchemist13/kannada_beekeeping_wav2vec2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/TheAIchemist13/kannada_beekeeping_wav2vec2/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 79968b6ff951d587c470540e6de4d3fe7223e2b690207020eb93ed950c8b7560
- Size of remote file:
- 4.09 kB
- SHA256:
- a3cf7d6c3f146b0d027fe6274cc2bc4f7a1294976f838fd72a6f8212c988d3ef
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