Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
data2vec-audio
Generated from Trainer
Eval Results (legacy)
Instructions to use jjyaoao/Echotune_clean_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jjyaoao/Echotune_clean_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="jjyaoao/Echotune_clean_test")# Load model directly from transformers import AutoTokenizer, AutoModelForCTC tokenizer = AutoTokenizer.from_pretrained("jjyaoao/Echotune_clean_test") model = AutoModelForCTC.from_pretrained("jjyaoao/Echotune_clean_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jjyaoao/Echotune_clean_test: direct link, hf CLI and curl.
- Browser
- Download file 373 MB
-
https://huggingface.co/jjyaoao/Echotune_clean_test/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jjyaoao/Echotune_clean_test/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jjyaoao/Echotune_clean_test/resolve/main/pytorch_model.bin
373 MB
- Xet hash:
- 320d8a2cfd169ffa4711c61fc3552ed7d8b82f76e8a831ef99a514e4ea0c03a8
- Size of remote file:
- 373 MB
- SHA256:
- 8e7d6a7ee367672c7cd1fbbf1cf319d5da0880c0538fded586fd61e146357a29
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