Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
data2vec-audio
Generated from Trainer
Eval Results (legacy)
Instructions to use bASILgIL/wav2vec_5e-5_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bASILgIL/wav2vec_5e-5_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bASILgIL/wav2vec_5e-5_3")# Load model directly from transformers import AutoTokenizer, AutoModelForCTC tokenizer = AutoTokenizer.from_pretrained("bASILgIL/wav2vec_5e-5_3") model = AutoModelForCTC.from_pretrained("bASILgIL/wav2vec_5e-5_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- db53090063ab205b96f4c9f2ccd5ea3fdcd1848f56e072a527795d958e71ea23
- Size of remote file:
- 5.37 kB
- SHA256:
- 021e4df114b98094f71417a48052e92821fa17bacedf6af5be70fad389db71b8
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