Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
wav2vec2
Generated from Trainer
Eval Results (legacy)
Instructions to use aconeil/w2v2-lmk_augmented with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aconeil/w2v2-lmk_augmented with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="aconeil/w2v2-lmk_augmented")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("aconeil/w2v2-lmk_augmented") model = AutoModelForCTC.from_pretrained("aconeil/w2v2-lmk_augmented", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from aconeil/w2v2-lmk_augmented: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/aconeil/w2v2-lmk_augmented/resolve/main/training_args.bin
- Command line
-
hf download hf://aconeil/w2v2-lmk_augmented/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/aconeil/w2v2-lmk_augmented/resolve/main/training_args.bin
5.84 kB
- Xet hash:
- 78dd3c70ebacc079aa7e24e0494f9cc3bb8ff524f669004d1b6e687e38b5107a
- Size of remote file:
- 5.84 kB
- SHA256:
- 1afb6833fdd33be04efb19ce81e6d81248c43ba6cf23790cc7e06f0fd8a56681
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