Instructions to use scasutt/wav2vec2-large-xlsr-53_toy_train_data_augment_0.1.csv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scasutt/wav2vec2-large-xlsr-53_toy_train_data_augment_0.1.csv with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="scasutt/wav2vec2-large-xlsr-53_toy_train_data_augment_0.1.csv")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("scasutt/wav2vec2-large-xlsr-53_toy_train_data_augment_0.1.csv") model = AutoModelForCTC.from_pretrained("scasutt/wav2vec2-large-xlsr-53_toy_train_data_augment_0.1.csv") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- faa131ef61088518f90136b6702e71e72e0f86d60871e9ce61c291a139ece63e
- Size of remote file:
- 3.06 kB
- SHA256:
- 3bd646f5e8a06d1649d7a89329b5fb61e37a2ce22544e2023a7637ae8f030f4f
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