Instructions to use AKulk/wav2vec2-base-timit-epochs15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AKulk/wav2vec2-base-timit-epochs15 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="AKulk/wav2vec2-base-timit-epochs15")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("AKulk/wav2vec2-base-timit-epochs15") model = AutoModelForCTC.from_pretrained("AKulk/wav2vec2-base-timit-epochs15", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 819d253593bc11e83f51a45edf596fcedaa7a62b5a672adc5aa0680a307ea726
- Size of remote file:
- 1.26 GB
- SHA256:
- 04285208d953f841c1f316415703bfd22845fe0192a0b96b165adfad45fb8629
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