Instructions to use Brokette/wav2vec2-base-timit-test5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Brokette/wav2vec2-base-timit-test5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Brokette/wav2vec2-base-timit-test5")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("Brokette/wav2vec2-base-timit-test5") model = AutoModelForCTC.from_pretrained("Brokette/wav2vec2-base-timit-test5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e69425eae3d850662c75c8b0bac5e7bb6710da551dbce31f02d7108c435dea66
- Size of remote file:
- 3.06 kB
- SHA256:
- 526de82a47072ff2a59adb20c9752790bc0591b5f00c54d21abd96ccfe9630dc
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