Instructions to use rkmt/wav2vec2-base-timit-demo-colab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rkmt/wav2vec2-base-timit-demo-colab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="rkmt/wav2vec2-base-timit-demo-colab")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("rkmt/wav2vec2-base-timit-demo-colab") model = AutoModelForCTC.from_pretrained("rkmt/wav2vec2-base-timit-demo-colab") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- df54e9695a99fcba84484cd26866c8b7c8c68ec33a557dc32847bf5dd45442fe
- Size of remote file:
- 2.93 kB
- SHA256:
- 070dd7f9f857e80b5fe27d28815577b2a14535c6256e5acf3d7283c1b44b2ee2
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