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:
- 5ac77d617fbbef2d485006512848f81a778f6250dabd1f40a818fcf4f94c73dd
- Size of remote file:
- 1.26 GB
- SHA256:
- cd78ce9bfdb6668d5f4b8eaee5afd2fc27c44f53cc889449f2b57708fed0d284
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