Instructions to use esc-benchmark/wav2vec2-ctc-chime4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-benchmark/wav2vec2-ctc-chime4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-benchmark/wav2vec2-ctc-chime4")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("esc-benchmark/wav2vec2-ctc-chime4") model = AutoModelForCTC.from_pretrained("esc-benchmark/wav2vec2-ctc-chime4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f2ac8a54a6e610e9f7270c16248df6aea531d2e1cccbd2515aa781282b76bf0
- Size of remote file:
- 1.26 GB
- SHA256:
- 9ef1365c9caeb52881f97d11cd6735634801189df8ef812f375a63ef75e5cd8a
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