Instructions to use esc-benchmark/wav2vec2-ctc-tedlium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-benchmark/wav2vec2-ctc-tedlium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-benchmark/wav2vec2-ctc-tedlium")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("esc-benchmark/wav2vec2-ctc-tedlium") model = AutoModelForCTC.from_pretrained("esc-benchmark/wav2vec2-ctc-tedlium") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 557d4d5d0b39424d6b1d00159d6c8f0e715a6c40300cb64752889c162cd35548
- Size of remote file:
- 1.26 GB
- SHA256:
- fcfeb02537a755eff6e041ef612a85e67c80ccc473dfe55d781b92d643f6d724
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