Instructions to use esc-benchmark/wav2vec2-aed-tedlium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-benchmark/wav2vec2-aed-tedlium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-benchmark/wav2vec2-aed-tedlium")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("esc-benchmark/wav2vec2-aed-tedlium") model = AutoModelForSpeechSeq2Seq.from_pretrained("esc-benchmark/wav2vec2-aed-tedlium") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d2f93539706e7b94ca98a351b7b2266408597b42d17c28761422f844c8c881ca
- Size of remote file:
- 2.35 GB
- SHA256:
- 4dff7590ad263bc52a238d3c7b2d76cb572915f2f12deb3ee64046fdd196b438
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