Instructions to use esc-bench/wav2vec2-aed-switchboard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esc-bench/wav2vec2-aed-switchboard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esc-bench/wav2vec2-aed-switchboard")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("esc-bench/wav2vec2-aed-switchboard") model = AutoModelForMultimodalLM.from_pretrained("esc-bench/wav2vec2-aed-switchboard") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e40b2a6c451af190f686188ca3d3646a00958eed76c3aab29ee8778acca6c3f4
- Size of remote file:
- 2.35 GB
- SHA256:
- 6c72e104d929cedc81e7ffa251a898e16118db4d9a341b89be22415f5db74cdd
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