Instructions to use esb/wav2vec2-aed-chime4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use esb/wav2vec2-aed-chime4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="esb/wav2vec2-aed-chime4")# Load model directly from transformers import AutoTokenizer, AutoModelForSpeechSeq2Seq tokenizer = AutoTokenizer.from_pretrained("esb/wav2vec2-aed-chime4") model = AutoModelForSpeechSeq2Seq.from_pretrained("esb/wav2vec2-aed-chime4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ff4d558acbc7d2f5edb14489ec93db198ebfc7d1e9fe3beb7cc87e257b41229
- Size of remote file:
- 2.35 GB
- SHA256:
- 0d27b7d96cf9acbcd3760f646a46c552df489af8e4c71d7a242d6fee7309c7fe
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