Instructions to use facebook/wav2vec2-large-robust-ft-swbd-300h with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/wav2vec2-large-robust-ft-swbd-300h with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-robust-ft-swbd-300h")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("facebook/wav2vec2-large-robust-ft-swbd-300h") model = AutoModelForCTC.from_pretrained("facebook/wav2vec2-large-robust-ft-swbd-300h", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4bf6ee682454417e6744281cfd4a07b9b32162351e7595bc463d0b347db95b4d
- Size of remote file:
- 1.26 GB
- SHA256:
- 8260c8f7f45e030013fcac8468d14646061d38d7b3cc614c3d8490b04e5d11eb
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