Instructions to use hf-tiny-model-private/tiny-random-Wav2Vec2ConformerForCTC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-Wav2Vec2ConformerForCTC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="hf-tiny-model-private/tiny-random-Wav2Vec2ConformerForCTC")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ConformerForCTC") model = AutoModelForCTC.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ConformerForCTC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93cb6dc5060f80b6e4e3aec0822311af856320bc6627ee80114316e0b72c74fb
- Size of remote file:
- 168 kB
- SHA256:
- b75c29cd0dafea042983f254a97ce664ded5b5cb90525b07af746ab49c1fe930
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