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:
- 269dcf9e8b8a10ed564a6e5ead790e639075267944ce655972257e3f7aad3de1
- Size of remote file:
- 203 kB
- SHA256:
- 29427491008806f5793a133b8d51f6c36812fe8720e2a6f76c5f45c9e078c0c2
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