Instructions to use hf-tiny-model-private/tiny-random-Wav2Vec2ForCTC 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-Wav2Vec2ForCTC 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-Wav2Vec2ForCTC")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ForCTC") model = AutoModelForCTC.from_pretrained("hf-tiny-model-private/tiny-random-Wav2Vec2ForCTC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 435c06d2ddc81e79568e92ccfa48fcfb12c58ca19e67690016165018749d9613
- Size of remote file:
- 135 kB
- SHA256:
- ffd2c41410a52b7495dc4f58f05c3fe3cd43d6ca9926a066f4344bf6f1de6d95
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