Instructions to use hf-tiny-model-private/tiny-random-WavLMForCTC 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-WavLMForCTC 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-WavLMForCTC")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-WavLMForCTC") model = AutoModelForCTC.from_pretrained("hf-tiny-model-private/tiny-random-WavLMForCTC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b4a5ff30cc220b399c9a4d94044584bdf8b4fa3c5d50a50461b695d1bba4f0a7
- Size of remote file:
- 123 kB
- SHA256:
- 13037edb05c5f1010f61c06311b4adc87c4b547acc2c7aeef46bd1ddef2527ed
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