Instructions to use voidful/hubert-tiny-v2-unit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use voidful/hubert-tiny-v2-unit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="voidful/hubert-tiny-v2-unit")# Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("voidful/hubert-tiny-v2-unit") model = AutoModelForCTC.from_pretrained("voidful/hubert-tiny-v2-unit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7fa9dcccb56607ba91b4b364b4357b3adeed5d840aef2fe0a43cb89a774bf623
- Size of remote file:
- 559 Bytes
- SHA256:
- f5f029cec310163a8fd335bec37058ace9c8c9aa1da1205fd4faa98a6a9b0e61
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