Instructions to use vipul22/whisper-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vipul22/whisper-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="vipul22/whisper-tiny")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("vipul22/whisper-tiny") model = AutoModelForSpeechSeq2Seq.from_pretrained("vipul22/whisper-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dbd6b8acf9c90e0280320cb66f601bd5a554e4e5d61b2e2357ccc5a41d03d1ec
- Size of remote file:
- 3.71 kB
- SHA256:
- 7d87b6df914a1bafe49caf02848f836c4fefe3ed16bac13237c1c98906970115
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