Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Chinese
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use seiching/whisper-medium-seiching with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seiching/whisper-medium-seiching with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="seiching/whisper-medium-seiching")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("seiching/whisper-medium-seiching") model = AutoModelForSpeechSeq2Seq.from_pretrained("seiching/whisper-medium-seiching") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3f074072c071354ba61b6ac042571af3963ab6c1d60fa824b40067a67e6eb89c
- Size of remote file:
- 4.16 kB
- SHA256:
- c0a95e05a99e2f7aff969d18d6117d8111101485d849dff90f22d9f55019a2bd
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