Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
whisper
Generated from Trainer
Instructions to use tensorboy/whisper-tiny-hi2_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tensorboy/whisper-tiny-hi2_test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="tensorboy/whisper-tiny-hi2_test")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("tensorboy/whisper-tiny-hi2_test") model = AutoModelForSpeechSeq2Seq.from_pretrained("tensorboy/whisper-tiny-hi2_test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f3e1893aadbd40910d7e71d897432aafb1ecc6f1acae5fd00d291f80d23f7ce
- Size of remote file:
- 5.24 kB
- SHA256:
- d9287bb38beb16bf58664c3ed10f735c1bd05dd62bb178cf34a56cf97c52b277
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.