Automatic Speech Recognition
Transformers
PyTorch
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use GCYY/whisper-tiny-asr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GCYY/whisper-tiny-asr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="GCYY/whisper-tiny-asr")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("GCYY/whisper-tiny-asr") model = AutoModelForSpeechSeq2Seq.from_pretrained("GCYY/whisper-tiny-asr") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 516b4eccf1cc4b423a65e968dfabd7f974016ccca53b27e5970d5e7ada3a7545
- Size of remote file:
- 151 MB
- SHA256:
- ff866aeabe8a0ca8802ed38b9d9a574aa28dfb46443abd34e524d134fd6a181d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.