Instructions to use afaaaak/whisper-tiny-ps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use afaaaak/whisper-tiny-ps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="afaaaak/whisper-tiny-ps")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("afaaaak/whisper-tiny-ps") model = AutoModelForSpeechSeq2Seq.from_pretrained("afaaaak/whisper-tiny-ps", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 89f822c234afa63bf05caac5019f424714e7f5fba6d07380d1afccf48991e6ff
- Size of remote file:
- 5.91 kB
- SHA256:
- c7a69c27e482f0c49ba6bb550a00115ab5bae0f71cf0fe01dd22bfa25c19ff93
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.