Instructions to use EducativeCS2023/whisper-en-tiny-trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EducativeCS2023/whisper-en-tiny-trained with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="EducativeCS2023/whisper-en-tiny-trained")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("EducativeCS2023/whisper-en-tiny-trained") model = AutoModelForSpeechSeq2Seq.from_pretrained("EducativeCS2023/whisper-en-tiny-trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from EducativeCS2023/whisper-en-tiny-trained: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/EducativeCS2023/whisper-en-tiny-trained/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://EducativeCS2023/whisper-en-tiny-trained/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/EducativeCS2023/whisper-en-tiny-trained/resolve/main/pytorch_model.bin
151 MB
- Xet hash:
- 7c15d43376773965375f6e94560df2b7e83576428eaf15b3b558ae69cfd71fde
- Size of remote file:
- 151 MB
- SHA256:
- 2ee387836fea489c51fd5f5687aa8350b539eef9ab4fed1436f3d1a1a5960619
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.