Automatic Speech Recognition
Transformers
PyTorch
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use sandychoii/whisper-tiny-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sandychoii/whisper-tiny-en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sandychoii/whisper-tiny-en")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("sandychoii/whisper-tiny-en") model = AutoModelForSpeechSeq2Seq.from_pretrained("sandychoii/whisper-tiny-en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sandychoii/whisper-tiny-en: direct link, hf CLI and curl.
- Browser
- Download file 151 MB
-
https://huggingface.co/sandychoii/whisper-tiny-en/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sandychoii/whisper-tiny-en/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sandychoii/whisper-tiny-en/resolve/main/pytorch_model.bin
151 MB
- Xet hash:
- 05001eee29973b8f7dbd6243cb401f822780ae805697cfea6025fb6c2e097014
- Size of remote file:
- 151 MB
- SHA256:
- 0496f2f93467711ee26e33e763c892557dcc027b4e7b9c5c35f8ff25fa89722f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.