Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Eval Results
Instructions to use openai/whisper-base.en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-base.en with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-base.en") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-base.en", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from openai/whisper-base.en: direct link, hf CLI and curl.
- Browser
- Download file 290 MB
-
https://huggingface.co/openai/whisper-base.en/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://openai/whisper-base.en/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/openai/whisper-base.en/resolve/main/pytorch_model.bin
290 MB
- Xet hash:
- e556c2188dee55ee065dc67c1e2e76d1b3a0be3dd7a69dfd1f76bbda36aac94b
- Size of remote file:
- 290 MB
- SHA256:
- 404fa07b37813e1a425ae22348db6f7d0b359f72acd8d457927cc0d029beb278
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.