Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use openai/whisper-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use openai/whisper-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from openai/whisper-base: direct link, hf CLI and curl.
- Browser
- Download file 290 MB
-
https://huggingface.co/openai/whisper-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://openai/whisper-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/openai/whisper-base/resolve/main/pytorch_model.bin
290 MB
- Xet hash:
- 6a4eade82604c687caa88ffd7e813101e1735459ef915f818bfc85ad22300b01
- Size of remote file:
- 290 MB
- SHA256:
- c37f294c9563a49217d108687fbd3cda5bdf0441a451512d87bf66343ab31a87
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