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")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("openai/whisper-base") model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d627bee77e9ea994f08fe5a000542be15e7acfbd97a1a3183369ac3069c8efee
- Size of remote file:
- 290 MB
- SHA256:
- f69b3fcc5b0044b1436bc9e4181548709129977871877b875bddfaf1b41fc667
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