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 tf_model.h5 from openai/whisper-base: direct link, hf CLI and curl.
- Browser
- Download file 291 MB
-
https://huggingface.co/openai/whisper-base/resolve/main/tf_model.h5
- Command line
-
hf download hf://openai/whisper-base/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/openai/whisper-base/resolve/main/tf_model.h5
291 MB
- Xet hash:
- 7c1d32a4b02029a3c4b66dba2470c2990507e98430985e2c999804d5156df28f
- Size of remote file:
- 291 MB
- SHA256:
- a997bb84b79970c89a3b5dd6188b18c1e0f1814cfecefbe40039ed052361129b
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