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
Adding ONNX file of this model
#19
by iAJChang - opened
- model.onnx +3 -0
model.onnx
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version https://git-lfs.github.com/spec/v1
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oid sha256:a8612e2ba358e32fef8ffe095d2ebb290a035e385d06ea45e520159a8df7c15d
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size 396983720
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