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
Commit ·
e926f15
1
Parent(s): aac9b65
Add TF weights (#1)
Browse files- Add TF weights (ad24b4a9c34e511edf01bcee1a3e55ad295619ac)
- tf_model.h5 +3 -0
tf_model.h5
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oid sha256:a997bb84b79970c89a3b5dd6188b18c1e0f1814cfecefbe40039ed052361129b
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