Automatic Speech Recognition
Transformers
PyTorch
TensorFlow
JAX
Safetensors
whisper
audio
hf-asr-leaderboard
Eval Results (legacy)
Instructions to use michaellee8/wisper-base-jwt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use michaellee8/wisper-base-jwt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="michaellee8/wisper-base-jwt")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("michaellee8/wisper-base-jwt") model = AutoModelForSpeechSeq2Seq.from_pretrained("michaellee8/wisper-base-jwt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
44e8c42
1
Parent(s): b9c964e
fix: error
Browse files- handler.py +3 -1
handler.py
CHANGED
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@@ -28,5 +28,7 @@ class EndpointHandler:
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raise RuntimeError("missing token")
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decoded = jwt.decode(token, PUBLIC_KEY, algorithms=["RS512"])
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print("received input from jti=", decoded["jti"])
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-
return pipeline(*
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raise RuntimeError("missing token")
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decoded = jwt.decode(token, PUBLIC_KEY, algorithms=["RS512"])
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print("received input from jti=", decoded["jti"])
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+
inputs = data.pop("inputs", None)
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parameters = data.pop("parameters", {})
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return pipeline(inputs, **parameters)
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