Automatic Speech Recognition
Transformers
ONNX
English
onnxruntime
speech
asr
granite
ibm
quantized
int8
fp16
Instructions to use smcleod/ibm-granite-speech-4.1-2b-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smcleod/ibm-granite-speech-4.1-2b-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="smcleod/ibm-granite-speech-4.1-2b-onnx")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("smcleod/ibm-granite-speech-4.1-2b-onnx", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb8914436f9ee974ce9f9e671ac4c10814970f856a8a68dfc5bbae85acd1d88d
- Size of remote file:
- 1.63 GB
- SHA256:
- 70652d6a31cbae2d57c7e8cefb665f6c1ee503e495d191b951fff09ddb7f8608
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