Instructions to use mohitsha/whisper-tiny-dynamic-quantized-onnxruntime with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohitsha/whisper-tiny-dynamic-quantized-onnxruntime with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mohitsha/whisper-tiny-dynamic-quantized-onnxruntime")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mohitsha/whisper-tiny-dynamic-quantized-onnxruntime") model = AutoModelForSpeechSeq2Seq.from_pretrained("mohitsha/whisper-tiny-dynamic-quantized-onnxruntime") - Notebooks
- Google Colab
- Kaggle
Upload decoder_with_past_model_quantized.onnx with huggingface_hub
Browse files
decoder_with_past_model_quantized.onnx
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:049bd9539b176a717ede870e4fdfdbfa0d777342e95e9aa28f9dd67801ef68a4
|
| 3 |
+
size 49141134
|