Is there any option to download tensorflowlite model from EdgeImpulse and the tflite model can be analyzed in STM32MX tool to generate C code?
Please help
Related
I want to preprocess an audio file and convert it to spectrogram before inserting it to my tflite model in flutter app. is there a way I can run my preprocessing function (.py) on flutter by converting it to .tflite?
That's currently not supported. You would have to include the preprocessing steps in the model itself (as TF ops, if the ops are supported in TFLite) so that it's included in converted TFLite model, or preprocess it manually outside the model in Flutter.
Kindly close the issue / mark this as the accepted answer if your issue is resolved.
I am currently working with Darknet on Yolov4, with 1 class.
I need to export those weights to onnx format, for tensorRT inference.
I've tried multiple technics, using ultralytics to convert or going from tensorflow to onnx. But none seems to work. Is there a direct way to do it?
Check this GitHub repo: https://github.com/Tianxiaomo/pytorch-YOLOv4
Running the demo_darknet2onnx.py script you'll be able to generate the ONNX model from the .cfg and .weights darknet files.
Usage example:
python demo_darknet2onnx.py <cfgFile> <weightFile> <imageFile> <batchSize>
You can also decide the batch size for the inference calls of the converted model.
The following repo exports yolov3 models from darknet to onnx, for tensorRT inference. You can use this as reference for your model.
https://github.com/jkjung-avt/tensorrt_demos/tree/master/yolo
You can convert scaled YOLO-yolov4,yolov4-csp.yolov4x-mish,yolov4-P5 etc models into onxx & its perfectly work fine.
https://github.com/linghu8812/tensorrt_inference
How to create an ONNX file manually? I mean without using the frameworks like PyTorch, caffe2, e.t.c, can we(binary encode maybe) create an onnx file, if we know the network details in prior like # of nodes, types of nodes, their connections, e.t.c?
Found it!! We can use onnx.helper class inside onnx library to create a model and save it in onnx format.
I'm using kaldi for asr and now I want to do speaker segmentation using Kaldi's x-vector approach. They are providing some example segmentation scripts at https://github.com/kaldi-asr/kaldi/tree/master/egs/sre16/v2 .They also provide a basic pretrained model on LDC corpus at https://david-ryan-snyder.github.io/2017/10/04/model_sre16_v2.html
This pretrained model has following structure when unarchived:
I don't have access to LDC corpus and I want to know how to train a model on my own data, and then how to use that model to do actual segmentation ?
I want to know how to train a model on my own data
There is voxceleb demo which uses public data, you can run it yourself.
You can also format your data in the proper data structure (create data/utt2spk and data/wav.scp files) and run with your data.
https://github.com/kaldi-asr/kaldi/tree/master/egs/voxceleb/v2
and then how to use that model to do actual segmentation ?
You start with the scripts from the demo, removing unused parts. That will give you basic segmentation demo. You can call this reduced demo to do the segmentation with system(2) call from your application or in a similar way.
Then if you need you can turn the scripts into corresponding C++ API calls and call the same procedure from C++ or from any scripting language.
I have created a simple Simulink library because I am learning about masks. The library is saved into Documents folder, which is in path of MATLAB. To test my library, I've created a model and I've inserted my block from my library. When I want to change the value of a parameter in the mask I receive this error screenshot of message
Is there any configuration to do?
Thank you so much