Develop Reference

How to use a trained Tensorflow Lite model in python?

I have a Tensorflow Lite model (.tflite file), which is already trained.
I need to use it in an API python view that receives recorded .wav files for speech recognition, and returns the equivalent text to the recorded file that was sent.
Any advices ou tutos on how I could use the trained model in order to treat the recorded instructions?
Thanks.

Refer to the TFLite Inference Guide for more details.
Specifically, for python refer to this

What are all the formats to save machine learning model in scikit-learn, keras, tensorflow and mxnet?

There are many ways to save a model and its weights. It is confusing when there are so many ways and not any source where we can read and compare their properties.
Some of the formats I know are:
1. YAML File - Structure only
2. JSON File - Structure only
3. H5 Complete Model - Keras
4. H5 Weights only - Keras
5. ProtoBuf - Deployment using TensorFlow serving
6. Pickle - Scikit-learn
7. Joblib - Scikit-learn - replacement for Pickle, for objects containing large data.
Discussion:
Unlike scikit-learn, Keras does not recommend you save models using pickle. Instead, models are saved as an HDF5 file. The HDF5 file contains everything you need to not only load the model to make predictions (i.e., architecture and trained parameters) but also to restart training (i.e., loss and optimizer settings and the current state).
What are other formats to save the model for Scikit-learn, Keras, Tensorflow, and Mxnet? Also what info I am missing about each of the above-discussed formats?

There are also formats like onnx which basically supports most of the frameworks and helps in removing the confusion of using different formats for different frameworks.

There exists also TFJS format, which enables you to use the model on web or node.js environments. Additionally, you will need TF Lite format to make inference on mobile and edge devices. Most recently, TF Lite for Microcontrollers exports the model as a byte array in C header file.

Your question on formats for saving a model has multiple possible answers, based on why you want to save your model:
Save your model to resume training it later
Save your model to load it for inference later
These scenarios give you a couple of options:
You could save your model using the library-specific saving functions; if you want to resume training, make sure that you have saved all the information you need to really be able to resume training. Formats here will vary by library, and indeed are not aimed at being formats that you would inspect or read in any way - they are just files. If you are looking for a library that wraps all of these save functions behind a common API, you should check out the modelstore Python library.
You can also want to use a common format like ONNX; there are converters from Keras to ONNX and scikit-learn to ONNX available; but it is uncommon to use this format to later resume training. The benefit here is that they are all saved to a common format, which may streamline the process of loading them later.

saved model.h5 convert to tensorflow file format

after some searching and asked a question here i found a way to save the entire model of keras inside one file which is example.h5 for later use i found an answer here
https://machinelearningmastery.com/save-load-keras-deep-learning-models/
my previous question is
keras save the model weights to one file
and now i have to change to the mode.h5 convert it to tflite for tensorflow lite then i can used inside the mobile app

When to use the .ckpt vs .hdf5 vs. .pb file extensions in Tensorflow model saving?

Tensorflow explains that models can be saved in three file formats: .ckpt or .hdf5 or .pb. There's a lot of documentation so it would be nice to get a simpler comparison of when to use which file format.
Here's my current understanding:
ckpt
From https://www.tensorflow.org/guide/checkpoint:
Checkpoints capture the exact value of all parameters (tf.Variable
objects) used by a model. Checkpoints do not contain any description
of the computation defined by the model and thus are typically only
useful when source code that will use the saved parameter values is
available.
So it seems like you should use cpkt for checkpointing during training when you know that your source code will be the same. Why is it recommended though over .pb and .hdf5? Does it save space? Does it include data that the other file formats do not?
pb
Also from https://www.tensorflow.org/guide/checkpoint:
The SavedModel format on the other hand includes a serialized
description of the computation defined by the model in addition to the
parameter values (checkpoint). Models in this format are independent
of the source code that created the model. They are thus suitable for
deployment via TensorFlow Serving, TensorFlow Lite, TensorFlow.js, or
programs in other programming languages (the C, C++, Java, Go, Rust,
C# etc. TensorFlow APIs).
The SavedModel format is .pb plus some metadata. So you should save in .pb when you are deploying a model?
hdf5
Use when saving the model weights (matrix of numbers) only?

It seems you already know some of the differences, but just to add.
.ckpt
This is mainly used for resuming the training and also to allow users to customize savepoints and load to (ie. Highest Accuracy, Latest Trained Model, etc).
And also to create different models from different training checkpoints.
This only saves the weights of the variables or the graph therefore as you indicated you need to have full architectures and functions used.
.pb (Protobuffer)
This is the TensorFlow file format which saves everything about the Model including custom objects, this is the recommended file format to ensure maximum portability when using and exporting to different platforms (ie. Tensorflow Lite, Tensorflow Serving, etc.).
.h5 (HD5F)
This is the suggested saving format of Native Keras, which also saves everything about the model but when used in TensorFlow 2.1.0 (import tensorflow.keras) it will not save the custom objects automatically and will require additional steps to be performed.
You could read more about it in this link.

Interpretation script for Yolo's region based output to openvino

Hello I am new to OPENCV/CVAT, I use openvino to run auto annotation, I want to use YoloV3 for this mission.
I need to convert Yolo model to OpenVINO format for opencv/cvat/auto_annotation.
https://github.com/opencv/cvat/tree/develop/cvat/apps/auto_annotation.
To annotate a task with a custom model I need to prepare 4 files:
Model config (*.xml) - a text file with network configuration.
Model weights (*.bin) - a binary file with trained weights.
Label map (*.json) - a simple json file with label_map dictionary
like object with string values for label numbers.
Interpretation script (*.py) - a file used to convert net output
layer to a predefined structure which can be processed by CVAT. This
code will be run inside a restricted python's environment, but it's
possible to use some builtin functions like str, int, float, max,
min, range.
I converted Yolo model to OpenVINO format and created xml and bin files. I write the mapping lson file.
Now I need to write interpretation python script for Yolo's region based output. How can I do that?
Is there an interrupt file from tensorflow models to openvino?

Recommend you to start with Yolo V3 C++ or Python sample.
For C++:
https://github.com/opencv/open_model_zoo/tree/master/demos/object_detection_demo_yolov3_async
For python sample:
https://github.com/opencv/open_model_zoo/tree/master/demos/python_demos/object_detection_demo_yolov3_async
Similar discussion can be found in https://software.intel.com/en-us/forums/computer-vision/topic/816875

This is now bundled with CVAT. The interpret script and json file can be found in the repo here: https://github.com/opencv/cvat/tree/develop/utils/open_model_zoo/yolov3