Displaying 1 to 14 from 14 results

TensorFlowTTS - :stuck_out_tongue_closed_eyes: TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, Korean, Chinese, German and Easy to adapt for other languages)

  •    Python

🤪 TensorFlowTTS provides real-time state-of-the-art speech synthesis architectures such as Tacotron-2, Melgan, Multiband-Melgan, FastSpeech, FastSpeech2 based-on TensorFlow 2. With Tensorflow 2, we can speed-up training/inference progress, optimizer further by using fake-quantize aware and pruning, make TTS models can be run faster than real-time and be able to deploy on mobile devices or embedded systems. Different Tensorflow version should be working but not tested yet. This repo will try to work with the latest stable TensorFlow version. We recommend you install TensorFlow 2.3.0 to training in case you want to use MultiGPU.

awesome-tensorflow-lite - TensorFlow Lite models, samples, tutorials, tools and learning resources.

  •    

TensorFlow Lite is a set of tools that help convert and optimize TensorFlow models to run on mobile and edge devices. It's currently running on more than 4 billion devices! With TensorFlow 2.x, you can train a model with tf.Keras, easily convert a model to .tflite and deploy it; or you can download a pretrained TensorFlow Lite model from the model zoo. Please submit a PR if you would like to contribute and follow the guidelines here.




E2E-Object-Detection-in-TFLite - This repository shows how to train a custom detection model with the TFOD API, optimize it with TFLite, and perform inference with the optimized model

  •    Jupyter

This repository shows how to train a custom detection model with the TFOD API (TF2 and TF1), optimize it with TFLite, and perform inference with the optimized model. Training_a_pets_detector_model_within_minutes_with_TFOD_API.ipynb notebook uses Colab to demonstrate the training workflow but does not actually uses the Colab runtime for training. It uses Cloud TPUs.

MIRNet-TFLite-TRT - TensorFlow Lite models for MIRNet for low-light image enhancement.

  •    Jupyter

This repository shows the TensorFlow Lite and TensorRT model conversion and inference processes for the MIRNet model as proposed by Learning Enriched Features for Real Image Restoration and Enhancement. This model is capable of enhancing low-light images upto a great extent. Model training code and pre-trained weights are provided by Soumik through this repository.

CFU-Playground - Want a faster ML processor? Do it yourself! -- A framework for playing with custom opcodes to accelerate TensorFlow Lite for Microcontrollers (TFLM)

  •    C++

This project provides a framework that an engineer, intern, or student can use to design and evaluate enhancements to an FPGA-based “soft” processor, specifically to increase the performance of machine learning (ML) tasks. The goal is to abstract away most infrastructure details so that the user can get up to speed quickly and focus solely on adding new processor instructions, exploiting them in the computation, and measuring the results. This project enables rapid iteration on processor improvements -- multiple iterations per day.

Cartoonizer-with-TFLite - How to create a Cartoonizer with TensorFlow Lite models.

  •    Jupyter

This is the GitHub repository for an end-to-end tutorial on How to Create a Cartoonizer with TensorFlow Lite, published on the official TensorFlow blog. The tutorial demonstrates the steps for TFLite model saving, conversion and all the way up to model deployment on an Android App. It's one of a series of the End-to-End TensorFlow Lite Tutorials. See the full list of TensorFlow Lite samples and learning resources on awesome-tflite. In this project repo, the ml folder contains the model files, and the instructions on how to save the model, and convert it to selfe2anime.tflite, and add metadata to it via either command line or a Colab notebook.


E2E-tfKeras-TFLite-Android - End to end training MNIST image classifier with tf

  •    Jupyter

End to end training MNIST image classifier with tf.Keras, convert to TFLite and deploy to Android

Selfie2Anime-with-TFLite - How to create Selfie2Anime from tflite model to Android.

  •    Jupyter

Selfie2Anime with TensorFlow Lite is one of the many End-to-End TensorFlow Lite Tutorials. See the full list of TensorFlow Lite samples and learning resources on awesome-tflite. The ml folder contains the model files, and the instructions on how to save the model, and convert it to selfe2anime.tflite, and add metadata to it via either command line or a Colab notebook.

Flutter-License - TCS humAIn

  •    Dart

TCS humAIn This is a Flutter application that is used to locate the license plate out of a picture given to the application. Cue the Drum Rolls for what I am about to disclose. With the help of Sayak Paul the tensorflow model that was 255mb was cut short to a 2mb file. TFLite did the trick for us. The application is an offline application, that can do a pretty niffty job at license plate location.

TensorFlow-Notebooks - My Tensorflow Notebook

  •    Jupyter

My Tensorflow Notebook. In this notebooks I have implemented various kind of model optimisation techniques.

TFLite_Micro_MicroSpeech_M5Stack - M5Stack (ESP32) port of TensorFlow Lite for Microcontrollers demo "Micro Speech"

  •    C++

M5Stack port of micro-speech demo. Say "yes" or "no" to your M5Stack. M5stack would change its facial expression according to your words. M5Stack is an ESP32-based module with TFT LCD and analog MEMS microphone. This port uses Arduino-based M5Stack library on PlatformIO. I have tested this on my M5Stack FIRE, but M5GO would also work.






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