Speech Recognition using STM32 and Machine Learning
-
Updated
Jun 11, 2024 - C
Speech Recognition using STM32 and Machine Learning
A video call apps to enable deaf people to communicate with normal people using sign language recognition and speech-to-text
Emgu TF is a cross platform .Net wrapper for the Google Tensorflow library. Allows Tensorflow functions to be called from .NET compatible languages such as C#, VB, VC++, IronPython.
Go binding for TensorFlow Lite
Visualizer for neural network, deep learning and machine learning models
NNtrainer is Software Framework for Training Neural Network Models on Devices.
A TensorFlow Lite object detection prototype using the COCO-SSD-Mobile-Net model.
Pytorch to Keras/Tensorflow conversion made intuitive
The Learning and Experiencing Cycle Interface (LExCI).
Yocto layer for TensorFlow Lite interpreter with Python / C++.
HVACGraphicsClassifier is an experimental tinyML project aimed at classifying HVAC control system graphics using computer vision. A project goal from the start is support of Tiny ML Micro, allowing models to be quantized to run on microcontrollers with the TensorFlow C library.
Android On_device 1:1 Face Recognition And Alive Detect;1:N & M:N Face Search SDK 。 🧒 离线版设备端Android1:1人脸识别动作活体检测,静默活体检测 以及1:N M:N 人脸搜索 SDK 封装
A lightweight face-recognition toolbox and pipeline based on tensorflow-lite
OpenEmbedded meta layer to install AI frameworks and tools for the STM32MPU series
A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
A cross-platform framework that deploys and applies SSCMA models to microcontrol devices
🔥 High-performance TensorFlow Lite library for React Native with GPU acceleration
A robust and efficient TinyML inference engine.
This project implements a Speech Emotion Recognition (SER) model using TensorFlow Lite, specifically designed for deployment on microcontrollers like the Arduino Nano BLE33. The model is trained on the RAVDESS dataset and can recognize seven emotions: Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise.
Add a description, image, and links to the tensorflow-lite topic page so that developers can more easily learn about it.
To associate your repository with the tensorflow-lite topic, visit your repo's landing page and select "manage topics."