Sound Classification Based on PaddlePaddle
The project you provided details how to perform speech recognition tasks using PaddlePaddle and the PaddleSpeech acoustic model library. The entire process, from data preparation, model training, prediction, to some auxiliary functions, is clearly described. Below is a summary and some suggestions for your project: ### Project Overview 1. **Environment Setup**: - Python 3.6+ is used with necessary dependency libraries installed. - PaddlePaddle-gpu and PaddleSpeech are installed.
Read MoreSound Classification Based on TensorFlow
This project provides a detailed introduction to the steps of audio classification using TensorFlow, covering data preparation, model training, prediction, and real-time audio recognition. Below are some summaries and supplementary explanations for the code and technical details you provided: ### 1. Dataset Preparation - **Data Source**: Utilized a bird sound classification dataset from Kaggle. - **Data Processing**: - Converted audio files into mel spectrograms. - Read files into numpy arrays using the Librosa library, and
Read MoreBuilding a Smart Assistant Quickly with AIUI on Android
This article introduces how to quickly build a smart assistant similar to Xiaomi's AI Assistant. First, create an application using AIUI (a full-stack human-computer interaction voice solution launched by iFlytek), select the Android platform, and enable the semantic understanding function. Then, add a personalized character and various skills in the skills, and configure fallback responses and text-to-speech. Next, develop an Android application: download the AIUI SDK and copy the dynamic library to the corresponding folder. Modify the APPID in `aiui_phone.json`, run the project for testing, and finally demonstrate a case implemented through this method.
Read MoreDetecting if a User is Speaking Using WebRTC in Android
This article introduces how to implement voice activity detection (VAD) using WebRTC in an Android application. First, an Android project is created, and the `local.properties` file is modified to add the NDK path. A `CMakeLists.txt` file is then created in the `app` directory to configure the compilation environment. Next, necessary configuration items are added to the `build.gradle` file. Subsequently, the WebRTC source code is cloned, and the required VAD
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