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dify

Dify is an open-source LLM app development platform. Dify's intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production.

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screenshot-to-code

Drop in a screenshot and convert it to clean code (HTML/Tailwind/React/Vue)

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gpt-pilot

The first real AI developer

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SnakeList

iOS**区免费排行榜前100Swift/SwiftUI/React Native/Flutter/Weex应用现状。Top 100 Free Apps in U.S. Swift/React Native/Flutter/Weex Adoption.

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kws-training-suite

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openWakeWord

An open-source audio wake word (or phrase) detection framework with a focus on performance and simplicity.

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search_with_lepton

Building a quick conversation-based search demo with Lepton AI.

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WhisperHallu

Experimental code: sound file preprocessing to optimize Whisper transcriptions without hallucinated texts

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SimplePingHelper

How to perform a Ping in an iPhone app

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NetSpeedMeasurer

网络实时测速下载和上传的最大最小网速以及平均网速、以及实时速率

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NtSpeed

See Active Traffic Network Speed

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react-navigation

Routing and navigation for your React Native apps

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Silero-VAD-for-iOS

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GPT-SoVITS

1 min voice data can also be used to train a good TTS model! (few shot voice cloning)

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mlx-examples

Examples in the MLX framework

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mobile-aloha

Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation

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rnnoise

Recurrent neural network for audio noise reduction

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pyannote-audio_speaker-diarization_cpp

C++ version of pyannote audio speaker diarizaiton pipeline

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silero-vad

Silero VAD: pre-trained enterprise-grade Voice Activity Detector

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whisper.rn

React Native binding of whisper.cpp.

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multilingual_kws

Few-shot Keyword Spotting in Any Language and Multilingual Spoken Word Corpus

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interactive-tutorials

Interactive Tutorials

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Wake-UP-word-detection

Wake-up-word(WUW)system is an emerging development in recent times. Voice interaction with systems have made life ease and aids in multi-tasking. Apple, Google, Microsoft, Amazon have developed a custom wake-word engine, which are addressed by words such as ‘Hey Siri’. ‘Ok Google’, ‘Cortana’, ‘Alexa’. Our project focuses initially only detection and response to a customized wake-up command. The wake-up command used is “GOLUMOLU”. A wake-up-word detection system search for specific word and reads the word, where it rejects all other words, phrases and sounds. WUW system needs only less memory space, low computational cost and high precision. Artificial Neural Networks(ANN) have reduced the complexity, computational time, latency, thus the efficiency of system has improved. Deep learning has improved the efficiency of automatic speech recognition(SR), where wake word detection is a subset of SR but unlike keyword spotting and voice recognition. A deep learning RNN model is used for the training of the network. RNN are specifically used in case of temporal sequence data and has the ability to process data of different length but of same dimension. For training a model, labelled dataset is needed. We generated three forms of data: golumolu, negative and background. Such that, the model learns circumspectly and attentively detects when specific word found. To start communication with system, the wake word should be delivered. The main task of WUW detection system is to detect the speech, to identify WUW words among spoken words, to check whether the word spoken in altering context.

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