Awesome GitHub RepositoriesOn-Device Speech Recognizers

This project is a Python speech recognition library that serves as a unified interface for converting spoken audio into text. It functions as a bridge between Python applications and a variety of speech-to-text engines, providing a consistent way to interact with both local and cloud-based recognition services. The library distinguishes itself as a multi-engine transcription tool, wrapping diverse online APIs and offline recognition backends into a standardized format. This allows for interchangeable recognition engines and supports multilingual audio transcription through various language pa

Cactus is an on-device AI inference engine designed for executing large language models, vision models, and speech-to-text systems on mobile and wearable hardware. It provides a programmable tensor computation graph for defining sequences of matrix operations and activation functions, alongside a local retrieval augmented generation framework that grounds model responses using local text files. The project features a multiplatform SDK with language bindings for integrating AI capabilities into mobile applications and a model conversion system that transforms external model formats for optimiz

Porcupine is an on-device wake word detection engine that listens for a specific spoken phrase in real-time audio and triggers actions, all processed locally without any cloud connectivity. It includes a custom wake word model creator that generates production-ready models from just a few spoken examples in seconds, requiring no training data. Beyond wake word detection, Porcupine also provides on-device speech recognition for real-time transcription with custom vocabulary, an on-device audio content searcher that indexes and finds spoken phrases in audio files or streams, and a lightweight vo

CTranslate2 is a C++ inference engine and runtime for Transformer models, designed to execute models on both CPU and GPU with optimizations for speed and memory efficiency. It functions as a model format converter, quantization tool, and REST API server, enabling deployment of neural machine translation, automatic speech recognition, and text generation models. The engine distinguishes itself through a suite of runtime optimizations including layer fusion, weight-matrix quantization, batch-by-length grouping, and a caching allocator that reuses GPU memory. It supports tensor-parallel model di

PocketSphinx is an offline speech recognition engine that converts raw audio from files or live microphone streams into written text without requiring a network connection. It functions as a speech-to-text library, a real-time transcription engine, and a voice command processor, capable of detecting and transcribing spoken commands from continuous audio streams with configurable acoustic and language models. The engine uses weighted finite-state transducers to represent acoustic, phonetic, and language models as a single search graph for efficient decoding. It employs fixed-point acoustic mod

Tesseract-OCR-iOS is a native integration of the Tesseract engine for iOS applications. It provides on-device image recognition to identify and extract printed text from images, converting them into editable strings. The project enables local device execution, meaning image processing and text extraction occur entirely on the hardware without the use of external cloud services or network requests. It utilizes a C++ wrapper and an Objective-C bridge to interface high-level iOS APIs with the underlying Tesseract engine and Leptonica image processing library. The library supports document digit

Tess-two is an optical character recognition tool and Android application designed to extract written text from images using the Tesseract engine. It functions as an image analysis utility for detecting visual artifacts, blur, and optical flow within local image files on Android devices. The project includes an image pre-processing suite used to clean and manipulate images to increase the accuracy of text recognition. This involves a pipeline that applies grayscale conversion and binarization before the recognition process. The software integrates native image processing and character analys

Foundry-Local is a machine learning development tool designed to facilitate private, on-device inference and model management. It provides a local server environment that hosts machine learning models directly on the user's hardware, ensuring that all data processing, including prompt handling and audio transcription, remains within the local environment without requiring external cloud connectivity. The project distinguishes itself by automating the entire model lifecycle, including the discovery, downloading, and versioning of assets to maintain compatibility with host hardware. It features

Sherpa-ncnn is an edge-based speech recognition and synthesis engine designed to run neural network models locally on mobile, embedded, and desktop hardware. It provides a cross-platform framework for offline speech-to-text transcription and text-to-speech synthesis, ensuring that all audio processing occurs on-device without requiring an internet connection or external cloud services. The project distinguishes itself through its use of the ncnn inference engine, which is optimized for low-latency execution on resource-constrained devices. It incorporates on-device model quantization to reduc

यह प्रोजेक्ट Apple Silicon के लिए विशेष रूप से इंजीनियर, ऑन-डिवाइस स्पीच रिकग्निशन, सिंथेसिस और ऑडियो प्रोसेसिंग के लिए एक व्यापक टूलकिट है। यह वास्तविक समय, फुल-डुप्लेक्स वॉयस एजेंट बनाने के लिए एक फ्रेमवर्क प्रदान करता है जो पूरी तरह से ऑफ़लाइन काम करते हैं, प्रदर्शन और गोपनीयता बनाए रखने के लिए नेटिव हार्डवेयर त्वरण का लाभ उठाते हैं। ऑप्टिमाइज़्ड मशीन लर्निंग मॉडल का उपयोग करके, लाइब्रेरी बाहरी क्लाउड सेवाओं पर निर्भरता के बिना जटिल ऑडियो कार्यों के स्थानीय निष्पादन को सक्षम बनाती है। यह लाइब्रेरी स्थानीय, उच्च-प्रदर्शन वॉयस इंटरैक्शन पर अपने विशेष फ़ोकस के माध्यम से खुद को अलग बनाती है। इसमें स्ट्रीमिंग ऑडियो पाइपलाइनों के लिए परिष्कृत ऑर्केस्ट्रेशन शामिल है, जो कम विलंबता के साथ वास्तविक समय ट्रांसक्रिप्शन, स्पीच सिंथेसिस और वॉयस क्लोनिंग की अनुमति देता है। सिस्टम को निरंतर, इंटरैक्टिव बातचीत को संभालने के लिए डिज़ाइन किया गया है, जिसमें ऑडियो फीडबैक लूप को रोकने और पर्सिस्टेंट स्ट्रीमिंग सत्रों को प्रबंधित करने के लिए इन-बिल्ट तंत्र शामिल हैं। मुख्य इंटरैक्शन से परे, प्रोजेक्ट ऑडियो एन्हांसमेंट और प्रबंधन क्षमताओं का एक विस्तृत सूट प्रदान करता है। यह स्पीकर डायराइजेशन और एम्बेडिंग निष्कर्षण के साथ-साथ सोर्स सेपरेशन, नॉइज़ रिडक्शन और ऑडियो अपसैंपलिंग सहित उन्नत सिग्नल प्रोसेसिंग का समर्थन करता है। फ्रेमवर्क व्यापक मॉडल प्रबंधन उपयोगिताएँ भी प्रदान करता है, जैसे क्वांटाइजेशन नियंत्रण, मेमोरी प्रबंधन और कस्टम मॉडल वेट लोडिंग के लिए समर्थन, यह सुनिश्चित करते हुए कि डेवलपर्स स्थानीय हार्डवेयर पर प्रोसेसिंग गति और संसाधन खपत को संतुलित कर सकें। प्रोजेक्ट में ऑडियो कार्यों को निष्पादित करने और मॉडल वेट्स को ऑप्टिमाइज़्ड प्रारूपों में बदलने के लिए एक कमांड-लाइन इंटरफ़ेस शामिल है। यह मानक उद्योग इंटरफ़ेस के साथ एकीकरण की सुविधा के लिए HTTP और WebSocket एंडपॉइंट्स को भी उजागर करता है।

Expo Speech Recognition is a cross-platform mobile module that converts live microphone audio and pre-recorded files into text using native speech engines. It provides offline speech recognition capabilities by downloading and verifying local speech models to enable on-device processing without an active network connection. The library includes session lifecycle management to start, stop, or abort recording, alongside real-time spoken language detection with confidence scoring. It emits volume change events for metering interfaces, handles audio session configuration and routing, and persist