Awesome GitHub RepositoriesAutomatic Speech Recognition

This project is a speech recognition and translation engine that utilizes a sequence-to-sequence transformer architecture to convert audio into text. It is built upon a weakly supervised learning framework, which leverages large-scale, unlabelled audio-transcript data to create generalized speech representations capable of performing simultaneous transcription, language identification, and translation. The system distinguishes itself through a unified multi-task modeling approach that shares token sequences across different objectives, allowing it to handle diverse languages and vocabularies

PyTorch is a machine learning framework centered on a GPU-ready tensor library that supports multi-dimensional array operations across both CPU and accelerator hardware. It provides a foundational infrastructure for mathematical computation and dynamic neural network construction, utilizing a tape-based automatic differentiation system that allows for flexible, non-static graph execution. The framework is designed for deep integration with Python, enabling natural usage alongside standard scientific computing ecosystems. It distinguishes itself through a comprehensive distributed training sui

whisper.cpp is a C++ implementation of the Whisper speech-to-text model, serving as a lightweight machine learning inference engine and quantized runtime. It provides high-performance automatic speech recognition and real-time audio transcription without requiring a Python environment. The project utilizes model quantization to reduce memory usage and increase inference speed on local hardware. It incorporates hardware acceleration to optimize processing speed across different processors. The system covers audio processing capabilities including voice activity detection, speaker diarization,

This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed

Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic speech recognition, and large-scale language model training. It provides a framework for processing and aligning diverse data sources, including text, audio, and video, to support tasks such as speech-to-text conversion and multimodal sequence learning. The project is distinguished by its distributed training capabilities, which utilize parameter sharding, mixed-precision training, and CPU offloading to handle models that exceed single-device memory. It also includes specializ

faster-whisper is an automatic speech recognition framework and an optimized implementation of the Whisper speech-to-text engine. It functions as a CTranslate2 inference engine designed to convert spoken audio into written text. The project serves as a model quantization tool that transforms large audio model weights into lower precision formats. This process reduces memory usage and increases execution speed on hardware by utilizing integer quantized weights. The framework covers a broad range of capabilities including batch audio transcription for parallel processing and voice activity det

WhisperX is an automated speech recognition toolkit designed to convert spoken audio into text while maintaining precise synchronization with the original media. It functions as an integrated pipeline that combines transcription, phoneme-based alignment, and speaker diarization to produce structured, attributed transcripts. The project distinguishes itself through its use of forced alignment, which matches existing text to audio signals at the phoneme level to generate accurate word-level timestamps. It also incorporates speaker diarization to identify and label unique voices within a recordi

FunASR is an automatic speech recognition toolkit and multilingual speech-to-text engine designed to convert spoken audio into written text across more than fifty languages. It provides a framework for speaker diarization, an OpenAI-compatible transcription API for local server hosting, and speech models compatible with the ONNX format. The project distinguishes itself by supporting high-performance inference on edge hardware via self-contained binaries and portable model exports. It incorporates specialized capabilities for natural speech generation with adjustable timbre and emotional expre

NeMo is a multimodal AI framework and toolkit designed for the development, training, and scaling of large language models, generative AI systems, and speech-based models. It functions as an automatic speech recognition toolkit, a text-to-speech engine, and a framework for building models that process and generate combinations of text, image, and audio data. The project serves as a conversational AI orchestrator capable of managing real-time, interruptible voice interactions. It provides specialized workflows for speech translation, converting spoken audio from one language into text or speec

This repository is a deep learning for natural language processing course and curriculum. It provides educational material and guides focused on neural network architectures used for processing natural language, speech signals, and text classification. The content includes instructional tutorials on sequence modeling and neural language modeling, covering the implementation of n-gram and recurrent neural networks. It also provides a framework for studying word embeddings to map linguistic meanings into numerical representations. The curriculum covers a broad range of capabilities, including

Kaldi is an automatic speech recognition toolkit used to train and deploy models that convert spoken audio into text. It functions as a framework for designing and evaluating acoustic and language models through a structured pipeline of processing tools. The system acts as a cross-platform speech engine, capable of compiling recognition logic for Android and WebAssembly to enable execution on mobile devices and web browsers. It also includes a dedicated converter for migrating speech recognition models from the HTK format into a compatible internal structure. The toolkit covers a broad range

PaddleSpeech is a comprehensive toolkit of neural models for speech recognition, synthesis, and translation built on the PaddlePaddle deep learning framework. It provides a collection of frameworks and tools for converting spoken audio into written text, synthesizing natural audio from text, and performing direct speech translation. The toolkit includes specialized capabilities for keyword spotting to detect trigger words and speaker verification systems that extract unique voiceprints to identify and distinguish between individuals. It also features end-to-end translation tools that map audi

This project is a multimodal translation framework and large language model capable of speech-to-speech, speech-to-text, and text-to-text translation across nearly 100 languages. It provides a real-time speech translation engine and a comprehensive toolkit for converting spoken audio between languages. The system is distinguished by its ability to preserve the original speaker's tone, pace, and prosody during translation. It utilizes a specialized on-device inference toolkit that converts model checkpoints into C-based libraries, enabling low-latency execution on mobile and edge hardware with

SpeechBrain is an all-in-one deep learning toolkit designed for speech and audio processing. Built as a modular library, it provides a structured environment for developing, training, and deploying neural network models across a wide range of tasks, including automatic speech recognition, speaker identification, and audio enhancement. The framework distinguishes itself through a configuration-driven approach that separates model architecture and training hyperparameters from application logic. By utilizing externalized configuration files and standardized recipes, it enables reproducible rese

Azure Docs is the official technical documentation repository for Microsoft Azure, the cloud computing platform. It provides comprehensive guidance on the full spectrum of Azure services, covering everything from core infrastructure components like virtual machines, Kubernetes clusters, and serverless computing to platform services for AI, machine learning, data analytics, and storage. The documentation details how to provision, manage, and govern cloud resources at scale, including policy enforcement, identity management, and cost optimization. The documentation distinguishes Azure through i

Whisper is a high-performance speech-to-text inference engine that uses graphics hardware shaders to accelerate the transcription of spoken audio into written text. It implements a GPU-accelerated automatic speech recognition framework specifically designed to run Whisper models. The system focuses on high-speed processing for both recorded audio files and live microphone streams. It utilizes voice activity detection to analyze raw audio in real time, triggering the inference engine only when human speech is detected. The engine covers a broad range of capabilities including real-time audio

AudioGPT is an LLM-driven audio framework and processing suite that uses large language models to orchestrate neural audio pipelines. It functions as a multimodal audio generator and processing system, integrating a collection of pretrained models to handle speech synthesis, sound generation, and audio manipulation. The system is distinguished by its ability to generate audio from diverse inputs, including text and images, and its capacity to produce synchronized talking head videos. It also operates as a neural speech translator, converting spoken language between different tongues while pre

ESPnet is a comprehensive speech processing toolkit and PyTorch-based trainer designed for building end-to-end speech recognition, synthesis, and translation models. It provides a structured framework for developing automatic speech recognition systems using transducer and encoder-decoder architectures, alongside engines for text-to-speech synthesis and speech translation pipelines. The project distinguishes itself through a recipe-based workflow execution system that ensures experimental reproducibility by running standardized sequences of scripts for data preparation and model training. It

RealtimeSTT is a local speech-to-text engine and real-time automatic speech recognition server. It utilizes transformer-based recognition and omnilingual pipelines to convert live audio streams into text, providing a WebSocket-based streaming API for raw PCM audio transmission. The project is distinguished by a dual-backend transcription pipeline that uses a lightweight engine for immediate partial suggestions and a heavier model for final high-accuracy results. It includes a wake word detection system to trigger recording and employs a shared-resource inference model to distribute heavy spee

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