Open-source alternatives to Distil Whisper

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

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,

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

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

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

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

WhisperLive is a real-time speech-to-text server that converts live audio streams into text using Whisper models. It functions as a backend service that receives microphone input via WebSockets and provides incremental transcriptions with word-level timestamps. The system utilizes a GPU-accelerated inference engine and a keyword-boosted transcription API to improve the recognition accuracy of domain-specific jargon, acronyms, and product names. It also includes a speaker diarization tool that clusters audio embeddings to identify and label different participants within a recording. Additiona

This project is a self-hosted meeting transcription and summarization tool that converts audio recordings into text transcripts and structured notes using large language models. It functions as an enterprise meeting documentation manager, allowing for the organization and editing of timestamped records. The system prioritizes data privacy through local-first processing and the ability to deploy on private infrastructure. It supports a provider-agnostic architecture, enabling users to connect to local AI engines, self-hosted servers, or cloud-based API endpoints for both transcription and summ

This project is an AI meeting assistant and interview copilot that monitors system audio and screen content to generate real-time responses during video calls. It functions as a system audio transcription tool and a context-aware prompt manager, injecting user documents and behavioral profiles into large language model prompts to tailor AI outputs. The system features a stealth screen overlay, utilizing a transparent window that displays information on top of other applications while remaining invisible to screen-sharing software and proctoring tools. It employs a process-hiding mechanism to

Omi is an open-source wearable AI platform that captures audio and screen data to provide real-time conversational assistance and memory. It integrates a wearable hardware development kit with a vector memory database and large language model capabilities to create a persistent digital record of user interactions. The platform is distinguished by its BLE audio streaming pipeline, which transmits raw audio from wearable hardware for real-time transcription and speaker identification. It utilizes a plugin-based agent tool framework that allows AI assistants to autonomously invoke custom functio

MiniCPM is a collection of small language models designed for local, on-device deployment in resource-constrained environments. The project focuses on running dense Transformer models on consumer hardware, including GPUs, CPUs, and Apple Silicon, without requiring custom code forks. The project distinguishes itself through heavy optimization for edge hardware, utilizing quantized weight compression in GGUF and MLX formats to reduce memory overhead. It implements advanced inference techniques such as speculative sampling and radix-tree prefix caching to accelerate generation speed and throughp

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

Vibe is a cross-platform transcription tool that converts spoken audio into text by running Whisper neural models directly on your device, with no cloud dependency. It can transcribe audio from files, microphones, system output, and network streams, and supports both batch processing of multiple files and real-time captioning from continuous input. Beyond basic transcription, Vibe identifies and labels different speakers through speaker diarization, and offers a choice of Command-Line Interface or HTTP API for automated and remote workflows. It also includes plugins to export transcripts to c

Voicebox is a local speech processing system that provides text-to-speech generation, speech-to-text transcription, and voice cloning. It utilizes local machine learning inference and GPU acceleration to process audio and text data without relying on external API calls. The project features a voice cloning toolkit for creating synthetic profiles from audio samples and a timeline-based voice editor for composing multi-character conversations. It also includes an AI voice management API that allows external applications and AI agents to programmatically manage voice profiles and generate speech

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

MiniCPM-V is a multimodal large language model and vision-language system designed for complex visual and linguistic understanding. It functions as an on-device AI model, providing the capacity to process text, images, and video as a compact neural network. The project is specifically developed as an edge AI framework, utilizing quantization and weight sharding to run on memory-constrained mobile chipsets. This allows for the deployment of multimodal intelligence directly on mobile operating systems for local inference. Its capabilities cover multimodal content analysis of high-resolution im

LiteRT-LM is a high-performance inference framework designed to execute large language models locally on mobile, desktop, and IoT hardware. It serves as an on-device model runtime that utilizes CPU, GPU, and NPU acceleration to provide low-latency processing. The framework is distinguished by its ability to process text, vision, and audio inputs through a single multi-modal inference engine. It features a local HTTP server that emulates OpenAI-compatible API endpoints and a WebGPU-based runtime for executing models directly within a web browser. To ensure output reliability, it includes a con

Model-Optimizer is a deep learning toolkit and framework dedicated to compressing, pruning, quantizing, and optimizing neural network architectures. It provides methodologies covering weight quantization, model distillation, and speculative decoding for efficient text generation, alongside automated neural architecture search for discovering optimal network structures. The library implements post-training quantization pipelines that convert high-precision neural network weights into lower-bit formats using calibration data. Additional optimization techniques include teacher-student knowledge

Ten Framework is a multimodal large language model agent framework designed for building low-latency conversational agents. It integrates voice, text, and visual inputs in real time to facilitate human interaction. The project includes a real-time speech processing pipeline for streaming transcription, voice activity detection, and speaker diarization. It also features an avatar synchronization engine that coordinates character lip animations and visual outputs with synthesized speech. The framework covers edge AI deployment through containerized packaging and direct integration with embedde

This project is a Chinese automatic speech recognition framework and deep learning system designed to convert spoken Chinese audio into written text. It functions as a toolkit for training, evaluating, and deploying speech-to-text models, utilizing a specialized pinyin-to-text converter that transforms phonetic sequences into Chinese characters using a probability graph model. The system is distinguished by its deployment flexibility, offering a dockerized recognition server that provides transcription capabilities as a remote API. It supports high-performance streaming through a gRPC speech-

Sherpa-ONNX is an ONNX-based speech processing toolkit that provides a local speech recognition engine, an on-device voice synthesis tool, and a speaker identification framework. It is designed as a cross-platform speech API that enables speech-to-text, text-to-speech, and speaker verification tasks to be executed locally on a device without requiring network access. The project is distinguished by its ability to perform zero-shot voice cloning and speaker diarization on-device. It supports a wide range of hardware accelerations, including GPU and various NPU architectures, and provides a Web

Pipecat is a framework and software development kit for building real-time multimodal AI agents and speech-to-speech systems. It utilizes a frame-based data pipeline to route audio, video, and text through a modular sequence of processors, enabling the orchestration of low-latency conversational AI. The project is distinguished by its ability to coordinate complex multimodal services, including speech-to-text, language models, and text-to-speech, within a single pipeline. It features semantic voice activity detection for natural turn-taking, state-machine conversation flows for dialogue manag

Omnilingual-ASR is a multilingual automatic speech recognition framework and toolkit designed to transcribe audio across 1,600 languages. It provides a complete pipeline for converting speech to text, including a toolkit for fine-tuning pre-trained speech models to specific languages or datasets using custom training recipes. The system supports zero-shot speech recognition, allowing the model to predict text in unseen languages without extensive training data. It further enables few-shot language guidance through in-context examples and uses language codes to constrain transcription output t

This is a collection of pre-trained neural models for speech recognition, synthesis, and voice activity detection. It provides a library of assets designed for speech-to-text, text-to-speech, and the identification of human speech segments within audio. The project features text-to-speech synthesis with support for multiple languages and the use of Speech Synthesis Markup Language to control prosody, pitch, and timing. For speech recognition, the system includes capabilities for transcribing audio to text with word-level timestamp extraction and an automated punctuation restorer to insert cap

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