Open-source alternatives to Silero Models

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

Bert-VITS2 is a neural speech synthesis system and AI voice generator designed to convert written text into natural sounding audio. It utilizes a VITS2 engine and a neural speech synthesis model to produce high-fidelity human voices. The system incorporates a multilingual BERT language processor to improve the prosody and emotional accuracy of the generated speech. It supports multilingual voice generation and custom voice cloning to replicate specific human speech patterns and tones. The architecture covers text-to-speech synthesis through a multi-stage pipeline involving phoneme alignment,

This project is a neural text-to-speech system and voice trainer that converts written text into spoken audio across a variety of global languages and regional dialects. It functions as an ONNX-based engine capable of performing fast offline inference and uses a phoneme-based controller to manage precise pronunciation. The system distinguishes itself through a comprehensive toolkit for neural voice training, allowing for the creation of custom single-speaker or multi-speaker models. It supports the export of these models to a standardized open format and provides hardware acceleration via gra

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,

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

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This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene

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

Silero VAD is a voice activity detection model and deep learning speech classifier designed to distinguish human speech from silence across diverse languages and noisy environments. It functions as a pre-trained neural network capable of identifying speech segments within both static audio recordings and real-time data streams. The project includes a language identification tool for classifying spoken languages and a framework for fine-tuning audio models. It provides utilities for optimizing detection thresholds using validation datasets and retraining the model with custom labeled audio to

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

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

ChatTTS-ui is a web-based interface and API wrapper for the ChatTTS model, designed to convert written text and mixed language input into spoken audio. It functions as an AI speech synthesis dashboard and a programmatic generator for creating naturalistic voice output. The project focuses on custom voice profiling and speech nuance control. It allows for the maintenance of consistent speaker characteristics using seed values and data files, while providing controls for tone, laughter, and pauses through behavioral prompts and sampling parameters. The system includes a client-server architect

This project is a cross-platform machine learning inference engine designed to execute pre-trained models across diverse operating systems and hardware environments. It functions as a standardized execution framework that manages the entire lifecycle of model inference, from loading and graph optimization to hardware-accelerated execution and generative sequence management. The runtime distinguishes itself through a highly modular architecture that decouples model logic from hardware-specific kernels. By utilizing an execution provider abstraction, it enables developers to offload computation

This project is an educational resource and comprehensive guide for implementing and deploying deep learning models using the PyTorch framework. It provides a structured learning curriculum consisting of tutorials and notebooks that cover neural network architectures, data pipelines, and model optimization across multiple AI domains. The curriculum includes practical implementation guides for building convolutional networks, transformers, and recurrent models. It specifically focuses on workflows for computer vision, including image classification, object detection, and segmentation, as well

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

Bark Voice Cloning is a text-to-speech synthesis engine designed to generate natural-sounding audio and replicate specific vocal characteristics. The system utilizes a transformer-based autoregressive model to convert written text into high-fidelity speech, supporting multilingual output and expressive delivery. The project distinguishes itself through zero-shot voice cloning, which extracts speaker identity embeddings from short audio samples to condition the generative model without requiring extensive fine-tuning. It also provides specialized workflows for voice identity conversion, allowi

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

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

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

MLOps-Basics is a collection of implementation guides and blueprints for automating the machine learning lifecycle. It provides practical workflows for managing the transition of models from training to production deployment, focusing on the integration of operational tools into the machine learning pipeline. The project features specific architectural patterns for deploying containerized models using serverless infrastructure and cloud registries. It includes frameworks for tracking large datasets and model artifacts via remote storage, as well as guides for converting models into standardiz

This project is an on-device AI SDK providing a framework for running large language models, vision models, and speech models locally. It serves as an orchestration layer for local LLM execution, ensuring data privacy and offline availability by utilizing hardware acceleration on the device. The SDK is distinguished by its comprehensive voice and multimodal capabilities, including a coordinated voice pipeline for activity detection, speech-to-text, and text-to-speech synthesis. It also provides a dedicated implementation kit for local retrieval-augmented generation and tools for processing co

WeNet is an end-to-end automatic speech recognition (ASR) toolkit designed for both Chinese and English, built around transformer-based models. It supports streaming and non-streaming inference out of the box, and is structured to be production-ready, with model export and deployment paths for servers and mobile devices. The toolkit distinguishes itself through a chunk-based streaming transformer architecture that processes audio in fixed-size segments for low latency while preserving context across chunks. It jointly trains models with both CTC and attention loss to combine alignment accurac

mlx-audio is an audio processing toolkit built on Apple MLX that provides speech transcription, text-to-speech synthesis, voice cloning, and audio source separation using local models. It offers an OpenAI-compatible REST API and web interface for running audio generation and transcription tasks, enabling drop-in integration with existing tools that follow that endpoint structure. The toolkit supports text-prompted audio source separation, allowing specific sounds to be isolated from mixed recordings based on natural language descriptions. It also provides voice cloning from a short reference

ChatTTS is a conversational text-to-speech generative model designed to convert written dialogue into natural sounding audio. It functions as a multilingual speech synthesis framework capable of producing human-like audio across different languages and speaker profiles. The system is distinguished by its ability to generate interactive dialogue with realistic vocal nuances. It utilizes a speech nuance controller to insert specific tokens that trigger non-verbal elements, such as laughter, pauses, and interjections, during the synthesis process. The project includes a streaming audio generato

llmware is a Python framework for AI agent orchestration and model management, designed to coordinate multi-model workflows and autonomous agents. It provides a unified model catalog and standardized interface to execute specialized language models for complex research, analysis, and structured data generation. The project distinguishes itself through its heavy emphasis on local execution and quantized inference, allowing models to run on private infrastructure using CPU, GPU, and NPU acceleration via runtimes like ONNX and OpenVino. It features a specialized ability to translate natural lang