VoxCPM

VoiceStudio is an open-source, fully local desktop application for voice cloning and design, text-to-speech, transcription, dictation, video dubbing, and long-form audio production. It runs on macOS Apple Silicon, Windows, Linux, or Docker and keeps voices, projects, settings, and generated files on the machine by default. The application combines a Tauri desktop shell, a React and Vite interface, and a local FastAPI backend with registries for multiple TTS and ASR engines. Its workflows can transcribe and translate video, preserve speaker assignments, synthesize replacement speech, render audiobook chapters, separate vocals, diarize speakers, process batch jobs, and route work to CPU, CUDA, Apple Silicon MPS/MLX, ROCm, or optional remote workers. It also exposes local REST, SSE, WebSocket, OpenAI-compatible audio, and MCP interfaces, including speech synthesis and transcription endpoints. VoiceStudio is licensed under AGPL-3.0; downloaded models and tokenizers retain their own licenses. The local workflow requires no account, API key, subscription, or usage meter, although remote workers, configured external ASR endpoints, analytics, and other network-backed functions are opt-in. The repository describes the software as an active beta and distributes packaged desktop releases as well as source code.

Speech To Speech is an open-source, modular voice-agent pipeline from Hugging Face. It chains voice activity detection, speech-to-text, a language model, and text-to-speech as separate stages connected by queues; each stage has interchangeable backends. The language-model stage supports OpenAI-compatible protocols and can use hosted providers, Hugging Face Inference Providers, vLLM, or llama.cpp, while the system streams transcripts, generated text, tool calls, and synthesized audio. The project exposes the core OpenAI Realtime event set through WebSocket and WebRTC and includes commands for running a realtime server, a microphone-and-speaker client, or a local setup. It is distributed as a Python package and requires Python 3.10 or newer. The repository says the pipeline is used as the conversation backend for Reachy Mini robots.

Moonshine Voice is an open-source AI toolkit for developers building real-time voice agents and applications. It provides on-device speech-to-text, intent recognition, and text-to-speech, including streaming transcription that processes audio while the user is still speaking. The project offers speech-to-text models ranging from higher-accuracy models to approximately 1 MB models and provides one library for Python, JavaScript/WASM, iOS, Android, macOS, Linux, Windows, and Raspberry Pi. It is used by Gemma Translator for speech recognition and speech output, and can run without an account or API keys. The project is distributed under the MIT License, including its models by default, with legacy non-streaming models for non-English languages covered by a non-commercial Moonshine Community License.

Vois is a local AI voice-generation and audio-processing studio for writing, casting, voice selection and cloning, multi-speaker scripts, editing, and mastering. It provides local text-to-speech, more than 100 natural voices, audio tools for podcasts, audiobooks, and video, and integrations with AI agents; its Pro plan adds Omni, more than 600 languages, and Voice Design.

whisper.cpp is a C/C++ implementation of OpenAI's Whisper automatic speech-recognition model for speech-to-text inference. It is designed for offline, on-device use and supports CPU-only inference, GPU and NPU acceleration, integer quantization, mixed F16/F32 precision, voice activity detection, and a C-style API. The implementation targets macOS, iOS, Android, Linux, Windows, WebAssembly, Raspberry Pi, Docker, and other platforms, with optimizations including ARM NEON, Apple's Accelerate and Metal frameworks, x86 AVX, POWER VSX, Vulkan, CUDA-related NVIDIA support, ROCm, OpenVINO, and other backends. The repository includes the whisper-cli example, which transcribes 16-bit WAV audio using Whisper models converted to ggml format; its zero-dependency implementation is intended for integration into applications such as on-device captioning tools.