moonshine-ai/moonshine Alternatives

choose when you need a mature, lightweight, Apache-2.0 on-device speech toolkit with STT, TTS, VAD, diarization, and broader platform support.

Speech-to-text, text-to-speech, speaker diarization, speech enhancement, source separation, and VAD using next-gen Kaldi with onnxruntime without Internet connection. Support embedded systems, Android, iOS, HarmonyOS, Raspberry Pi, RISC-V, RK NPU, Axera NPU, Ascend NPU, x86_64 servers, websocket server/client, support 12 programming languages

choose when you need a PyTorch research and training toolkit to build custom speech models rather than use pre-trained on-device models.

A PyTorch-based Speech Toolkit

choose when you need a broad end-to-end speech processing framework for training ASR/TTS/translation systems on your own data.

End-to-End Speech Processing Toolkit

choose when you need a production ASR pipeline with streaming, VAD, and OpenAI-compatible serving, and you can run server-side.

Open-source speech recognition toolkit for training, inference, streaming ASR, VAD, punctuation, speaker diarization pipelines, and OpenAI-compatible/MCP serving.

choose when you need a scalable generative AI framework with ASR/TTS and LLM integration for researchers and cloud deployments.

A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)

choose when you want an easy-to-use all-in-one speech toolkit with SOTA ASR/TTS and a Chinese-friendly stack.

Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation and Keyword Spotting. Won NAACL2022 Best Demo Award.

choose when you're building Apple-only Swift/CoreML apps and want native audio models without a cross-platform dependency.

Frontier CoreML audio models in your apps — text-to-speech, speech-to-text, voice activity detection, and speaker diarization. In Swift, powered by SOTA open source.

choose when you're on Apple Silicon and want MLX/CoreML-based ASR/TTS speech-to-speech with tight Swift integration.

AI speech toolkit for Apple Silicon — ASR, TTS, speech-to-speech, VAD, and diarization powered by MLX and CoreML

choose when you need a production-ready streaming ASR toolkit focused on deployment and Mandarin/English support.

Production First and Production Ready End-to-End Speech Recognition Toolkit

choose when you need a TensorFlow-based framework for sequence-to-sequence speech research and experimentation.

Lingvo

choose when you need simultaneous/offline speech recognition and synthesis in one research model and can run on GPU.

StreamSpeech is an “All in One” seamless model for offline and simultaneous speech recognition, speech translation and speech synthesis.

choose when you need an ultra-lightweight ncnn-based ASR/VAD for embedded devices and don't need TTS or intent.

Real-time speech recognition and voice activity detection (VAD) using next-gen Kaldi with ncnn without Internet connection. Support iOS, Android, Linux, macOS, Windows, Raspberry Pi, VisionFive2, LicheePi4A etc.

choose when you want a simple, established offline ASR API for mobile/server and don't need TTS or intent.

Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node

choose when you need a compact multilingual ASR model with emotion/language ID and no TTS/intent.

Open-source SenseVoiceSmall model for Mandarin, Cantonese, English, Japanese, and Korean ASR, language ID, emotion recognition, and audio event detection.

choose when you want a local STT-only tool with LLM post-processing in Rust.

Fully local, private and cross platform Speech-to-Text with LLM Post-processing

choose when you need a tiny embedded Python voice-interface library and can accept an unmaintained, archived project.

Spokestack is a library that allows a user to easily incorporate a voice interface into any Python application with a focus on embedded systems.