speech processing, speech recognition, speech synthesis, speech translation
ESPnet is an end-to-end speech processing toolkit covering ASR, TTS, speech translation, speaker diarization, and other tasks for research and production.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
Choose when you prefer the PaddlePaddle framework and want a comparable all-in-one speech toolkit with active maintenance.
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 want a modular, PyTorch-based speech toolkit with a more research-oriented interface than ESPnet's recipe-based system.
A PyTorch-based Speech Toolkit
Choose when you need a production-grade speech AI framework integrated with NeMo for large-scale training and deployment.
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)
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose when you need extremely low latency ASR/TTS for voice agents with C++ inference, sacrificing training flexibility and breadth.
Very low latency speech to text, intent recognition, and text to speech, for building voice agents and interfaces
Choose when you need offline on-device speech tasks with ONNX runtime for embedded systems, as a lightweight inference-only alternative.
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 prefer TensorFlow for building custom sequence models and want a lower-level framework rather than a speech-specific toolkit.
Lingvo
Choose when you need a seamless unified model for offline/simultaneous ASR, translation, and synthesis to avoid using separate ESPnet components.
StreamSpeech is an “All in One” seamless model for offline and simultaneous speech recognition, speech translation and speech synthesis.
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Choose when you only need production-ready streaming ASR with simpler deployment than ESPnet and don't require other speech tasks.
Production First and Production Ready End-to-End Speech Recognition Toolkit
Choose when you want a focused ASR pipeline with VAD, punctuation, and serving, but don't need TTS or other ESPnet features.
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 tokenizer-free multilingual TTS model for creative voice design and cloning, more specialized than ESPnet's TTS.
VoxCPM2: Tokenizer-Free TTS for Multilingual Speech Generation, Creative Voice Design, and True-to-Life Cloning
Choose when you need high-quality voice cloning TTS for many languages without building a full ESPnet pipeline.
High-Quality Voice Cloning TTS for 600+ Languages
Choose when you only need TTS and want a mature, battle-tested library, even though it's not as broad or recently updated as ESPnet.
🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
Choose when you need lightweight offline ASR APIs for mobile/servers without training, but only for speech recognition.
Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node
Choose when you focus solely on audio/music/speech generation and want a research toolkit for that subset, not recognition.
Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development.
Choose when you need a prompt-controlled multi-voice TTS engine and ESPnet's TTS doesn't fit your specific needs.
EmotiVoice 😊: a Multi-Voice and Prompt-Controlled TTS Engine
Choose when you need a foundational expressive TTS model specifically, without the broader tools of ESPnet.
Foundational model for human-like, expressive TTS
Choose when you need zero-shot multi-speaker TTS and voice conversion with a lightweight model, not a full suite.
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone
Choose when you need a fast, local neural TTS system for offline use with a simple CLI, even though it's archived and only handles TTS.
A fast, local neural text to speech system
Choose when you need a small multilingual ASR model with emotion detection and language ID, without ESPnet's full pipeline.
Open-source SenseVoiceSmall model for Mandarin, Cantonese, English, Japanese, and Korean ASR, language ID, emotion recognition, and audio event detection.
Choose when you want the VITS architecture for TTS as a standalone model, instead of using ESPnet's integrated environment.
VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
Does the same job, but ships as an app. Useful to a person, not swappable into a codebase.
Choose when building Apple apps that require CoreML-based speech models, as a platform-specific wrapper.
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 building Swift/Apple Silicon apps needing native ASR/TTS with MLX/CoreML, not a cross-platform toolkit.
AI speech toolkit for Apple Silicon — ASR, TTS, speech-to-speech, VAD, and diarization powered by MLX and CoreML
Choose when you want a ready-to-use GUI for few-shot voice cloning without integrating a full speech toolkit.
1 min voice data can also be used to train a good TTS model! (few shot voice cloning)
Choose when you need a lightweight demo/GUI for voice cloning; it's an application, not a maintainable library.
Clone a voice in 5 seconds to generate arbitrary speech in real-time
Choose when you want a simple voice-cloning GUI; it's a demo application, not a library to integrate.
🚀Clone a voice in 5 seconds to generate arbitrary speech in real-time
These projects were analysed and named espnet among their alternatives. The relationship is not symmetric — how espnet rates them is a separate judgement, made when espnet is analysed in its own right.
calls espnet “An end-to-end speech processing toolkit; choose it if you want a comprehensive PyTorch framework covering ASR and TTS.”
🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
calls espnet “choose for broad end-to-end speech task coverage and reproducible PyTorch recipes.”
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)
calls espnet “PyTorch-based end-to-end speech toolkit covering ASR, TTS, ST, and more; choose it if you want a non-Paddle implementation with broad community support.”
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.
calls espnet “Choose when you prefer ESPnet's mature, recipe-driven PyTorch toolkit with extensive ASR/TTS support.”
A PyTorch-based Speech Toolkit
calls espnet “Choose for a research-oriented end-to-end speech processing toolkit with many ASR recipes and model architectures.”
Robust Speech Recognition via Large-Scale Weak Supervision
calls espnet “Choose if you want a comprehensive end-to-end speech toolkit that supports TTS recipes; heavier than MockingBird.”
🚀Clone a voice in 5 seconds to generate arbitrary speech in real-time
calls espnet “Choose for a comprehensive speech processing toolkit with TTS recipes.”
SoTA open-source TTS
calls espnet “choose for a research-oriented end-to-end speech toolkit with extensive recipe support.”
Open-source speech recognition toolkit for training, inference, streaming ASR, VAD, punctuation, speaker diarization pipelines, and OpenAI-compatible/MCP serving.
calls espnet “Use when you need a flexible end-to-end speech processing toolkit for research and training across many tasks, not a lightweight edge runtime.”
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
calls espnet “End-to-end speech processing toolkit with translation support, if you want a research-oriented framework.”
Foundational Models for State-of-the-Art Speech and Text Translation
calls espnet “Choose when you need an end-to-end speech processing toolkit for research/training with TTS components.”
Use Microsoft Edge's online text-to-speech service from Python WITHOUT needing Microsoft Edge or Windows or an API key
calls espnet “choose when you need a broad end-to-end speech processing framework for training ASR/TTS/translation systems on your own data.”
Very low latency speech to text, intent recognition, and text to speech, for building voice agents and interfaces
calls espnet “when you need a research speech-processing toolkit to build custom pipelines; not ready-made”
Real-time, local speech-to-text with streaming ASR, speaker diarization, translation, and OpenAI/Deepgram-compatible APIs.
calls espnet “Choose when you need a mature, all-in-one speech processing toolkit with extensive recipes and research models, similar to Amphion.”
Amphion (/æmˈfaɪən/) is a toolkit for Audio, Music, and Speech Generation. Its purpose is to support reproducible research and help junior researchers and engineers get started in the field of audio, music, and speech generation research and development.
calls espnet “Use for a broad speech-processing framework with TTS recipes, if you want many tools.”
High-Quality Voice Cloning TTS for 600+ Languages
calls espnet “an end-to-end speech processing toolkit with many recipes; choose for research/custom training.”
Open-source SenseVoiceSmall model for Mandarin, Cantonese, English, Japanese, and Korean ASR, language ID, emotion recognition, and audio event detection.
calls espnet “choose for academic experiments needing many speech tasks; it is heavier.”
EmotiVoice 😊: a Multi-Voice and Prompt-Controlled TTS Engine
calls espnet “choose for ESPnet research recipes covering TTS/ASR rather than a single-model implementation.”
VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
calls espnet “Choose when you need an end-to-end speech processing toolkit supporting ASR/TTS with extensive recipes.”
A text-to-speech (TTS), speech-to-text (STT) and speech-to-speech (STS) library built on Apple's MLX framework, providing efficient speech analysis on Apple Silicon.
calls espnet “End-to-end speech processing toolkit with many TTS recipes; choose it for a research framework with broad support.”
High-quality multi-lingual text-to-speech library by MyShell.ai. Support English, Spanish, French, Chinese, Japanese and Korean.