Showing 4 open source projects for "runtime onnx"

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  • 1
    kokoro-onnx

    kokoro-onnx

    TTS with kokoro and onnx runtime

    kokoro-onnx is a text-to-speech toolkit that wraps the Kokoro neural TTS model in an easy-to-use ONNX Runtime interface, so you can generate speech from Python with minimal setup. It focuses on running efficiently on commodity hardware, including macOS with Apple Silicon, while still delivering near real-time performance for many use cases. The project ships prebuilt model files and a simple example script, so you can go from installation to producing an audio.wav file in just a few steps. ...
    Downloads: 302 This Week
    Last Update:
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  • 2
    DragonianVoice

    DragonianVoice

    C++ inference library for multiple SVC/TTS

    ...It uses ONNX Runtime and other backends to accelerate inference, with notes on how different execution providers such as CUDA or DirectML affect operator support and numerical stability. Recent versions integrate with fish-speech via a dedicated fish-speech.cpp subproject using ggml.
    Downloads: 3 This Week
    Last Update:
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  • 3
    Supertonic

    Supertonic

    Lightning-fast, on-device TTS, running natively via ONNX

    Supertonic is a lightning-fast, on-device text-to-speech system built around ONNX Runtime for maximum speed and portability. It focuses on running entirely locally, eliminating the need for cloud APIs and providing low latency and strong privacy guarantees, even on constrained devices like Raspberry Pi boards and e-readers. The core model is highly compact at around 66 million parameters, yet benchmarks show it can generate speech up to 167× faster than real time on modern consumer hardware and significantly outpace popular cloud TTS APIs in throughput and real-time factor. ...
    Downloads: 1 This Week
    Last Update:
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  • 4
    Matcha-TTS

    Matcha-TTS

    A fast TTS architecture with conditional flow matching

    Matcha-TTS is a non-autoregressive neural text-to-speech architecture that uses conditional flow matching to generate speech quickly while maintaining natural quality. It models speech as an ODE-based generative process, and conditional flow matching lets it reach high-quality audio in only a few synthesis steps, which greatly reduces latency compared to score-matching diffusion approaches. The model is fully probabilistic, so it can generate diverse realizations of the same text while still...
    Downloads: 6 This Week
    Last Update:
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