Showing 5 open source projects for "embedded c"

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  • 1
    Moonshine Voice

    Moonshine Voice

    Fast and accurate automatic speech recognition (ASR) for edge devices

    moonshine is an open-source automatic speech recognition toolkit optimized for fast and accurate transcription on edge devices and local environments. The project is designed to enable real-time voice applications such as live transcription, voice commands, and embedded speech interfaces without requiring heavy cloud infrastructure. Its architecture emphasizes low latency and flexible input handling, allowing audio streams of varying durations rather than relying on fixed processing windows....
    Downloads: 6 This Week
    Last Update:
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  • 2
    DeepSpeech

    DeepSpeech

    Open source embedded speech-to-text engine

    DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers. DeepSpeech is an open-source Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow to make the implementation easier. A pre-trained English model is available for use and can be downloaded following the...
    Downloads: 17 This Week
    Last Update:
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  • 3

    Audacity-Extra

    dark themed version of free Audacity sound editor

    audacity-extra now provides a sleek dark themed version of the Audacity open source sound editor. The project experiments with Audacity variations. There's a vowel-sound target-practice display for language learners and an analog waveform data logger for embedded systems.
    Downloads: 0 This Week
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  • 4
    A machine translation program designed to accept verbal or text input and provide text or speech synthesized voice translation as output. Makes use of 3 current open-source projects. The source is currently C/C++ and embedded perl.
    Downloads: 0 This Week
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  • 5
    This project will show how to implement the Hidden Markov Model approximations of Voice Recognition into embedded and low power systems.
    Downloads: 0 This Week
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