Showing 4 open source projects for "gpu speed"

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  • 1
    The SpeechBrain Toolkit

    The SpeechBrain Toolkit

    A PyTorch-based Speech Toolkit

    SpeechBrain is an open-source and all-in-one conversational AI toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. SpeechBrain supports state-of-the-art methods for end-to-end speech recognition, including models based on CTC, CTC+attention, transducers, transformers, and neural language models relying on recurrent neural networks and transformers. Speaker recognition is already deployed in a...
    Downloads: 2 This Week
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  • 2
    VoiceCommander 2.0 Multilingual Offline

    VoiceCommander 2.0 Multilingual Offline

    2.0 Offline multilingual voice control for Windows. Fast, private

    ...Features: - 100% offline (no cloud, no data sharing) - Multilingual voice commands - Control apps, browser, and system - OCR + text-to-speech screen reading - Portable (no installation) - No registry changes NVIDIA GPU recommended (GTX 1060+). CPU mode supported (slower). For any other information please contact : Ducktheapp
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    Downloads: 3 This Week
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  • 3
    VoiceCommander Multilingual Offline

    VoiceCommander Multilingual Offline

    Offline multilingual voice control for Windows. Fast, private, local.

    ...Features: - 100% offline (no cloud, no data sharing) - Multilingual voice commands - Control apps, browser, and system - OCR + text-to-speech screen reading - Portable (no installation) - No registry changes NVIDIA GPU recommended (GTX 1060+). CPU mode supported (slower). A practical alternative to cloud-based voice assistants. For any other information please contact : ducktheapp@gmail.com The Duck
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    Downloads: 1 This Week
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  • 4
    Tensorpack

    Tensorpack

    A Neural Net Training Interface on TensorFlow, with focus on speed

    Tensorpack is a neural network training interface based on TensorFlow v1. Uses TensorFlow in the efficient way with no extra overhead. On common CNNs, it runs training 1.2~5x faster than the equivalent Keras code. Your training can probably gets faster if written with Tensorpack. Scalable data-parallel multi-GPU / distributed training strategy is off-the-shelf to use. Squeeze the best data loading performance of Python with tensorpack.dataflow. Symbolic programming (e.g. tf.data) does not...
    Downloads: 0 This Week
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