Search Results for "ffdshow audio decoder" - Page 4

Showing 80 open source projects for "ffdshow audio decoder"

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  • 1
    The goal of the Decals project was to create an open source MPEG-4 ALS audio decoder licensed under the LGPL. That is not necessary now that there is an LGPL ALS decoder in FFmpeg. Yay!
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  • 2
    A audio player can rolled display lyrics while playing a music for Linux. This player uses javazoom decoder as the core decoder. All these base on Java, interface with SWT implemented.(Linux + Java)
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  • 3
    MiMo-V2.6-Flash

    MiMo-V2.6-Flash

    Efficient 309B omnimodal MoE for coding, agents, vision, and audio

    ...A five-layer speculative decoder predicts multiple subsequent tokens to accelerate inference.
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  • 4
    MiMo-V2.6-Pro

    MiMo-V2.6-Pro

    1T omnimodal MoE model for coding, agents, and long-horizon reasoning

    ...Its sparse Mixture-of-Experts architecture contains 1.02T total parameters with 42B activated per token, using 384 routed experts with eight active per token. The model natively processes text, images, video, and audio and supports a 1M-token context window for large repositories, extended tool traces, and multi-session agent workflows. MiMo-V2.6-Pro-RL uses a unified mixed reinforcement learning process rather than separate domain-specific runs, alongside groupwise agentic grading that rewards higher-quality and more efficient solutions. Its architecture combines sliding-window and global attention, a 681M-parameter vision encoder, dedicated audio encoders, and a five-layer multi-token speculative decoder. ...
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  • 5
    Inkling-Small

    Inkling-Small

    Efficient multimodal MoE model for coding, tools, and reasoning

    Inkling-Small is an open-weight general-purpose multimodal model from Thinking Machines Lab, designed for agentic systems, coding assistants, chatbots, retrieval workflows, and natural-language applications. It accepts text, images, and audio as input and produces text output, with multilingual and multi-programming-language capabilities. The model uses a sparse Mixture-of-Experts architecture with 276B total parameters and 12B active per token, enabling strong performance with lower inference cost than a fully dense model of similar scale. Its 42-layer decoder routes each token through six of 256 specialized experts plus two shared experts, while hybrid local and global attention supports efficient processing. ...
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