GUI for a Vocal Remover that uses Deep Neural Networks.
- vocal-remover
- pytorch
- source
- separation
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GUI for a Vocal Remover that uses Deep Neural Networks.
Datasets, Transforms and Models specific to Computer Vision
Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are committed to automating these high-value generic R&D processes through R&D-Agent, which lets AI drive data-driven AI. 🔗https://aka.ms/RD-Agent-Tech-Report
Unified framework for building enterprise RAG pipelines with small, specialized models
Serve large Qwen models fast on the GPUs you actually own. Qwen3.8-27B on a single 24 GB card with vLLM: 127 tok/s single-user (381 when the answer quotes the prompt), ~1,035 tok/s at 64 concurrent, 150k-262k context. vLLM patches, requant pipeline, benchmarks.
NFStream: a Flexible Network Data Analysis Framework.
Audio processing by using pytorch 1D convolution network
kapre: Keras Audio Preprocessors
Seamless long-form MiniMax H3 video generation for ComfyUI with native AV latent continuity, chunked sampling, session resume, and Spectrum/Sage/Sol compatibility.
Evidence-aware Japanese speech visualization: pitch and intonation, waveform inspection, and experimental alignment.
A lightweight Python package for teaching computational methods through art history. Extract and analyze artist-specific works from WikiArt with built-in visualizations. Designed for digital humanities and computational design courses. Features genre/style analysis, matplotlib integration, and pedagogical clarity over algorithmic complexity.