Search Results for "separation"
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Showing 25 open source projects for "separation"
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ClouDNS provides fast and secure Free DNS Hosting and Premium DNS Hosting with Global Anycast DNS Network.ClouDNS is the biggest European provider of globally Managed DNS services, including GeoDNS, Anycast DNS and DDoS protected DNS. We also offer Domain names, Monitoring services, SSL Certificates, and Google Workspace.
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Ultimate Vocal Remover (UVR5)
GUI for a Vocal Remover that uses Deep Neural Networks
This application uses state-of-the-art source separation models to remove vocals from audio files. UVR's core developers trained all of the models provided in this package (except for the Demucs v3 and v4 4-stem models). -
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Voice-Pro
Comprehensive Gradio WebUI for audio processing
Voice-Pro is the best gradio WebUI for transcription, translation and text-to-speech. It can be easily installed with one click. Create a virtual environment using Miniconda, running completely separate from the Windows system (fully portable). Supports real-time transcription and translation, as well as batch mode. -
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audioFlux
A library for audio and music analysis, feature extraction
...It supports dozens of time-frequency analysis transformation methods and hundreds of corresponding time-domain and frequency-domain feature combinations. It can be provided to deep learning networks for training and is used to study various tasks in the audio field such as Classification, Separation, Music Information Retrieval(MIR) ASR, etc. -
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Spice.ai OSS
A self-hostable CDN for databases
Spice is a portable runtime offering developers a unified SQL interface to materialize, accelerate, and query data from any database, data warehouse, or data lake. Spice connects, fuses, and delivers data to applications, machine-learning models, and AI backends, functioning as an application-specific, tier-optimized Database CDN. The Spice runtime, written in Rust, is built-with industry-leading technologies such as Apache DataFusion, Apache Arrow, Apache Arrow Flight, SQLite, and DuckDB.... -
For every need the right PDF solutionPDFCreator converts every printable document to PDF and many other formats. Convert your documents to PDF, JPG, PNG, TIF and more. Merge multiple documents to one file. Use automatic saving to have a fully automated PDF printer. Profiles make frequently used settings available with one click. We take care of the complexity and make converting PDFs simple for you.
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DoWhy
DoWhy is a Python library for causal inference
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks. Much like machine learning libraries have done for prediction, DoWhy is a Python library that aims to spark causal thinking and analysis. DoWhy provides a wide variety of algorithms for effect estimation, causal structure learning, diagnosis of causal... -
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ChatGPT Clone
ChatGPT interface with better UI
...The goal is to replicate the core chat UXâmessage history, streaming tokens, code blocks, and system promptsâwhile letting you plug in different provider APIs or local models. It showcases a clean separation between the web client and the message orchestration layer so you can experiment with prompts, roles, and memory strategies. The project is useful for prototyping assistants, documentation bots, and internal developer tools without committing to a specific vendor or UI framework. Configuration is kept simple so newcomers can get a working chat in minutes and then dial in features like authentication or multi-model routing. ... -
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TTS WebUI
A single Gradio + React WebUI with extensions for ACE-Step
TTS-WebUI is a unified Gradio + React web interface that brings together a large ecosystem of text-to-speech, voice conversion, and audio generation models under a single UI. It supports a wide range of models such as Bark, MusicGen, Tortoise, RVC, StyleTTS2, ParlerTTS, CosyVoice, XTTSv2, Stable Audio, SeamlessM4T, and many others, exposing them as interchangeable backends for speech and music synthesis. The project provides an installer that sets up Conda, Python environments, and all... -
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Hamilton DAGWorks
Helps scientists define testable, modular, self-documenting dataflow
Hamilton is a lightweight Python library for directed acyclic graphs (DAGs) of data transformations. Your DAG is portable; it runs anywhere Python runs, whether it's a script, notebook, Airflow pipeline, FastAPI server, etc. Your DAG is expressive; Hamilton has extensive features to define and modify the execution of a DAG (e.g., data validation, experiment tracking, remote execution). To create a DAG, write regular Python functions that specify their dependencies with their parameters. As... -
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NeMo Curator
Scalable data pre processing and curation toolkit for LLMs
NeMo Curator is a Python library specifically designed for fast and scalable dataset preparation and curation for large language model (LLM) use-cases such as foundation model pretraining, domain-adaptive pretraining (DAPT), supervised fine-tuning (SFT) and paramter-efficient fine-tuning (PEFT). It greatly accelerates data curation by leveraging GPUs with Dask and RAPIDS, resulting in significant time savings. The library provides a customizable and modular interface, simplifying pipeline... -
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Generative AI Swift
This SDK is now deprecated, use the unified Firebase SDK
deprecated-generative-ai-swift is a Swift client and example scaffold for building generative AI apps using the Gemini models. Although marked âdeprecatedâ, the repo demonstrates how to integrate Gemini inference into iOS and macOS apps via Swift APIs, providing boilerplate for prompt dispatching, streaming responses, UI integration, and error handling. It includes a sample app that showcases a chat interface, where users send messages and receive responses streamed in real time, with UI... -
11
Axon
Nx-powered Neural Networks
...Training API â An API for quickly training models, inspired by PyTorch Ignite. Axon provides abstractions that enable easy integration while maintaining a level of separation between each component. You should be able to use any of the APIs without dependencies on others. By decoupling the APIs, Axon gives you full control over each aspect of creating and training a neural network. At the lowest-level, Axon consists of a number of modules with functional implementations of common methods in deep learning. -
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The SpeechBrain Toolkit
A PyTorch-based Speech Toolkit
...SpeechBrain provides different models for speaker recognition, including X-vector, ECAPA-TDNN, PLDA, and contrastive learning. Spectral masking, spectral mapping, and time-domain enhancement are different methods already available within SpeechBrain. Separation methods such as Conv-TasNet, DualPath RNN, and SepFormer are implemented as well. SpeechBrain provides efficient and GPU-friendly speech augmentation pipelines and acoustic features extraction. -
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Asteroid
The PyTorch-based audio source separation toolkit for researchers
The PyTorch-based audio source separation toolkit for researchers. Pytorch-based audio source separation toolkit that enables fast experimentation on common datasets. It comes with a source code thats supports a large range of datasets and architectures, and a set of recipes to reproduce some important papers. Building blocks are thought and designed to be seamlessly plugged together. -
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Frevo
Frevo is probably the simplest tool for evolutionary design
FREVO is an open-source framework developed in Java to help engineers and scientists in evolutionary design or optimization tasks. The major feature of FREVO is the componentwise decomposition and separation of the key building blocks for each optimization tasks. We identify these as the problem definition, solution representation and the optimization method. This structure enables the components to be designed separately allowing the user to easily swap and evaluate different configurations and methods or to connect an external simulation tool. ... -
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MLP Network Configurator
interactive tool designed to help users understand how neural network
.... - Model Configuration: Define hidden layers, activation functions, loss and optimizer types. - Training: Visualize loss curves, predictions, PCA-based class separation. - Testing: Accuracy calculation, error graphs, confusion matrix, misclassified sample montage. - Camera Dataset Builder: Capture and label images live via webcam. - Model Insight: Animated weight & bias evolution, interactive heatmaps. - Save & Load: Export models and configurations for reuse and sharing. đž Donateware Model MLP Network Configurator is distributed as donateware: - The program is fully functional -
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Demucs
Code for the paper Hybrid Spectrogram and Waveform Source Separation
Demucs (Deep Extractor for Music Sources) is a deep-learning framework for music source separationâextracting individual instrument or vocal tracks from a mixed audio file. The system is based on a U-Net-like convolutional architecture combined with recurrent and transformer elements to capture both short-term and long-term temporal structure. It processes raw waveforms directly rather than spectrograms, allowing for higher-quality reconstruction and fewer artifacts in separated tracks. ... -
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ChatGPT UI
A ChatGPT web client that supports multiple users, and databases
A ChatGPT web client that supports multiple users, multiple database connections for persistent data storage, supports i18n. Provides Docker images and quick deployment scripts. Support gpt-4 model. You can select the model in the "Model Parameters" of the front-end. The GPT-4 model requires whitelist access from OpenAI. Added web search capability to generate more relevant and up-to-date answers from ChatGPT! This feature is off by default, you can turn it on in `Chat->Settings` in the... -
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simpleaichat
Python package for easily interfacing with chat apps
...It supports structured responses and validation patterns so your app can reliably parse model outputs instead of wrestling with brittle free-text parsing. The project encourages clean separation between system prompts, user messages, and tool outputs to keep conversations predictable. With convenience helpers for logging, environment configuration, and retries, it reduces the friction of moving from a quick experiment to a reliable internal tool. -
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audioFlux
A library for audio and music analysis, feature extraction.
...It supports dozens of time-frequency analysis transformation methods and hundreds of corresponding time-domain and frequency-domain feature combinations. It can be provided to deep learning networks for training, and is used to study various tasks in the audio field such as Classification, Separation, Music Information Retrieval(MIR) and ASR etc. -
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tf2_course
Notebooks for my "Deep Learning with TensorFlow 2 and Keras" course
tf2_course provides the notebooks for the âDeep Learning with TensorFlow 2 and Kerasâ course authored by the same author, AurĂ©lien GĂ©ron. It is structured as a teaching toolkit: youâll find notebooks covering neural networks with Keras, lower-level TensorFlow APIs, data loading & preprocessing, convolutional and recurrent networks, and deployment/distribution of models. The material is intended for learners who already have foundational knowledge of ML and wish to deepen their understanding... -
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Music Source Separation
Separate audio recordings into individual sources
Music Source Separation is a PyTorch-based open-source implementation for the task of separating a music (or audio) recording into its constituent sources â for example isolating vocals, instruments, bass, accompaniment, or background from a mixed track. It aims to give users the ability to take any existing song and decompose it into separate stems (vocals, accompaniment, etc.), or to train custom separation models on their own datasets (e.g. for speech enhancement, instrument isolation, or other audio-separation tasks). ... -
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MAE (Masked Autoencoders)
PyTorch implementation of MAE
MAE (Masked Autoencoders) is a self-supervised learning framework for visual representation learning using masked image modeling. It trains a Vision Transformer (ViT) by randomly masking a high percentage of image patches (typically 75%) and reconstructing the missing content from the remaining visible patches. This forces the model to learn semantic structure and global context without supervision. The encoder processes only the visible patches, while a lightweight decoder reconstructs the... -
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SG2Im
Code for "Image Generation from Scene Graphs", Johnson et al, CVPR 201
...Instead of conditioning on free-form text alone, it leverages graph structure to control layout and interactions, generating scenes that respect constraints like âperson left of dogâ or âcup on table.â The pipeline typically predicts object layouts (bounding boxes and masks) from the graph, then renders a realistic image conditioned on those layouts. This separation lets the model reason about geometry and composition before committing to texture and color, improving spatial fidelity. The repository includes training code, datasets, and evaluation scripts so researchers can reproduce baselines and extend components such as the graph encoder or image generator. In practice, sg2im demonstrates how structured semantics can guide generative models to produce controllable, compositional imagery. -
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The source code has been migrated and is now hosted on Github: https://github.com/jenetics/jenetics Jenetics is an advanced Genetic Algorithm, Evolutionary Algorithm and Genetic Programming library, respectively, written in modern day Java. It is designed with a clear separation of the several algorithm concepts, e. g. Gene, Chromosome, Genotype, Phenotype, Population and fitness Function. Jenetics allows you to minimize or maximize the given fitness function without tweaking it. In contrast to other GA implementations, the library uses the concept of an evolution stream (EvolutionStream) for executing the evolution steps. ...
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Java Library for Machine Learning
A pure Java library for machine learning
JML is a machine learning library in Java, it is a pure Java package, and thus is cross-platform. The goal of JML is to make machine learning methods very easy to use and speed up code conversion from MATLAB to Java. Please be noted that JML has been replaced by LAML.
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