Open Source Python Artificial Intelligence Software - Page 6
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Python Artificial Intelligence Software
View 13659 business solutionsBrowse free open source Python Artificial Intelligence Software and projects below. Use the toggles on the left to filter open source Python Artificial Intelligence Software by OS, license, language, programming language, and project status.
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Premier Construction SoftwareRated #1 Construction Accounting Software by Forbes Advisor in 2022 & 2023. Our modern SAAS solution is designed to meet the needs of General Contractors, Developers/Owners, Homebuilders & Specialty Contractors.
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Failed Payment Recovery for Subscription BusinessesFlexPay’s innovative platform uses multiple technologies to achieve the highest number of retained customers, resulting in reduced involuntary churn, longer life span after recovery, and higher revenue. Leading brands like LegalZoom, Hooked on Phonics, and ClinicSense trust FlexPay to recover failed payments, reduce churn, and increase customer lifetime value.
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HeartMuLa
A Family of Open Sourced Music Foundation Models
HeartMuLa is the open-source library and reference implementation for the HeartMuLa family of music foundation models, designed to support both music generation and music-related understanding tasks in a cohesive stack. At the center is HeartMuLa, a music language model that generates music conditioned on inputs like lyrics and tags, with multilingual support that broadens the range of lyric-driven use cases. The project also includes HeartCodec, a music codec optimized for high reconstruction fidelity, enabling efficient tokenization and reconstruction workflows that are critical for training and generation pipelines. For text extraction from audio, it provides HeartTranscriptor, a Whisper-based model tuned specifically for lyrics transcription, which helps bridge generated or recorded audio back into structured text. It also introduces HeartCLAP, which aligns audio and text into a shared embedding space. -
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Kitten TTS
State-of-the-art TTS model under 25MB
KittenTTS is an open-source, ultra-lightweight, and high-quality text-to-speech model featuring just 15 million parameters and a binary size under 25 MB. It is designed for real-time CPU-based deployment across diverse platforms. Ultra-lightweight, model size less than 25MB. CPU-optimized, runs without GPU on any device. High-quality voices, several premium voice options available. Fast inference, optimized for real-time speech synthesis. -
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WhisperJAV
Uses Qwen3-ASR, local LLM, Whisper, TEN-VAD
WhisperJAV is an open-source speech transcription pipeline designed specifically for generating subtitles for Japanese adult video content. The project addresses challenges that standard speech recognition models face when transcribing this type of audio, which often includes low signal-to-noise ratios and large numbers of non-verbal vocalizations. Traditional automatic speech recognition systems can misinterpret these sounds as words, leading to inaccurate transcripts. WhisperJAV introduces a specialized pipeline that separates text generation from timestamp alignment, allowing the system to generate transcripts and then align them with audio using forced alignment techniques. The framework supports several speech recognition models, including Qwen-based ASR systems and fine-tuned Whisper models trained on domain-specific dialogue. -
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EPUB to Audiobook Converter
EPUB to audiobook converter, optimized for Audiobookshelf
EPUB to Audiobook Converter is a tool designed to convert EPUB ebooks into chaptered audiobooks, optimized specifically for Audiobookshelf servers. It reads each chapter from an EPUB file, generates audio using a chosen text-to-speech backend, and outputs separate MP3 files with chapter titles preserved as metadata to make navigation easier. The project supports multiple TTS providers, including Microsoft Azure TTS, EdgeTTS, OpenAI TTS, local Piper, and Kokoro via an OpenAI-compatible endpoint, allowing users to choose between cloud and self-hosted voices. A recent addition is a Gradio-based WebUI, which wraps all configuration options in a graphical interface for users who prefer not to work with the command line. The tool offers advanced options such as controlling chapter ranges, handling paragraph detection via newline modes, removing endnote markers, and using regex-based search-and-replace files to tweak pronunciations. It can be run directly with Python or via Docker. -
The full-stack observability platform that protects your dataLayer, tags and conversion dataCode-Cube.io detects issues instantly, alerts you in real time and helps you resolve them fast. No manual QA. No unreliable data. Just data you can trust and act on.
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HY-World 1.5
A Systematic Framework for Interactive World Modeling
HY-WorldPlay is a Hunyuan AI project focusing on immersive multimodal content generation and interaction within virtual worlds or simulated environments. It aims to empower AI agents with the capability to both understand and generate multimedia content — including text, audio, image, and potentially 3D or game-world elements — enabling lifelike dialogue, environmental interpretations, and responsive world behavior. The platform targets use cases in digital entertainment, game worlds, training simulators, and interactive storytelling, where AI agents need to adapt to real-time user inputs and changes in environment state. It blends advanced reasoning with multimodal synthesis, enabling agents to describe scenes, generate context-appropriate responses, and contribute to narrative or gameplay flows. The underlying framework typically supports large-context state tracking across extended interactions, blending temporal and spatial multimodal signals. -
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HackerRepo.org
Collection of cybersecurity-related references, scripts, tools, code
HackerRepo is a massive curated repository that aggregates thousands of cybersecurity, ethical hacking, and digital forensics resources into a single structured knowledge base. The project is designed as a companion learning hub for security professionals, penetration testers, and researchers who want organized access to tools, references, and training material. It spans both offensive and defensive security topics, including exploit development, threat hunting, reverse engineering, AI security, and bug bounty methodologies. The repository is continuously maintained and categorized into specialized directories so users can quickly locate relevant learning material or utilities. Rather than being a single tool, it functions as an extensive reference library that supports skill development across the entire cybersecurity lifecycle. Overall, h4cker serves as a comprehensive open knowledge repository for practitioners building or expanding professional security expertise. -
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Nerve
The Simple Agent Development Kit
Nerve is a developer-friendly Agent Development Kit (ADK) that utilizes YAML and a CLI to define, run, orchestrate, and evaluate LLM-driven agents. It supports declarative setups, tool integration, workflow pipelines, and both MCP client and server roles. Nerve is a simple yet powerful Agent Development Kit (ADK) to build, run, evaluate, and orchestrate LLM-based agents using just YAML and a CLI. It’s designed for technical users who want programmable, auditable, and reproducible automation using large language models. Define agents using a clean YAML format: system prompt, task, tools, and variables — all in one file. -
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Qwen3-Coder
Qwen3-Coder is the code version of Qwen3
Qwen3-Coder is the latest and most powerful agentic code model developed by the Qwen team at Alibaba Cloud. Its flagship version, Qwen3-Coder-480B-A35B-Instruct, features a massive 480 billion-parameter Mixture-of-Experts architecture with 35 billion active parameters, delivering top-tier performance on coding and agentic tasks. This model sets new state-of-the-art benchmarks among open models for agentic coding, browser-use, and tool-use, matching performance comparable to leading models like Claude Sonnet. Qwen3-Coder supports an exceptionally long context window of 256,000 tokens, extendable to 1 million tokens using Yarn, enabling repository-scale code understanding and generation. It is capable of handling 358 programming languages, from common to niche, making it versatile for a wide range of development environments. The model integrates a specially designed function call format and supports popular platforms such as Qwen Code and CLINE for agentic coding workflows. -
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SD.Next
All-in-one WebUI for AI generative image and video creation
SD.Next is an all-in-one web user interface for generative image creation that expands beyond basic Stable Diffusion workflows to cover broader image and video generation, captioning, and processing tasks. It is designed as a power-user environment where model management, generation features, and workflow controls are centralized in a single UI rather than spread across separate scripts and utilities. The project emphasizes broad model support and includes mechanisms for discovering, downloading, and configuring models through integrated tooling, lowering the setup burden for experimentation. It also provides documentation and an ecosystem of guides that help users move from basic generation to more advanced usage patterns, including API-based automation. SD.Next is built to run across common desktop platforms and focuses on practicality: install, generate, iterate, and automate with minimal friction. -
AestheticsPro Medical Spa SoftwareAestheticsPro is the most complete Aesthetics Software on the market today. HIPAA Cloud Compliant with electronic charting, integrated POS, targeted marketing and results driven reporting; AestheticsPro delivers the tools you need to manage your medical spa business. It is our mission To Provide an All-in-One Cutting Edge Software to the Aesthetics Industry.
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Transformers
State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX
Hugging Face Transformers provides APIs and tools to easily download and train state-of-the-art pre-trained models. Using pre-trained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch. These models support common tasks in different modalities. Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages. Images, for tasks like image classification, object detection, and segmentation. Audio, for tasks like speech recognition and audio classification. Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments. -
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pyttsx3
Offline Text To Speech synthesis for python
pyttsx3 is an offline text-to-speech library for Python that wraps native speech engines instead of calling cloud APIs. It is designed to work entirely without an internet connection, making it suitable for local automation, kiosks, accessibility tools, and embedded applications. On Windows it uses SAPI5, on Linux it typically uses eSpeak or eSpeak-NG, and on macOS it can use NSSpeechSynthesizer or AVSpeechSynthesizer, giving it broad cross-platform compatibility. The library exposes a simple but flexible API for controlling voice selection, speaking rate, volume, and other synthesis parameters from Python code. It supports both a high-level speak convenience function and a lower-level engine object with event hooks, queuing, and saving output to audio files. The repository includes examples and documentation that show how to adjust properties dynamically, persist synthesized output, and integrate pyttsx3 into GUIs or background services. -
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CodeGeeX
CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023)
CodeGeeX is a large-scale multilingual code generation model with 13 billion parameters, trained on 850B tokens across more than 20 programming languages. Developed with MindSpore and later made PyTorch-compatible, it is capable of multilingual code generation, cross-lingual code translation, code completion, summarization, and explanation. It has been benchmarked on HumanEval-X, a multilingual program synthesis benchmark introduced alongside the model, and achieves state-of-the-art performance compared to other open models like InCoder and CodeGen. CodeGeeX also powers IDE plugins for VS Code and JetBrains, offering features like code completion, translation, debugging, and annotation. The model supports Ascend 910 and NVIDIA GPUs, with optimizations like quantization and FasterTransformer acceleration for faster inference. -
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LLaMA 3
The official Meta Llama 3 GitHub site
This repository is the former home for Llama 3 model artifacts and getting-started code, covering pre-trained and instruction-tuned variants across multiple parameter sizes. It introduced the public packaging of weights, licenses, and quickstart examples that helped developers fine-tune or run the models locally and on common serving stacks. As the Llama stack evolved, Meta consolidated repositories and marked this one deprecated, pointing users to newer, centralized hubs for models, utilities, and docs. Even as a deprecated repo, it documents the transition path and preserves references that clarify how Llama 3 releases map into the current ecosystem. Practically, it functioned as a bridge between Llama 2 and later Llama releases by standardizing distribution and starter code for inference and fine-tuning. Teams still treat it as historical reference material for version lineage and migration notes. -
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LMDeploy
LMDeploy is a toolkit for compressing, deploying, and serving LLMs
LMDeploy is a toolkit designed for compressing, deploying, and serving large language models (LLMs). It offers tools and workflows to optimize LLMs for production environments, ensuring efficient performance and scalability. LMDeploy supports various model architectures and provides deployment solutions across different platforms. -
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VoxCPM2
Tokenizer-Free TTS for Multilingual Speech Generation
VoxCPM2 is an advanced open-source text-to-speech system that redefines speech synthesis by eliminating traditional tokenization and instead generating continuous speech representations through a diffusion-based autoregressive architecture. Built on top of the MiniCPM model family, it enables highly natural, expressive, and context-aware speech generation that adapts tone, emotion, and pacing directly from input text. The system is trained on massive multilingual datasets, enabling support for dozens of languages and dialects while maintaining high fidelity and realism in generated audio. VoxCPM stands out for its ability to perform voice cloning with minimal input, capturing not only the speaker’s timbre but also nuanced features such as rhythm, accent, and emotional delivery. It also introduces voice design capabilities, allowing users to generate entirely new voices from natural language descriptions without requiring reference audio. -
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labelme Image Polygonal Annotation
Image polygonal annotation with Python
Labelme is a graphical image annotation tool. It is written in Python and uses Qt for its graphical interface. Image annotation for polygon, rectangle, circle, line and point. Image flag annotation for classification and cleaning. Video annotation. (video annotation). GUI customization (predefined labels / flags, auto-saving, label validation, etc). Exporting VOC-format dataset for semantic/instance segmentation. (semantic segmentation, instance segmentation). Exporting COCO-format dataset for instance segmentation. (instance segmentation). The first time you run labelme, it will create a config file in ~/.labelmerc. You can edit this file and the changes will be applied the next time that you launch labelme. If you would prefer to use a config file from another location, you can specify this file with the --config flag. -
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/last30days
Claude Code skill that researches any topic across Reddit + X
/last30days is a specialized Claude Code skill designed to research current trends and practices across Reddit, X, and the wider web from the last 30 days, synthesize that data, and produce copy-paste-ready prompts or summaries that reflect what the community is actually talking about now. Rather than returning generic model responses, it intelligently analyzes social media and community discussions to identify what’s genuinely trending or working in practice across topics ranging from prompt techniques to tool usage or cultural trends. This makes it particularly useful for prompt engineers, content creators, and developers who want up-to-date prompts and insights that align with the most recent consensus and shared best practices in fast-moving fields like AI tooling. -
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AIOGram
Framework for Telegram Bot API written in Python 3.7 with asyncio
aiogram is modern and fully asynchronous framework for Telegram Bot API written in Python with asyncio and aiohttp. It helps you to make your bots faster and simpler. Is a pretty simple and fully asynchronous framework for Telegram Bot API written in Python 3.7 with asyncio and aiohttp. -
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Agent Development Kit (ADK)
Open-source, code-first Python toolkit for building, evaluating, etc.
ADK (Android Device Key) Python is a reference implementation by Google for working with Android attestation keys in Python. It facilitates the integration of Android attestation features into backends or systems that require verification of device identity and integrity. This is especially important in high-security applications where verifying that a device is genuine and uncompromised is critical. ADK Python helps developers verify hardware-backed keys, work with JSON Web Tokens (JWT), and integrate with Android’s Key Attestation infrastructure. -
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Alpa
Training and serving large-scale neural networks
Alpa is a system for training and serving large-scale neural networks. Scaling neural networks to hundreds of billions of parameters has enabled dramatic breakthroughs such as GPT-3, but training and serving these large-scale neural networks require complicated distributed system techniques. Alpa aims to automate large-scale distributed training and serving with just a few lines of code. -
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Detectron2
Next-generation platform for object detection and segmentation
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark. It is powered by the PyTorch deep learning framework. Includes more features such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc. Can be used as a library to support different projects on top of it. We'll open source more research projects in this way. It trains much faster. Models can be exported to TorchScript format or Caffe2 format for deployment. With a new, more modular design, Detectron2 is flexible and extensible, and able to provide fast training on single or multiple GPU servers. Detectron2 includes high-quality implementations of state-of-the-art object detection. -
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LiteLLM
lightweight package to simplify LLM API calls
Call all LLM APIs using the OpenAI format [Anthropic, Huggingface, Cohere, Azure OpenAI etc.] liteLLM supports streaming the model response back, pass stream=True to get a streaming iterator in response. Streaming is supported for OpenAI, Azure, Anthropic, and Huggingface models. -
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OBLITERATUS
OBLITERATE THE CHAINS THAT BIND YOU
OBLITERATUS is an advanced open-source toolkit designed to analyze and modify the internal behavior of large language models by identifying and removing mechanisms responsible for refusal or restricted responses. It implements a set of techniques collectively referred to as “abliteration,” which target specific internal representations within neural networks to alter how models respond to certain prompts. Unlike traditional fine-tuning approaches, OBLITERATUS operates directly on model activations, enabling behavioral changes without retraining the model. The toolkit provides a full pipeline for probing, analyzing, and modifying model behavior, including visualization tools that help researchers understand where and how refusal mechanisms are encoded. It supports multiple analytical methods such as PCA and SVD to locate these behavioral directions within model layers. -
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Open Interpreter
A natural language interface for computers
Open Interpreter is an open-source tool that provides a natural-language interface for interacting with your computer. It lets large language models (LLMs) run code locally (Python, JavaScript, shell, etc.), enabling you to ask your computer to do tasks like data analysis, file manipulation, browsing, etc. in human terms (“chat with your computer”), with safeguards. Runs locally or via configured remote LLM servers/inference backends, giving flexibility to use models you trust or have locally. It prompts you to approve code before executing, and supports both online LLM models and local inference servers. It seeks to combine convenience (like ChatGPT’s code interpreter) with control and flexibility by running on your own machine. -
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Transformer Engine
A library for accelerating Transformer models on NVIDIA GPUs
Transformer Engine (TE) is a library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit floating point (FP8) precision on Hopper GPUs, to provide better performance with lower memory utilization in both training and inference. TE provides a collection of highly optimized building blocks for popular Transformer architectures and an automatic mixed precision-like API that can be used seamlessly with your framework-specific code. TE also includes a framework-agnostic C++ API that can be integrated with other deep-learning libraries to enable FP8 support for Transformers. As the number of parameters in Transformer models continues to grow, training and inference for architectures such as BERT, GPT, and T5 become very memory and compute-intensive. Most deep learning frameworks train with FP32 by default. This is not essential, however, to achieve full accuracy for many deep learning models.
