Open Source Python Artificial Intelligence Software - Page 4
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Python Artificial Intelligence Software
View 13511 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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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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vLLM
A high-throughput and memory-efficient inference and serving engine
vLLM is a fast and easy-to-use library for LLM inference and serving. High-throughput serving with various decoding algorithms, including parallel sampling, beam search, and more. -
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Qwen3-TTS
Qwen3-TTS is an open-source series of TTS models
Qwen3-TTS is an open-source text-to-speech (TTS) project built around the Qwen3 large language model family, focused on generating high-quality, natural-sounding speech from plain text input. It provides researchers and developers with tools to transform text into expressive, intelligible audio, supporting multiple languages and voice characteristics tuned for clarity and fluidity. The project includes pre-trained models and inference scripts that let users synthesize speech locally or integrate TTS into larger pipelines such as voice assistants, accessibility tools, or multimedia generation workflows. Because it’s part of the broader Qwen ecosystem, it benefits from the model’s understanding of linguistic nuances, enabling more accurate pronunciation, prosody, and contextual delivery than many traditional TTS systems. Developers can customize voice output parameters like speed, pitch, and volume, and combine the TTS stack with other AI components. -
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Aider
Aider is AI pair programming in your terminal
Aider is an AI pair programming tool that runs directly in your terminal, helping developers build new projects or extend existing codebases faster and more confidently. It works alongside you like a coding partner, using powerful large language models to understand your code and implement precise changes. Aider creates a structured map of your entire repository, allowing it to handle large and complex projects effectively. It supports over 100 programming languages, making it flexible for nearly any development stack. With built-in Git integration, Aider keeps you in control by automatically committing clean, reversible changes. Whether you’re coding locally or in the cloud, Aider turns natural language requests into reliable, production-ready code. -
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Hunyuan3D-2.1
From Images to High-Fidelity 3D Assets
Hunyuan3D-2.1 is Tencent Hunyuan’s advanced 3D asset generation system that produces high-fidelity 3D models with Physically Based Rendering (PBR) textures. It is fully open-source with released model weights, training, and inference code. It improves on prior versions by using a PBR texture pipeline (enabling realistic material effects like reflections and subsurface scattering) and allowing community fine-tuning and extension. It supports both shape generation (mesh geometry) and texture generation modules. Physically Based Rendering texture synthesis to model realistic material effects, including reflections, subsurface scattering, etc. Cross-platform support (MacOS, Windows, Linux) via Python / PyTorch, including diffusers-style APIs. -
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OpenVoice
Instant voice cloning by MIT and MyShell. Audio foundation model
OpenVoice is a versatile instant voice cloning system that can replicate a speaker’s tone color from just a short audio clip and then generate speech in multiple languages. It is designed not only to match the timbre of the reference voice, but also to give granular control over style parameters such as emotion, accent, rhythm, pauses, and intonation. The model supports cross-lingual and even zero-shot cross-lingual voice cloning, so a speaker recorded in one language can be made to speak naturally in others. Architecturally, OpenVoice separates “tone color” cloning from style control, which makes it easier to keep a consistent identity while flexibly changing prosody or language. The project provides open-weight models, inference code, and examples, making it suitable both for research and for building production voice experiences. It is actively developed by MyShell, which also integrates OpenVoice into broader agent and entertainment workflows. -
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UFO³
Weaving the Digital Agent Galaxy
UFO is an open-source framework developed by Microsoft for building intelligent agents that automate interactions with graphical user interfaces on the Windows operating system. The system allows users to issue natural language instructions that are translated into automated actions across multiple desktop applications. Using a dual-agent architecture, the framework analyzes both visual interface elements and system control structures in order to understand how applications should be manipulated. This enables the agent to navigate complex software environments and perform tasks that normally require manual interaction. UFO integrates mechanisms for task decomposition, planning, and execution so that high-level user requests can be broken down into smaller steps performed by specialized agents. The framework can operate across multiple applications simultaneously, allowing workflows that span several programs to be automated seamlessly. -
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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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python-telegram-bot
A Python wrapper you can't refuse
python-telegram-bot is a library that provides a pure Python interface for the Telegram Bot API. It supports all types and methods of the API 4.8, and is compatible with all Python versions 3.5+ as well as PyPy. Apart from the pure API implementation, python-telegram-bot also offers several high-level classes contained in the telegram.ext submodule. These make bot development much easier and straightforward. python-telegram-bot is free and open source, fun to use, and fast and easy to install. Visit https://github.com/python-telegram-bot/python-telegram-bot/blob/master/examples/README.md to see official examples or the project’s wiki on https://github.com/python-telegram-bot/python-telegram-bot/wiki/Examples to see other community-built bots. -
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CoPaw
Your Personal AI Assistant; easy to install, deploy on local or coud
CoPaw is a personal AI assistant designed to run on your own machine or in the cloud, giving you full control over memory, models, and data. Built by the AgentScope team, it connects to multiple chat platforms—including DingTalk, Feishu, QQ, Discord, iMessage, and more—through a single unified assistant. CoPaw supports both cloud-based LLM providers and fully local models such as llama.cpp, MLX, and Ollama, allowing you to operate without API keys if preferred. It includes a browser-based Console for chatting, configuring models, managing memory, and extending capabilities with custom skills. With built-in cron scheduling, heartbeat check-ins, and extensible skill loading, CoPaw grows with your workflow over time. Easy installation options—including pip, one-line scripts, Docker, and cloud deployment—make it accessible for both developers and non-technical users. -
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DeepSeek-V3.2-Exp
An experimental version of DeepSeek model
DeepSeek-V3.2-Exp is an experimental release of the DeepSeek model family, intended as a stepping stone toward the next generation architecture. The key innovation in this version is DeepSeek Sparse Attention (DSA), a sparse attention mechanism that aims to optimize training and inference efficiency in long-context settings without degrading output quality. According to the authors, they aligned the training setup of V3.2-Exp with V3.1-Terminus so that benchmark results remain largely comparable, even though the internal attention mechanism changes. In public evaluations across a variety of reasoning, code, and question-answering benchmarks (e.g. MMLU, LiveCodeBench, AIME, Codeforces, etc.), V3.2-Exp shows performance very close to or in some cases matching that of V3.1-Terminus. The repository includes tools and kernels to support the new sparse architecture—for instance, CUDA kernels, logit indexers, and open-source modules like FlashMLA and DeepGEMM are invoked for performance. -
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ImageAI
A python library built to empower developers
ImageAI is an easy-to-use Computer Vision Python library that empowers developers to easily integrate state-of-the-art Artificial Intelligence features into their new and existing applications and systems. It is used by thousands of developers, students, researchers, tutors and experts in corporate organizations around the world. You will find features supported, links to official documentation as well as articles on ImageAI. ImageAI is widely used around the world by professionals, students, research groups and businesses. ImageAI provides API to recognize 1000 different objects in a picture using pre-trained models that were trained on the ImageNet-1000 dataset. The model implementations provided are SqueezeNet, ResNet, InceptionV3 and DenseNet. ImageAI provides API to detect, locate and identify 80 most common objects in everyday life in a picture using pre-trained models that were trained on the COCO Dataset. -
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Qwen-Image
Qwen-Image is a powerful image generation foundation model
Qwen-Image is a powerful 20-billion parameter foundation model designed for advanced image generation and precise editing, with a particular strength in complex text rendering across diverse languages, especially Chinese. Built on the MMDiT architecture, it achieves remarkable fidelity in integrating text seamlessly into images while preserving typographic details and layout coherence. The model excels not only in text rendering but also in a wide range of artistic styles, including photorealistic, impressionist, anime, and minimalist aesthetics. Qwen-Image supports sophisticated editing tasks such as style transfer, object insertion and removal, detail enhancement, and even human pose manipulation, making it suitable for both professional and casual users. It also includes advanced image understanding capabilities like object detection, semantic segmentation, depth and edge estimation, and novel view synthesis. -
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ebook2audiobook
Generate audiobooks from e-books, voice cloning & 1107+ languages
ebook2audiobook is a tool to convert legally obtained eBooks (non-DRM) into fully narrated audiobooks, complete with chapters and metadata. It automates the pipeline: it reads the eBook file, splits it into appropriate segments (chapters, paragraphs), uses text-to-speech (TTS) models to synthesize audio, optionally applies voice cloning, and outputs a final audiobook — ideal for people who prefer listening over reading, or for accessibility purposes. The tool supports a wide array of underlying TTS backends (XTTSv2, Bark, VITS, Fairseq, Tacotron2, YourTTS and more), which gives flexibility depending on hardware availability, voice preference, and language. It also supports a huge number of languages — apparently “+1110 languages and dialects” in its supported set — making it suitable for eBooks in many languages. -
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AudioCraft
Audiocraft is a library for audio processing and generation
AudioCraft is a PyTorch library for text-to-audio and text-to-music generation, packaging research models and tooling for training and inference. It includes MusicGen for music generation conditioned on text (and optionally melody) and AudioGen for text-conditioned sound effects and environmental audio. Both models operate over discrete audio tokens produced by a neural codec (EnCodec), which acts like a tokenizer for waveforms and enables efficient sequence modeling. The repo provides inference scripts, checkpoints, and simple Python APIs so you can generate clips from prompts or incorporate the models into applications. It also contains training code and recipes, so researchers can fine-tune on custom data or explore new objectives without building infrastructure from scratch. Example notebooks, CLI tools, and audio utilities help with prompt design, conditioning on reference audio, and post-processing to produce ready-to-share outputs. -
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AutoClip
AI-powered video clipping and highlight generation
AutoClip is an open-source, AI-powered video processing system designed to automate the extraction of “highlight” segments from full-length videos — ideal for creators who want to generate bite-sized clips, compilations, or highlight reels without manually sifting through hours of footage. The system supports downloading videos from major platforms (e.g. YouTube, Bilibili), or accepting local uploads, and then applies AI analysis to identify segments worth clipping based on content (e.g. high energy moments, speech, or other heuristics). Once highlights are identified, AutoClip can automatically cut those segments and optionally assemble them into a compilation, thus greatly reducing manual video editing effort. It uses a modern web application stack with a front end (React + TypeScript) for user interaction and a back end that handles downloading, processing, clipping, and queue management, allowing real-time progress feedback and easy deployment, e.g. via Docker. -
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EasyOCR
Ready-to-use OCR with 80+ supported languages
Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc. EasyOCR is a python module for extracting text from image. It is a general OCR that can read both natural scene text and dense text in document. We are currently supporting 80+ languages and expanding. Second-generation models: multiple times smaller size, multiple times faster inference, additional characters and comparable accuracy to the first generation models. EasyOCR will choose the latest model by default but you can also specify which model to use. Model weights for the chosen language will be automatically downloaded or you can download them manually from the model hub. The idea is to be able to plug-in any state-of-the-art model into EasyOCR. There are a lot of geniuses trying to make better detection/recognition models, but we are not trying to be geniuses here. We just want to make their works quickly accessible to the public. -
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edge-tts
Use Microsoft Edge's online text-to-speech service from Python
edge-tts is a Python module and command-line tool that gives you direct access to Microsoft Edge’s online text-to-speech service without needing the Edge browser, Windows, or any API key. It wraps the same cloud voices used by Edge, exposing them through a simple CLI (edge-tts, edge-playback) and a Python API, so you can script high-quality speech generation in your own applications. The tool lets you list available voices, specify locale and voice name, and generate audio files in common formats like MP3 or WAV. It also supports generating subtitle files (such as SRT or VTT) alongside the speech, which is handy for video narration, e-learning, or accessibility workflows. From the CLI you can adjust parameters such as speaking rate, volume, and pitch, giving you some control over prosody without diving into SSML. The library is asynchronous under the hood, which makes it efficient for batch jobs or web services that need to synthesize many utterances concurrently. -
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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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MinerU
A high-quality tool for convert PDF to Markdown and JSON
MinerU is an open-source, high-quality document extraction toolkit focused on converting PDFs (and other document formats) into structured Markdown and JSON. It leverages OCR and layout analysis to preserve semantic structure and metadata, ideal for research and data science workflows. -
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Qwen-Image-Layered
Qwen-Image-Layered: Layered Decomposition for Inherent Editablity
Qwen-Image-Layered is an extension of the Qwen series of multimodal models that introduces layered image understanding, enabling the model to reason about hierarchical visual structures — such as separating foreground, background, objects, and contextual layers within an image. This architecture allows richer semantic interpretation, enabling use cases such as scene decomposition, object-level editing, layered captioning, and more fine-grained multimodal reasoning than with flat image encodings alone. By combining text and structured image representations, it aims to facilitate tasks where both descriptive and structural understanding are important, such as detailed image QA, interactive image editing via prompt layers, and image-conditioned generation with structural control. The layered approach supports training signals that help the model learn how visual elements relate to each other and to textual context, rather than simply learning global image embeddings. -
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SpeechRecognition
Speech recognition module for Python
Library for performing speech recognition, with support for several engines and APIs, online and offline. Recognize speech input from the microphone, transcribe an audio file, save audio data to an audio file. Show extended recognition results, calibrate the recognizer energy threshold for ambient noise levels (see recognizer_instance.energy_threshold for details). Listening to a microphone in the background, various other useful recognizer features. The easiest way to install this is using pip install SpeechRecognition. The first software requirement is Python 2.6, 2.7, or Python 3.3+. This is required to use the library. PyAudio is required if and only if you want to use microphone input (Microphone). PyAudio version 0.2.11+ is required, as earlier versions have known memory management bugs when recording from microphones in certain situations. To hack on this library, first make sure you have all the requirements listed in the "Requirements" section. -
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VibeVoice
Open-source multi-speaker long-form text-to-speech model
VibeVoice-1.5B is Microsoft’s frontier open-source text-to-speech (TTS) model designed for generating expressive, long-form, multi-speaker conversational audio such as podcasts. Unlike traditional TTS systems, it excels in scalability, speaker consistency, and natural turn-taking for up to 90 minutes of continuous speech with as many as four distinct speakers. A key innovation is its use of continuous acoustic and semantic speech tokenizers operating at an ultra-low frame rate of 7.5 Hz, enabling high audio fidelity with efficient processing of long sequences. The model integrates a Qwen2.5-based large language model with a diffusion head to produce realistic acoustic details and capture conversational context. Training involved curriculum learning with increasing sequence lengths up to 65K tokens, allowing VibeVoice to handle very long dialogues effectively. Safety mechanisms include an audible disclaimer and imperceptible watermarking in all generated audio to mitigate misuse risks. -
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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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CogVideo
Text and image to video generation: CogVideoX and CogVideo
CogVideo is an open-source family of advanced video generation models that can create videos from text, images, or existing video inputs. Built on large-scale Transformer and diffusion architectures, it enables multimodal generation across text-to-video, image-to-video, and video continuation tasks. The latest CogVideoX models offer higher resolution outputs, longer video durations, and improved controllability through prompt engineering. The project includes tools for inference, fine-tuning, and optimization, making it suitable for both research and production use. It supports efficient deployment on a range of GPUs, including consumer hardware with quantization techniques. Overall, CogVideo provides a powerful framework for generating high-quality AI videos and experimenting with cutting-edge multimodal AI systems.
