Search Results for "python text" - Page 2
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Showing 633 open source projects for "python text"
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MLX-Audio
A text-to-speech, speech-to-text and speech-to-speech library
MLX-Audio is a speech library built on Apple’s MLX framework and optimized for Apple Silicon machines (M-series Macs). It focuses on text-to-speech and speech-to-speech workflows, with APIs and a command-line interface that make it easy to generate high-quality audio from text. Because it uses MLX and targets Apple Silicon, inference is fast and can take advantage of hardware acceleration and quantization for efficient on-device performance. The project provides a straightforward CLI (mlx_audio.tts.generate) as well as a Python API for programmatic generation of audio, including parameters for voice choice, speed, language hints, output format, and sample rate. ... -
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DeepSeek-OCR
Contexts Optical Compression
DeepSeek-OCR is an open-source optical character recognition solution built as part of the broader DeepSeek AI vision-language ecosystem. It is designed to extract text from images, PDFs, and scanned documents, and integrates with multimodal capabilities that understand layout, context, and visual elements beyond raw character recognition. The system treats OCR not simply as “read the text” but as “understand what the text is doing in the image”—for example distinguishing captions from body text, interpreting tables, or recognizing handwritten versus printed words. ... -
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Nexa SDK
Nexa SDK is a comprehensive toolkit for supporting ONNX and GGML
Nexa SDK is a comprehensive toolkit for supporting ONNX and GGML models. It supports text generation, image generation, vision-language models (VLM), and speech-to-text (ASR), and text-to-speech (TTS) capabilities. Additionally, it offers an OpenAI-compatible API server with JSON schema mode for function calling and streaming support, and a user-friendly Streamlit UI. Users can run Nexa SDK in any device with Python environment, and GPU acceleration is supported, including CUDA, Metal, and ROCm. ... -
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SAM 3
Code for running inference and finetuning with SAM 3 model
SAM 3 (Segment Anything Model 3) is a unified foundation model for promptable segmentation in both images and videos, capable of detecting, segmenting, and tracking objects. It accepts both text prompts (open-vocabulary concepts like “red car” or “goalkeeper in white”) and visual prompts (points, boxes, masks) and returns high-quality masks, boxes, and scores for the requested concepts. Compared with SAM 2, SAM 3 introduces the ability to exhaustively segment all instances of an... -
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Memvid
Video-based AI memory library. Store millions of text chunks in MP4
Memvid encodes text chunks as QR codes within MP4 frames to build a portable “video memory” for AI systems. This innovative approach uses standard video containers and offers millisecond-level semantic search across large corpora with dramatically less storage than vector DBs. It's self-contained—no DB needed—and supports features like PDF indexing, chat integration, and cloud dashboards. -
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abogen
Generate audiobooks from EPUBs, PDFs and text with captions
abogen is a tool designed to generate audiobooks (or speech narrations) from textual sources such as EPUBs, PDFs, or plain text, with synchronized captions. In other words, it automates the pipeline of reading a digital book (or document), converting its text into speech via a TTS engine, and packaging the result into an audiobook format — likely along with timestamped captions or subtitles that align with the spoken audio. This can be very useful for accessibility, content consumption on... -
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AI Runner
Offline inference engine for art, real-time voice conversations
AI Runner is an offline inference engine designed to run a collection of AI workloads on your own machine, including image generation for art, real-time voice conversations, LLM-powered chatbots and automated workflows. It is implemented as a desktop-oriented Python application and emphasizes privacy and self-hosting, allowing users to work with text-to-speech, speech-to-text, text-to-image and multimodal models without sending data to external services. At the core of its LLM stack is a mode-based architecture with specialized “modes” such as Author, Code, Research, QA and General, and a workflow manager that automatically routes user requests to the right agent based on the task. ... -
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Label Sleuth
Open source no-code system for text annotation and building of text
An open-source no-code system for text annotation and building text classifiers. No AI knowledge needed. From task definition to working model in just a few hours! While domain experts label their data, Label Sleuth automatically trains in the background-appropriate machine learning models. To avoid wasted labeling effort, Label Sleuth employs active learning techniques to guide the user in what they should be labeled next. Domain experts can quickly start labeling their data through an... -
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Style-Bert-VITS2
Style-Bert-VITS2: Bert-VITS2 with more controllable voice styles
Style-Bert-VITS2 is a text-to-speech system based on Bert-VITS2 that focuses on highly controllable voice styles and emotional expression. It takes the original Bert-VITS2 v2.1 and its Japanese-Extra variant and extends them so you can control emotion and speaking style with fine-grained intensity, not just choose a generic tone. The project targets both power users and beginners: Windows users without Git or Python can install and run it using bundled .bat scripts, while advanced users can work with virtual environments, uv, and Python tooling. ... -
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Pocket TTS
A TTS that fits in your CPU (and pocket)
Pocket TTS is a lightweight text-to-speech project designed to run efficiently on CPUs, targeting developers who want local speech generation without depending on GPUs or hosted web APIs. It is built to feel practical in everyday applications, where installation and usage should be as simple as adding a dependency and calling a function. The project focuses on keeping the runtime footprint manageable while still producing natural-sounding speech, which makes it attractive for offline tools,... -
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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... -
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FastRTC
The python library for real-time communication
...FastRTC also integrates nicely with UI frameworks (e.g. via a web demo using Gradio), so developers can rapidly prototype and deploy real-time streaming applications without deep knowledge of low-level WebRTC internals. Because voice-enabled AI agents often involve many moving parts (speech-to-text, text processing, text-to-speech, streaming, session/chat management), FastRTC helps by handling the streaming aspect, leaving the rest to be plugged in modularly. -
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Whisper
Robust Speech Recognition via Large-Scale Weak Supervision
OpenAI Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multitasking model that can perform multilingual speech recognition, speech translation, and language identification. A Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. These tasks are jointly represented... -
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Hazm
Persian NLP Toolkit
Hazm is a natural language processing (NLP) library for Persian text, offering various tools for text preprocessing, tokenization, part-of-speech tagging, and more. -
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Transformers
State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX
...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. ... -
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OmniVoice
High-Quality Voice Cloning TTS for 600+ Languages
The OmniVoice project is a cutting-edge multilingual text-to-speech system designed to generate high-quality speech across more than 600 languages. Built on a diffusion language model-style architecture, it combines scalability with strong performance, enabling both natural-sounding voice synthesis and efficient inference speeds. One of its most notable capabilities is zero-shot voice cloning, allowing users to replicate a speaker’s voice using only a short reference audio clip. In addition,... -
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WhisperX
Automatic Speech Recognition with Word-level Timestamps
WhisperX is an advanced speech recognition system built on top of OpenAI’s Whisper model, designed to improve transcription accuracy and timing precision for long-form audio. It addresses key limitations of standard Whisper implementations by introducing voice activity detection and forced alignment techniques to produce word-level timestamps. The system enables batched inference, significantly increasing transcription speed while maintaining high accuracy. It is particularly effective for... -
18
LangKit
An open-source toolkit for monitoring Language Learning Models (LLMs)
LangKit is an open-source text metrics toolkit for monitoring language models. It offers an array of methods for extracting relevant signals from the input and/or output text, which are compatible with the open-source data logging library whylogs. Productionizing language models, including LLMs, comes with a range of risks due to the infinite amount of input combinations, which can elicit an infinite amount of outputs. The unstructured nature of text poses a challenge in the ML observability... -
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Umi-OCR
OCR software, free and offline
Umi-OCR is a free and open-source optical character recognition (OCR) tool designed to provide fast, offline text extraction from images, screenshots, PDFs, and more without requiring a network connection. It includes a highly efficient offline OCR engine with built-in multilingual recognition libraries, so users can extract text across multiple languages with high accuracy directly on their machines. The software supports flexible usage patterns including screenshot capture OCR, batch... -
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FLUX.2
Official inference repo for FLUX.2 models
FLUX.2 is a state-of-the-art open-weight image generation and editing model released by Black Forest Labs aimed at bridging the gap between research-grade capabilities and production-ready workflows. The model offers both text-to-image generation and powerful image editing, including editing of multiple reference images, with fidelity, consistency, and realism that push the limits of what open-source generative models have achieved. It supports high-resolution output (up to ~4 megapixels),... -
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MiniMax-MCP
Official MiniMax Model Context Protocol (MCP) server
MiniMax-MCP is the official Model Context Protocol (MCP) server for accessing MiniMax’s multimodal generative APIs from MCP-compatible clients. It acts as a bridge between tools like Claude Desktop, Cursor, Windsurf, OpenAI Agents, and the MiniMax platform, exposing capabilities such as text-to-speech, voice cloning, image generation, text-to-image, video generation, image-to-video, text-to-video, and music generation. The server is written in Python and distributed under the MIT license, with a pyproject.toml and uv-based workflow that makes installation and execution reproducible. Configuration is handled through JSON files that tell MCP clients how to launch the server (typically via uvx minimax-mcp) and which environment variables to use for the API key, host, and output directory. ... -
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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... -
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RealtimeTTS
Converts text to speech in realtime
RealtimeTTS is a low-latency text-to-speech library built for real-time applications such as voice chat with LLMs, assistants, and interactive tools. It is designed around a streaming model: you can feed it text incrementally (for example, as an LLM responds) and get audio output almost immediately, which keeps end-to-end latency very low. The library is engine-agnostic and plugs into a wide range of cloud and local TTS systems, including OpenAI, ElevenLabs, Azure, Coqui, Piper, StyleTTS2,... -
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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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Sopro TTS
A lightweight text-to-speech model with zero-shot voice cloning
...The model is designed to work with a small set of dependencies and to be accessible for developers who want offline TTS with customizable voice style, including options for streaming or non-streaming generation modes. Users can install it with standard Python tools, run a demo server locally, and experiment with CLI or Python API usage for producing synthetic speech.
