Search Results for "text-speaker" - Page 22
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Showing 1790 open source projects for "text-speaker"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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repren
Rename anything
...Because it’s script-friendly, it slots well into project maintenance, codebase migrations, or release engineering tasks. The goal is to give you a reliable, repeatable alternative to ad-hoc shell loops when large-scale text and filename changes are needed. -
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PPTAgent
PPTAgent: Generating and Evaluating Presentations
PPTAgent is a research system for generating and evaluating slide decks that goes beyond simple text-to-slides. It follows a two-stage, edit-based workflow: first it analyzes reference presentations to infer slide roles and structure, then it drafts an outline and iteratively performs editing actions to produce new slides. The project includes both the generation agent and an evaluation framework, PPTEval, to score content quality, design, and coherence. -
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HumanEval
Code for the paper "Evaluating Large Language Models Trained on Code"
human-eval is a benchmark dataset and evaluation framework created by OpenAI for measuring the ability of language models to generate correct code. It consists of hand-written programming problems with unit tests, designed to assess functional correctness rather than superficial metrics like text similarity. Each task includes a natural language prompt and a function signature, requiring the model to generate an implementation that passes all provided tests. The benchmark has become a standard for evaluating code generation models, including those in the Codex and GPT families. Researchers can use the dataset to run reproducible comparisons across models and track improvements in functional code synthesis. ... -
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cognee
Deterministic LLMs Outputs for AI Applications and AI Agents
...Cognee acts a semantic memory layer, unveiling hidden connections within your data and infusing it with your company's language and principles. This self-optimizing process ensures ultra-relevant, personalized, and contextually aware LLM retrievals. Any kind of data works; unstructured text or raw media files, PDFs, tables, presentations, JSON files, and so many more. Add small or large files, or many files at once. We map out a knowledge graph from all the facts and relationships we extract from your data. Then, we establish graph topology and connect related knowledge clusters, enabling the LLM to "understand" the data. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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segment-geospatial
A Python package for segmenting geospatial data with the SAM
The segment-geospatial package draws its inspiration from segment-anything-eo repository authored by Aliaksandr Hancharenka. To facilitate the use of the Segment Anything Model (SAM) for geospatial data, I have developed the segment-anything-py and segment-geospatial Python packages, which are now available on PyPI and conda-forge. My primary objective is to simplify the process of leveraging SAM for geospatial data analysis by enabling users to achieve this with minimal coding effort. I... -
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HASS Configurator
Configuration UI for Home Assistant
The HASS Configurator is a small web app (you access it via a web browser) that provides a filesystem browser and text-editor to modify files on the machine the configurator is running on. It has been created to allow easy configuration of Home Assistant. It is powered by Ace editor, which supports syntax highlighting for various code/markup languages. YAML files (the default language for Home Assistant configuration files) will be automatically checked for syntax errors while editing. ... -
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PaddleSpeech
Easy-to-use Speech Toolkit including Self-Supervised Learning model
...We provide high-speed and ultra-lightweight models, and also cutting-edge technology. We provide production ready streaming asr and streaming tts system. Our frontend contains Text Normalization and Grapheme-to-Phoneme (G2P, including Polyphone and Tone Sandhi). Moreover, we use self-defined linguistic rules to adapt Chinese context. -
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Weblate
Web based localization tool with tight version control integration
...There is infrastructure in place so that your translation closely follows development. This way translators can work on translations the entire time, instead of working through huge amounts of new text just prior to release. Copylefted; use, see, modify and share at will, and with everyone. All translators are properly credited in the version control system. Customizable quality checks helps improve translation quality. -
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DjangoBlog
A blog system based on python3.8 and Django3.0
Articles, pages, categories, tags (add, delete, edit), etc. Articles and pages support Markdown and highlighting. Articles support full-text search. Complete comment feature, include posting reply comment and email notification. Markdown supporting. Sidebar feature, new articles, most readings, tags, etc. OAuth Login supported, including Google, GitHub, Facebook, Weibo, QQ. Memcache supported, with cache auto refresh. Simple SEO Features, notify Google and Baidu when there was a new article or other things. ... -
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Moshi
A speech-text foundation model for real time dialogue
Moshi is a speech-text foundation model and full-duplex spoken dialogue framework. It uses Mimi, a state-of-the-art streaming neural audio codec. Mimi processes 24 kHz audio, down to a 12.5 Hz representation with a bandwidth of 1.1 kbps, in a fully streaming manner (latency of 80ms, the frame size), yet performs better than existing, non-streaming, codecs like SpeechTokenizer (50 Hz, 4kbps), or SemantiCodec (50 Hz, 1.3kbps). -
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Remarkable for Linux
The Markdown Editor for Linux
With Live Preview you can see your changes as you make them. There is no need to export first to check your syntax. This is accompanied by synchronized scrolling. Remarkable has Github Flavoured Markdown. This has a simple, easy-to-learn syntax with features like checklists, highlighting, links, images and more. Remarkable allows you to export your files to PDF and HTML from within the app. The HTML code is even prettified and PDFs have a TOC. You can style your markdown documents however... -
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CyberPPT
A Codex Skill for generating high-density, editable PowerPoints
...Users select from eight fixed visual systems, after which the skill plans each slide’s hierarchy, grid, charts, palette, and information density. Image-generation blueprints guide reconstruction while native text, shapes, tables, charts, and vectors preserve core editability. Multiple quality gates inspect structure, visuals, overflow, spatial alignment, curves, and editable elements. Delivery includes the PPTX, rendered previews, manifests, and QA results, with failed hard gates blocking completion. -
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myGPTReader
AI Slack bot for reading, summarizing, and chatting with content
...It enables users to quickly understand web pages, documents, and even video content by transforming them into interactive discussions rather than static reading experiences. myGPTReader supports a wide range of file formats, including eBooks, PDFs, and text-based documents, making it flexible for both casual and professional use cases. It also integrates voice interaction capabilities, allowing users to communicate with the system verbally and even use it as a language practice assistant. In addition to content reading, myGPTReader includes built-in prompt templates that enhance conversations and help users get more structured and relevant responses. ... -
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Agent Framework
Framework for building, orchestrating, and deploying AI agents
...It also includes components such as agent sessions for managing state, context providers for maintaining memory, and middleware for intercepting and extending agent behavior. Developers can integrate external tools and services so that agents can execute actions beyond text generation. -
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AutoTrain Advanced
Faster and easier training and deployments
...The project provides a no-code and low-code interface that allows users to train models using custom datasets without needing extensive expertise in machine learning engineering. It supports a wide range of tasks including text classification, sequence-to-sequence modeling, token classification, sentence embedding training, and large language model fine-tuning. The system integrates closely with the Hugging Face ecosystem and allows developers to train models using datasets hosted on the Hugging Face Hub. AutoTrain Advanced can run locally or in cloud environments, making it adaptable to different computational setups. ... -
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FlexLLMGen
Running large language models on a single GPU
FlexLLMGen is an open-source inference engine designed to run large language models efficiently on limited hardware resources such as a single GPU. The system focuses on high-throughput generation workloads where large batches of text must be processed quickly, such as large-scale data extraction or document analysis tasks. Instead of requiring expensive multi-GPU systems, the framework uses techniques such as memory offloading, compression, and optimized batching to run large models on commodity hardware. The architecture distributes computation and memory usage across the GPU, CPU, and disk in order to maximize the number of tokens processed during inference. ... -
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Weibo Crawler
Python crawler for collecting and downloading Sina Weibo user data
weibo-crawler is a Python-based data collection tool designed to retrieve information from Sina Weibo user accounts. It automates the process of gathering posts, user profile details, and engagement metrics from one or more target accounts. weibo-crawler can extract comprehensive information about users, including profile attributes such as nickname, follower count, following count, and account metadata. It also captures detailed data about each post, including the content, publishing time,... -
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LongBench
LongBench v2 and LongBench (ACL 25'&24')
...Traditional language model benchmarks typically evaluate tasks involving relatively short inputs, which does not reflect many real-world applications such as analyzing large documents or entire code repositories. LongBench addresses this gap by providing datasets that require models to process and reason over long sequences of text across multiple tasks. The benchmark includes multiple categories such as single-document question answering, multi-document reasoning, summarization, long dialogue understanding, and code analysis. It supports bilingual evaluation in English and Chinese to assess multilingual capabilities across extended contexts. Newer versions of the benchmark introduce extremely long context windows ranging from thousands to millions of tokens, enabling researchers to test the limits of modern long-context models. -
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LLM Colosseum
Benchmark LLMs by fighting in Street Fighter 3
LLM-Colosseum is an experimental benchmarking framework designed to evaluate the capabilities of large language models through gameplay interactions rather than traditional text-based benchmarks. The system places language models inside the environment of the classic video game Street Fighter III, where they must interpret the game state and decide which actions to perform during combat. This setup creates a dynamic environment that tests reasoning, situational awareness, and decision-making abilities in real time. ... -
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Lagent
A lightweight framework for building LLM-based agents
...The framework provides tools and abstractions that allow language models to interact with external tools, execute tasks, and perform multi-step reasoning processes. Instead of using LLMs only for text generation, Lagent enables developers to transform models into agents capable of performing actions such as retrieving data, executing code, or interacting with APIs. The system includes modular components that allow developers to connect different models and tools within the same agent architecture. Its design emphasizes simplicity and flexibility so that developers can experiment with different agent workflows without needing a complex infrastructure setup. ... -
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LISA
LISA: Reasoning Segmentation via Large Language Model
...This approach allows the system to identify objects or regions in images based on semantic descriptions, contextual reasoning, and world knowledge. The model integrates multimodal capabilities by combining language understanding with visual perception so that text instructions guide the segmentation process. Researchers created a specialized task called reasoning segmentation, where the model must generate a mask for regions described in natural language instructions. -
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AgentBench
A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)
AgentBench is an open-source benchmark designed to evaluate the capabilities of large language models when used as autonomous agents. Unlike traditional language model benchmarks that focus on static text tasks, AgentBench measures how models perform in interactive environments that require planning, reasoning, and decision-making. The benchmark includes multiple environments that simulate realistic scenarios such as web interaction, database querying, and problem solving tasks. These environments require agents to interpret instructions, take actions, and adapt their strategies based on feedback from the environment. ... -
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DocETL
A system for agentic LLM-powered data processing and ETL
...The platform allows developers and researchers to construct structured workflows that extract, transform, and organize information from sources such as reports, transcripts, legal documents, and other text-heavy data. Instead of relying on single prompts or ad-hoc scripts, DocETL provides a declarative pipeline framework that breaks complex document analysis tasks into manageable operations that can be optimized and orchestrated automatically. Pipelines are typically defined using a low-code YAML interface, giving users full control over prompts and processing steps while still simplifying workflow creation. -
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llms-from-scratch-cn
Build a large language model from 0 only with Python foundation
llms-from-scratch-cn is an educational open-source project designed to teach developers how to build large language models step by step using practical code and conceptual explanations. The repository provides a hands-on learning path that begins with the fundamentals of natural language processing and gradually progresses toward implementing full GPT-style architectures from the ground up. Rather than focusing on using pre-trained models through APIs, the project emphasizes understanding... -
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Dash Data Agent
Self-learning data agent that grounds its answers in layers of content
Dash is a self-learning data agent built by the Agno AI community that generates grounded answers to English queries over structured data by synthesizing SQL and reasoning based on six layers of context, improving automatically with each run. It sidesteps common limitations of simple text-to-SQL agents by incorporating multiple context layers — including schema structure, human annotations, known query patterns, institutional knowledge from docs, machine-discovered error patterns, and live runtime context — to generate SQL queries that are both technically correct and semantically meaningful. The system then executes those queries against a database and interprets the results, returning human-friendly insights not just raw rows, while learning from errors and successes to reduce repeated mistakes.
