Search Results for "decoder"
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Showing 332 open source projects for "decoder"
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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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Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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Pytorch-toolbelt
PyTorch extensions for fast R&D prototyping and Kaggle farming
...Extras for Catalyst library (Visualization of batch predictions, additional metrics). By design, both encoder and decoder produces a list of tensors, from fine (high-resolution, indexed 0) to coarse (low-resolution) feature maps. Access to all intermediate feature maps is beneficial if you want to apply deep supervision losses on them or encoder-decoder of object detection task. -
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UltraJSON
Ultra fast JSON decoder and encoder written in C with Python bindings
UltraJSON is an ultra-fast JSON encoder and decoder written in pure C with bindings for Python 3.7+. May be used as a drop-in replacement for most other JSON parsers for Python. Used to enable special encoding of "unsafe" HTML characters into safer Unicode sequences. Limits output to ASCII and escapes all extended characters above 127. Default is True. If your end format supports UTF-8, setting this option to false is highly recommended to save space. -
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go-json
Fast JSON encoder/decoder compatible with encoding/json for Go
Fast JSON encoder/decoder compatible with encoding/json for Go. -
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The Bitdefender disassembler
bddisasm is a fast, lightweight, x86/x64 instruction decoder
The Bitdefender disassembler is a highly optimized, lightweight x86/x64 instruction decoder designed for security, reverse engineering, and low-level systems analysis. It focuses on decoding machine instructions with high precision while providing detailed metadata about each instruction, including operands, access modes, CPU flags, and feature requirements. Unlike many disassemblers, bddisasm is built with zero dynamic memory allocation and no external dependencies, making it extremely portable and suitable for constrained environments such as kernels, hypervisors, and embedded systems. ... -
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Zstandard
Zstandard - Fast real-time compression algorithm
...It also offers a special mode for small data, called dictionary compression. The reference library offers a very wide range of speed / compression trade-off, and is backed by an extremely fast decoder (see benchmarks below). Zstandard library is provided as open source software using a BSD license. Its format is stable and published as IETF RFC 8478. The negative compression levels, specified with --fast=#, offer faster compression and decompression speed in exchange for some loss in compression ratio compared to level 1, as seen in the table above. ... -
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simplejson
simplejson is a simple, fast, extensible JSON encoder/decoder
simplejson is a simple, fast, complete, correct and extensible JSON <http://json.org> encoder and decoder for Python 3.3+ with legacy support for Python 2.5+. It is pure Python code with no dependencies but includes an optional C extension for a serious speed boost. simplejson is the externally maintained development version of the json library included with Python (since 2.6). This version is tested with the latest Python 3.8 and maintains backward compatibility with Python 3.3+ and the legacy Python 2.5 - Python 2.7 releases. ... -
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Super Audio CD Decoder input plugin for foobar2000. Decoder is capable of playing back Super Audio CD ISO images, DSDIFF, DSF and DSD WavPack files. DSD(DoP) and PCM output modes. Separate DSD Processor/DSD Converter plugins for track extraction into DSD/DST encoded files.
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Pilipala
Recommendations, live streams, video playback
PiliPala is a GPL‑licensed, Flutter-built third-party Bilibili client that works on Android, iOS, desktop, and web. It includes recommendations, live streams, video playback with gestures, adjustable playback speed, AV quality and decoder controls, bullet comments, subtitles, snapshots, fullscreen modes, UI theming, blacklists, and more. -
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Whisper
Robust Speech Recognition via Large-Scale Weak Supervision
...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 as a sequence of tokens to be predicted by the decoder, allowing a single model to replace many stages of a traditional speech-processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets. -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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Solidity
The Smart Contract Programming Language
Solidity is a statically-typed curly-braces programming language designed for developing smart contracts that run on Ethereum. Solidity 0.8.4 adds custom structured errors, allows more flexible configuration of the SMTChecker and fixes a bug in the Solidity ABI decoder v2. Read more about the bug here. With v0.8.4, errors can be defined inside and outside of contracts (including interfaces and libraries). This provides a convenient and gas-efficient way to explain to users why an operation failed. As a relatively young language, Solidity is advancing at a rapid speed. We aim for a regular (non-breaking) release every 2-3 weeks, with approximately two breaking releases per year. ... -
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Segment Anything
Provides code for running inference with the SegmentAnything Model
...It’s a promptable segmenter: you guide it with points, boxes, or rough masks, and it predicts high-quality object masks consistent with the prompt. The architecture separates a powerful image encoder from a lightweight mask decoder, so the heavy vision work can be computed once and the interactive part stays fast. A bundled automatic mask generator can sweep an image and propose many object masks, which is useful for dataset bootstrapping or bulk annotation. The repository includes ready-to-use weights, Python APIs, and example notebooks demonstrating both interactive and automatic modes. ... -
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nghttp2
HTTP/2 C Library and tools
...Since then we have updated nghttp2 library constantly to the latest specification and nghttp2 is now one of the most mature HTTP/2 implementations. HTTP/2 utilizes header compression method called HPACK. We offer HPACK encoder and decoder are available as public API. nghttp2 library itself is a bit low-level. The experimental high-level C++ API is also available. We have Python binding of this library, but we have not covered everything yet. -
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LZ4
Extremely fast compression algorithm
LZ4 is lossless compression algorithm, providing compression speed > 500 MB/s per core (>0.15 Bytes/cycle). It features an extremely fast decoder, with speed in multiple GB/s per core (~1 Byte/cycle). A high compression derivative, called LZ4_HC, is available, trading customizable CPU time for compression ratio. LZ4 library is provided as open-source software using a BSD license. This benchmark simulates simple "static content transfer" scenario such as OS Kernel compression or video game's static assets (text/images/tables/scripts/etc) which loading from Flash Memory / HDD / SSD. ... -
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far2l
Linux port of FAR v2
Linux fork of FAR Manager v2. Works also on OSX/MacOS and BSD (but the latter is not tested on a regular manner). Plug-ins that are currently working: NetRocks (SFTP/SCP/FTP/FTPS/SMB/NFS/WebDAV), colorer, multiarc, tmppanel, align, autowrap, drawing, edit case, SimpleIndent, Calculator, Python (optional scripting support). -
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Granite Code Models
A Family of Open Foundation Models for Code Intelligence
Granite Code Models are IBM’s open-source, decoder-only models tailored for code tasks such as fixing bugs, explaining and documenting code, and modernizing codebases. Trained on code from 116 programming languages, the family targets strong performance across diverse benchmarks while remaining accessible to the community. The repository introduces the model lineup, intended uses, and evaluation highlights, and it complements IBM’s broader Granite initiative spanning multiple modalities. ... -
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Typia
Super-fast/easy runtime validations and serializations
Super-fast/easy runtime validations and serializations through transformation. -
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Miso TTS
Miso TTS is an 8 billion, highly emotive text-to-speech model
Miso TTS is an advanced 8-billion-parameter text-to-speech model developed by Miso Labs for generating highly expressive and natural-sounding conversational speech. Built on an RVQ Transformer architecture inspired by Sesame CSM, it combines a powerful Llama-based backbone with an autoregressive audio decoder to produce high-quality audio from text. The model supports both standard speech synthesis and voice-conditioned generation using optional audio prompts for voice cloning. Miso TTS generates Mimi audio codes and can leverage conversation history to create more contextually aware and realistic dialogue. Designed for local deployment, it offers watermarking by default to help promote responsible use of generated audio. ... -
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Generic Image Decoder
A standalone, portable generic Ada package for decoding images
The Generic Image Decoder is a package for decoding a broad variety of image formats, from any data stream, to any kind of medium. Unconditionally portable code: OS-, CPU-, compiler- independent code. More information on... http://gen-img-dec.sf.net Alire crate: https://alire.ada.dev/crates/gid Mirror: https://github.com/zertovitch/gid -
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Kronos
A Foundation Model for the Language of Financial Markets
Kronos is a specialized open-source foundation model designed for analyzing and predicting financial market data using time-series representations of candlestick patterns. It is built as a decoder-only Transformer model trained specifically on K-line data, which captures open, high, low, close, and volume information across multiple global exchanges. The system introduces a novel tokenization approach that converts continuous financial data into discrete tokens, enabling the model to process market behavior similarly to language. ... -
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IndexTTS2
Industrial-level controllable zero-shot text-to-speech system
...It builds on state-of-the-art models such as XTTS and other modern neural TTS backbones, improving them with a conformer-based speech conditional encoder and upgrading the decoder to a high-quality vocoder (BigVGAN2), leading to clearer and more natural audio output. The system supports zero-shot voice cloning — meaning it can mimic a target speaker’s voice from a short reference sample — making it versatile for multi-voice uses. Compared to many open-source TTS tools, IndexTTS emphasizes efficiency and controllability: it offers faster inference, simpler training pipelines, and controllable speech parameters (like duration, pitch, and prosody), which is critical for production use. -
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GLM-OCR
Accurate × Fast × Comprehensive
GLM-OCR is an open-source multimodal optical character recognition (OCR) model built on a GLM-V encoder–decoder foundation that brings robust, accurate document understanding to complex real-world layouts and modalities. Designed to handle text recognition, table parsing, formula extraction, and general information retrieval from documents containing mixed content, GLM-OCR excels across major benchmarks while remaining highly efficient with a relatively compact parameter size (~0.9B), enabling deployment in high-concurrency services and edge environments. ... -
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go-toml
Go library for the TOML file format
...As much as possible, this library is designed to behave similarly as the standard library's encoding/json. While go-toml favors usability, it is written with performance in mind. Most operations should not be shockingly slow. Decoder can be set to "strict mode", which makes it error when some parts of the TOML document was not present in the target structure. This is a great way to check for typos. When most decoding errors occur, go-toml returns DecodeError), which contains a human readable contextualized version of the error. TOML supports native local date/times. ... -
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x-transformers
A simple but complete full-attention transformer
A simple but complete full-attention transformer with a set of promising experimental features from various papers. Proposes adding learned memory key/values prior to attending. They were able to remove feedforwards altogether and attain a similar performance to the original transformers. I have found that keeping the feedforwards and adding the memory key/values leads to even better performance. Proposes adding learned tokens, akin to CLS tokens, named memory tokens, that is passed through... -
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TorchAudio
Data manipulation and transformation for audio signal processing
The aim of torchaudio is to apply PyTorch to the audio domain. By supporting PyTorch, torchaudio follows the same philosophy of providing strong GPU acceleration, having a focus on trainable features through the autograd system, and having consistent style (tensor names and dimension names). Therefore, it is primarily a machine learning library and not a general signal processing library. The benefits of PyTorch can be seen in torchaudio through having all the computations be through PyTorch... -
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CTranslate2
Fast inference engine for Transformer models
CTranslate2 is a C++ and Python library for efficient inference with Transformer models. The project implements a custom runtime that applies many performance optimization techniques such as weights quantization, layers fusion, batch reordering, etc., to accelerate and reduce the memory usage of Transformer models on CPU and GPU. The execution is significantly faster and requires less resources than general-purpose deep learning frameworks on supported models and tasks thanks to many...
