Search Results for "aes256 encoder - decoder"
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Showing 11 open source projects for "aes256 encoder - decoder"
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IndexTTS2
Industrial-level controllable zero-shot text-to-speech system
IndexTTS is a modern, zero-shot text-to-speech (TTS) system engineered to deliver high-quality, natural-sounding speech synthesis with few requirements and strong voice-cloning capabilities. 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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Step3-VL-10B
Multimodal model achieving SOTA performance
...It achieves this efficiency and strong performance through unified pre-training on a massive 1.2 trillion-token multimodal corpus that jointly optimizes a language-aligned perception encoder with a powerful decoder, creating deep synergy between image processing and text understanding. -
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MAE (Masked Autoencoders)
PyTorch implementation of MAE
...It trains a Vision Transformer (ViT) by randomly masking a high percentage of image patches (typically 75%) and reconstructing the missing content from the remaining visible patches. This forces the model to learn semantic structure and global context without supervision. The encoder processes only the visible patches, while a lightweight decoder reconstructs the full image—making pretraining computationally efficient. After pretraining, the encoder serves as a powerful backbone for downstream tasks like image classification, segmentation, and detection, achieving top performance with minimal fine-tuning. The repository provides pretrained models, fine-tuning scripts, evaluation protocols, and visualization tools for reconstruction quality and learned features. -
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fairseq-lua
Facebook AI Research Sequence-to-Sequence Toolkit
fairseq-lua is the original Lua/Torch7 version of Facebook AI Research’s sequence modeling toolkit, designed for neural machine translation (NMT) and sequence generation. It introduced early attention-based architectures and training pipelines that later evolved into the modern PyTorch-based fairseq. The framework implements sequence-to-sequence models with attention, beam search decoding, and distributed training, providing a research platform for exploring translation, summarization, and... -
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Denoiser
Real Time Speech Enhancement in the Waveform Domain (Interspeech 2020)
Denoiser is a real-time speech enhancement model operating directly on raw waveforms, designed to clean noisy audio while running efficiently on CPU. It uses a causal encoder-decoder architecture with skip connections, optimized with losses defined both in the time domain and frequency domain to better suppress noise while preserving speech. Unlike models that operate on spectrograms alone, this design enables lower latency and coherent waveform output. The implementation includes data augmentation techniques applied to the raw waveforms (e.g. noise mixing, reverberation) to improve model robustness and generalization to diverse noise types. ... -
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DeepSeek-V4.1-Flash
Multimodal Mixture-of-Experts model with 552B backbone parameters
...It supports a context window of up to 1 million tokens, enabling developers to analyze extensive codebases, lengthy documents, and complex multi-step tasks. DeepSeek-V4.1-Flash introduces a Causal Encoder-Decoder architecture, Compressed Sparse Attention 2, and advanced KV cache compression to reduce memory requirements and computational costs, particularly for input-heavy AI applications. The model also features native image understanding, configurable reasoning effort from 1 to 100, and enhanced agentic capabilities for autonomous coding and tool calling. -
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MiMo-V2.6-Flash
Efficient 309B omnimodal MoE for coding, agents, vision, and audio
...Training uses a unified mixed RL process rather than separate domain-specific runs, alongside asynchronous GRPO and groupwise agentic grading that rewards higher-quality and more efficient solutions. Its architecture combines sliding-window and global attention with a 681M-parameter vision encoder and dedicated audio encoders. A five-layer speculative decoder predicts multiple subsequent tokens to accelerate inference. -
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MiMo-V2.6-Pro
1T omnimodal MoE model for coding, agents, and long-horizon reasoning
...MiMo-V2.6-Pro-RL uses a unified mixed reinforcement learning process rather than separate domain-specific runs, alongside groupwise agentic grading that rewards higher-quality and more efficient solutions. Its architecture combines sliding-window and global attention, a 681M-parameter vision encoder, dedicated audio encoders, and a five-layer multi-token speculative decoder. It targets coding, general and visual agents, tool use, cybersecurity, long-horizon reasoning, etc. -
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t5-base
Flexible text-to-text transformer model for multilingual NLP tasks
t5-base is a pre-trained transformer model from Google’s T5 (Text-To-Text Transfer Transformer) family that reframes all NLP tasks into a unified text-to-text format. With 220 million parameters, it can handle a wide range of tasks, including translation, summarization, question answering, and classification. Unlike traditional models like BERT, which output class labels or spans, T5 always generates text outputs. It was trained on the C4 dataset, along with a variety of supervised NLP... -
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bart-large-cnn
Summarization model fine-tuned on CNN/DailyMail articles
facebook/bart-large-cnn is a large-scale sequence-to-sequence transformer model developed by Meta AI and fine-tuned specifically for abstractive text summarization. It uses the BART architecture, which combines a bidirectional encoder (like BERT) with an autoregressive decoder (like GPT). Pre-trained on corrupted text reconstruction, the model was further trained on the CNN/DailyMail dataset—a collection of news articles paired with human-written summaries. It performs particularly well in generating concise, coherent, and human-readable summaries from longer texts. ...
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