awesome-repositories.com
博客
MCP
awesome-repositories.com

通过 AI 驱动的搜索,发现最优秀的开源仓库。

探索精选搜索开源替代品自托管软件博客网站地图
项目MCP 服务器关于排名机制媒体报道
法律隐私政策服务条款
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

12 个仓库

Awesome GitHub RepositoriesAutoregressive Transformers

Transformer architectures that generate sequences by predicting tokens based on previous outputs.

Distinguishing note: Focuses on the specific two-stage generation architecture for audio.

Explore 12 awesome GitHub repositories matching artificial intelligence & ml · Autoregressive Transformers. Refine with filters or upvote what's useful.

Awesome Autoregressive Transformers GitHub Repositories

用 AI 发现最棒的仓库。我们将通过 AI 为您搜索最匹配的仓库。
  • microsoft/vibevoicemicrosoft 的头像

    microsoft/VibeVoice

    49,394在 GitHub 上查看↗

    VibeVoice is a generative artificial intelligence platform designed for text-to-speech synthesis. It functions as a neural audio generation framework that converts written text into natural-sounding spoken audio, specifically engineered to maintain consistent vocal characteristics and narrative prosody across extended passages of content. The system distinguishes itself through its ability to generate long-form conversational speech while preserving speaker identity and linguistic content. By utilizing latent space disentanglement, the model separates speaker traits from the input text, allow

    Utilizes autoregressive transformer architectures to predict sequential audio tokens for consistent long-form speech generation.

    Python
    在 GitHub 上查看↗49,394
  • 2noise/chattts2noise 的头像

    2noise/ChatTTS

    39,464在 GitHub 上查看↗

    ChatTTS is a conversational text-to-speech generative model designed to convert written dialogue into natural sounding audio. It functions as a multilingual speech synthesis framework capable of producing human-like audio across different languages and speaker profiles. The system is distinguished by its ability to generate interactive dialogue with realistic vocal nuances. It utilizes a speech nuance controller to insert specific tokens that trigger non-verbal elements, such as laughter, pauses, and interjections, during the synthesis process. The project includes a streaming audio generato

    Implements an autoregressive transformer architecture to predict audio tokens for sequential speech generation.

    Pythonagentchatchatgpt
    在 GitHub 上查看↗39,464
  • fishaudio/fish-speechfishaudio 的头像

    fishaudio/fish-speech

    24,928在 GitHub 上查看↗

    This project is a generative speech synthesis engine that converts text into high-fidelity human speech. It utilizes a two-stage autoregressive transformer architecture that separates semantic token prediction from acoustic detail reconstruction to balance linguistic accuracy with audio quality. The system is designed to support multilingual output and conversational AI development, enabling the generation of context-aware speech that maintains flow across multiple dialogue turns. The platform distinguishes itself through a production-ready inference server that employs continuous batching to

    Separates semantic prediction from acoustic reconstruction to balance linguistic accuracy and audio fidelity.

    Pythonllamatransformertts
    在 GitHub 上查看↗24,928
  • neonbjb/tortoise-ttsneonbjb 的头像

    neonbjb/tortoise-tts

    14,864在 GitHub 上查看↗

    Tortoise-tts is a neural text-to-speech engine and voice cloning toolkit designed for high-quality audio generation. It functions as a zero-shot synthesis system, meaning it can generate speech for unseen speakers without requiring additional training or fine-tuning for each new voice. The system specializes in replicating human vocal characteristics using small sets of reference audio clips. It allows for the extraction of voice latents to mimic specific speakers, the generation of random synthetic identities, and the blending of multiple voice profiles to create hybrid vocal identities. Th

    Uses autoregressive transformer architectures to maintain natural prosody and speech rhythms during audio token prediction.

    Jupyter Notebook
    在 GitHub 上查看↗14,864
  • borisdayma/dalle-miniborisdayma 的头像

    borisdayma/dalle-mini

    14,756在 GitHub 上查看↗

    dalle-mini is a text-to-image model and generative AI system designed to transform natural language descriptions into synthetic images. It functions as an image generation training toolkit and a generative model capable of creating visual representations from text prompts. The project provides a containerized deployment for consistent execution across different computing environments. It includes the necessary scripts and configuration files to train custom generative models from datasets. The system utilizes an autoregressive transformer architecture that treats visual data as discrete toke

    Implements an autoregressive transformer architecture to generate image tokens sequentially from text descriptions.

    Python
    在 GitHub 上查看↗14,756
  • sparkaudio/spark-ttsSparkAudio 的头像

    SparkAudio/Spark-TTS

    10,930在 GitHub 上查看↗

    Spark-TTS is a deep learning text-to-speech synthesis engine designed to convert written text into high-fidelity audio. It utilizes a transformer-based architecture and autoregressive sequence modeling to generate coherent speech, transforming linguistic input into natural-sounding waveforms through neural speech codec synthesis. The platform distinguishes itself through zero-shot voice cloning, which allows users to mimic a target speaker’s unique vocal identity using only a short reference audio sample without requiring additional model training. It also features cross-lingual phonetic mapp

    Implements an autoregressive transformer architecture to generate high-fidelity speech by predicting audio tokens based on preceding context.

    Python
    在 GitHub 上查看↗10,930
  • nvidia/cosmosNVIDIA 的头像

    NVIDIA/cosmos

    10,494在 GitHub 上查看↗

    Cosmos is an open platform of world models, datasets, and tools for building physical AI systems such as robots and autonomous vehicles. It provides video generation and video understanding models that can generate synthetic videos and world simulations from text, image, video, or action inputs, and analyze videos to produce captions, event timestamps, spatial bounding boxes, and next-action predictions. The platform includes a world simulation generator that produces images, videos, synchronized audio, and action-conditioned rollouts for synthetic data, alongside a visual content analyzer th

    Processes video tokens autoregressively to output structured text predictions like captions, bounding boxes, and next actions.

    Jupyter Notebook
    在 GitHub 上查看↗10,494
  • nlpxucan/wizardlmnlpxucan 的头像

    nlpxucan/WizardLM

    9,486在 GitHub 上查看↗

    WizardLM is a large language model and instruction-tuning framework designed to execute sophisticated coding, mathematical, and conversational tasks. It functions as an AI system for mathematical reasoning and code generation, as well as a synthetic dataset generator used to train other language models. The project is distinguished by its evolutionary instruction tuning, which uses a method to rewrite simple instructions into complex tasks. This process expands training dataset difficulty and produces a high volume of open-domain tasks across various difficulty levels. The system covers capa

    Utilizes a transformer-based autoregressive architecture to predict subsequent tokens and maintain conversational context.

    Python
    在 GitHub 上查看↗9,486
  • google/traxgoogle 的头像

    google/trax

    8,304在 GitHub 上查看↗

    Trax is a deep learning framework and hardware-agnostic tensor engine designed for designing and training neural networks. It serves as a research tool providing high-level combinators for composing complex architectures, alongside a dedicated library for building transformer models and a toolkit for reinforcement learning. The framework is distinguished by its support for reversible and sparse transformer architectures, which reduce memory and computational overhead. It enables a single set of model instructions to execute across different hardware backends without changing the underlying co

    Provides a mechanism for sequential output generation by looping previous predictions back into the model.

    Python
    在 GitHub 上查看↗8,304
  • eleutherai/gpt-neoEleutherAI 的头像

    EleutherAI/gpt-neo

    8,275在 GitHub 上查看↗

    GPT-Neo is an open-source distributed training framework designed for scaling GPT-2 and GPT-3-style language models across multiple devices using mesh-tensorflow for model parallelism. It provides the infrastructure to train transformer-based language models with billions of parameters across distributed computing environments, making large-scale language model research accessible outside of proprietary systems. The framework supports training both autoregressive GPT-style models and masked language models like BERT or RoBERTa, with configurable masking strategies and token handling. It inclu

    Ships an autoregressive transformer decoder that generates text token-by-token with causal attention masking.

    Pythongptgpt-2gpt-3
    在 GitHub 上查看↗8,275
  • zyphra/zonosZyphra 的头像

    Zyphra/Zonos

    7,225在 GitHub 上查看↗

    Zonos is a controllable audio synthesis engine and large language model for text-to-speech. It serves as a multilingual speech generator capable of producing audio in English, Japanese, Chinese, French, and German. The system provides zero-shot voice cloning, allowing the replication of specific human voices using short audio samples. It supports the capture of nuanced behaviors, such as whispering, and provides parametric control over speaking rate, pitch, frequency, and emotional tone. The project covers a broad range of expressive speech synthesis and custom audio generation capabilities,

    Implements an autoregressive transformer architecture to predict speech tokens from text and acoustic embeddings.

    Python
    在 GitHub 上查看↗7,225
  • multimodal-art-projection/yuemultimodal-art-projection 的头像

    multimodal-art-projection/YuE

    6,292在 GitHub 上查看↗

    YuE: Open Full-song Music Generation Foundation Model, something similar to Suno.ai but open

    Generates music tokens sequentially using a transformer decoder with causal masking and shared latent conditioning.

    Pythonaiaudio-generationdeep-learning
    在 GitHub 上查看↗6,292
  1. Home
  2. Artificial Intelligence & ML
  3. Autoregressive Transformers

探索子标签

  • Sampling MechanismsTechniques for generating sequential outputs by feeding previous predictions back into the model. **Distinct from Autoregressive Transformers:** Focuses on the sampling process itself rather than the overall transformer architecture
  • Video Token ReasonersProcesses video tokens autoregressively to output structured text predictions like captions, bounding boxes, and next actions. **Distinct from Autoregressive Transformers:** Distinct from Autoregressive Transformers: specifically applies autoregressive decoding to video tokens for structured text output, not general sequence generation.