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通义百聆语音双子星升级并开源,大幅提升语音合成与识别能力

据通义大模型官方消息,通义百聆宣布其语音大模型Fun-CosyVoice3与Fun-ASR完成重大升级并同步开源。此次升级聚焦核心性能与实用性,为开发者与企业提供更强大的语音AI工具。 Fun-CosyVoice3:实时、精准的语音合成 模型首包延迟降低50%,实现“输入即发声”。中英混说错误率大幅下降56.4%,并支持9种语言、18种方言及情感控制。其开源版本Fun-CosyVoice3-0.5B具备优秀的zero-shot音色克隆能力。 Fun-ASR:强悍抗干扰的语音识别 该模型在嘈杂环境下准确率达93%,新增歌词与说唱识别功能。支持31种语言自由混说与多种中文方言,流式识别首字延迟仅160毫秒。轻量化版本Fun-ASR-Nano-0.8B同步开源,推理成本更低。 模型已在魔搭、HuggingFace及GitHub等平台开源,支持本地部署与二次开发。 https://github.com/FunAudioLLM/Fun-ASR(GitHub) https://funaudiollm.github.io/funasr/(GitHub.io) https://modelscope.cn/studios/FunAudioLLM/Fun-ASR-Nano/(国内体验demo) https://huggingface.co/spaces/FunAudioLLM/Fun-ASR-Nano(海外体验demo) https://modelscope.cn/models/FunAudioLLM/fun-asr-nano-2512(国内模型仓库) https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512(海外模型仓库)

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【Github2.2K星】PyTorch资源列表:450个NLP/CV/SP、论文实现、教程、示例

项目地址: https://github.com/bharathgs/Awesome-pytorch-list PyTorch 是什么? PyTorch是一个用于科学计算和深度学习的Python扩展库。它便于学习、编写和调试,支持灵活的动态计算图和GPU高速运算,具有完善的研发生态和技术社区。PyTorch于2017年由Facebook正式推出后,迅速引起了人工智能研发人员的关注,目前已成为最受重视的机器学习软件库之一。近日,Facebook 在首届 PyTorch 开发者大会发布了 PyTorch1.0 预览版,标志着这一框架更为稳定可用。 目录 一. 自然语言与语音处理NLP & Speech Processing 二. 计算机视觉CV 三. 概率生成库 四. 其他库 五. 教程和例子 六. 论文实现 七.其他Pytorch资源 目录内容 Pytorch 官网 项目链接:https://github.com/pytorch/pytorch/releases 一. 自然语言与语音处理 pytorch text : Torch text related contents. pytorch-seq2seq: A framework for sequence-to-sequence (seq2seq) models implemented in PyTorch. anuvada: Interpretable Models for NLP using PyTorch. audio: simple audio I/O for pytorch. loop: A method to generate speech across multiple speakers fairseq-py: Facebook AI Research Sequence-to-Sequence Toolkit written in Python. speech: PyTorch ASR Implementation. OpenNMT-py: Open-Source Neural Machine Translation in PyTorch http://opennmt.net neuralcoref: State-of-the-art coreference resolution based on neural nets and spaCy huggingface.co/coref sentiment-discovery: Unsupervised Language Modeling at scale for robust sentiment classification. MUSE: A library for Multilingual Unsupervised or Supervised word Embeddings nmtpytorch: Neural Machine Translation Framework in PyTorch. pytorch-wavenet: An implementation of WaveNet with fast generation Tacotron-pytorch: Tacotron: Towards End-to-End Speech Synthesis. AllenNLP: An open-source NLP research library, built on PyTorch. PyTorch-NLP: Text utilities and datasets for PyTorch pytorchnlp.readthedocs.io quick-nlp: Pytorch NLP library based on FastAI. TTS: Deep learning for Text2Speech LASER: Language-Agnostic SEntence Representations pyannote-audio: Neural building blocks for speaker diarization: speech activity detection, speaker change detection, speaker embedding gensen: Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning. translate: Translate - a PyTorch Language Library. espnet: End-to-End Speech Processing Toolkit espnet.github.io/espnet pythia: A software suite for Visual Question Answering UnsupervisedMT: Phrase-Based & Neural Unsupervised Machine Translation. jiant: The jiant sentence representation learning toolkit. 二. 计算机视觉 pytorch vision : Datasets, Transforms and Models specific to Computer Vision. pt-styletransfer: Neural style transfer as a class in PyTorch. OpenFacePytorch: PyTorch module to use OpenFace's nn4.small2.v1.t7 model img_classification_pk_pytorch: Quickly comparing your image classification models with the state-of-the-art models (such as DenseNet, ResNet, ...) SparseConvNet: Submanifold sparse convolutional networks. Convolution_LSTM_pytorch: A multi-layer convolution LSTM module face-alignment: 2D and 3D Face alignment library build using pytorch adrianbulat.com pytorch-semantic-segmentation: PyTorch for Semantic Segmentation. RoIAlign.pytorch: This is a PyTorch version of RoIAlign. This implementation is based on crop_and_resize and supports both forward and backward on CPU and GPU. pytorch-cnn-finetune: Fine-tune pretrained Convolutional Neural Networks with PyTorch. detectorch: Detectorch - detectron for PyTorch Augmentor: Image augmentation library in Python for machine learning. http://augmentor.readthedocs.io s2cnn: This library contains a PyTorch implementation of the SO(3) equivariant CNNs for spherical signals (e.g. omnidirectional cameras, signals on the globe) s 三. 概率生成库 ptstat: Probabilistic Programming and Statistical Inference in PyTorch pyro: Deep universal probabilistic programming with Python and PyTorch http://pyro.ai probtorch: Probabilistic Torch is library for deep generative models that extends PyTorch. paysage: Unsupervised learning and generative models in python/pytorch. pyvarinf: Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch. pyprob: A PyTorch-based library for probabilistic programming and inference compilation. mia: A library for running membership inference attacks against ML models. 四. 其他库 pytorch extras : Some extra features for pytorch. functional zoo : PyTorch, unlike lua torch, has autograd in it's core, so using modular structure of torch.nn modules is not necessary, one can easily allocate needed Variables and write a function that utilizes them, which is sometimes more convenient. This repo contains model definitions in this functional way, with pretrained weights for some models. torch-sampling : This package provides a set of transforms and data structures for sampling from in-memory or out-of-memory data. torchcraft-py : Python wrapper for TorchCraft, a bridge between Torch and StarCraft for AI research. aorun : Aorun intend to be a Keras with PyTorch as backend. logger : A simple logger for experiments. PyTorch-docset : PyTorch docset! use with Dash, Zeal, Velocity, or LovelyDocs. convert_torch_to_pytorch : Convert torch t7 model to pytorch model and source. pretrained-models.pytorch: The goal of this repo is to help to reproduce research papers results. pytorch_fft : PyTorch wrapper for FFTs 五. 教程例子 Practical Pytorch : Tutorials explaining different RNN models DeepLearningForNLPInPytorch : An IPython Notebook tutorial on deep learning, with an emphasis on Natural Language Processing. pytorch-tutorial : tutorial for researchers to learn deep learning with pytorch. pytorch-exercises : pytorch-exercises collection. pytorch tutorials : Various pytorch tutorials. pytorch examples : A repository showcasing examples of using pytorch pytorch practice : Some example scripts on pytorch. pytorch mini tutorials : Minimal tutorials for PyTorch adapted from Alec Radford's Theano tutorials. pytorch text classification : A simple implementation of CNN based text classification in Pytorch cats vs dogs : Example of network fine-tuning in pytorch for the kaggle competition Dogs vs. Cats Redux: Kernels Edition. Currently #27 (0.05074) on the leaderboard. 六. 论文实现 google_evolution : This implements one of result networks from Large-scale evolution of image classifiers by Esteban Real, et. al. pyscatwave : Fast Scattering Transform with CuPy/PyTorch,read the paper here scalingscattering : Scaling The Scattering Transform : Deep Hybrid Networks. deep-auto-punctuation : a pytorch implementation of auto-punctuation learned character by character. Realtime_Multi-Person_Pose_Estimation : This is a pytorch version of Realtime_Multi-Person_Pose_Estimation, origin code is here . PyTorch-value-iteration-networks : PyTorch implementation of the Value Iteration Networks (NIPS '16) paper pytorch_Highway : Highway network implemented in pytorch. pytorch_NEG_loss : NEG loss implemented in pytorch. pytorch_RVAE : Recurrent Variational Autoencoder that generates sequential data implemented in pytorch. pytorch_TDNN : Time Delayed NN implemented in pytorch. 七. 其他Pytorch资源 the-incredible-pytorch : The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch. generative models : Collection of generative models, e.g. GAN, VAE in Tensorflow, Keras, and Pytorch. http://wiseodd.github.io pytorch vs tensorflow : an informative thread on reddit. Pytorch discussion forum pytorch notebook: docker-stack : A project similar to Jupyter Notebook Scientific Python Stack drawlikebobross: Draw like Bob Ross using the power of Neural Networks (With PyTorch)! pytorch-tvmisc: Totally Versatile Miscellanea for Pytorch pytorch-a3c-mujoco: Implement A3C for Mujoco gym envs. PyTorch in 5 Minutes. pytorch_chatbot: A Marvelous ChatBot implemented using PyTorch. 原文发布时间为:2018-10-21 本文来自云栖社区合作伙伴“专知”,了解相关信息可以关注“专知”。

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在星盛数越网络下如何选择合适的高防服务器租用?

首先我们先了解防御类型和防御大小,我们常见的防火墙防御,是介于内部网和外部网、专用网和公共网之间的一种保护屏障,防火墙分为两种:一种是软件防火墙、另一种是硬件防火墙。1、软件防火墙:软件防火墙是寄生于操作平台上的,通过软件去实现隔离内部网与外部网之间的一种保护屏障。 2、硬件防火墙:硬件防火墙是镶嵌系统内的,硬件防火墙是有软件和硬件结合而生成的,硬件防火墙从性能方面和防御方面都要比软件防火墙要好。 其次是流量牵引技术,这是一种新型的防御,它能把正常流量和攻击流量区分开,把带有攻击的流量牵引到有防御DDOS、CC等攻击的设备上去,把流量攻击的方向牵引到其他设备上去而不是选择自身去硬抗。这样的方式对机房的带宽要求低,香港的高防服务器用的都是流量清洗技术,主要是因为香港本身带宽就不多,如果采用硬件防火墙的机房就需要有相应的闲置带宽供应防火墙,会造成更多的带宽浪费。 其次我们选择高防服务器租用时,需要注意一下几点: 1、选择合适的线路 高防服务器租用线路的选择一般分为单线、双线、多线BGP。单线线路常用的是联通线路或者电信线路,双线则是电信/网通两条线路同时接入,而多线BGP是用BGP协议实现的单IP双线路。 线路的选择主要考虑,站点访问人群是哪些区域,南方访客占据大多数而北方访客比较少的话建议用电信单线就行,要是南北方访客都差不多,而且处于业务需要想做全国的业务,那双线就是最合适的选择。 2、选择足够的带宽 带宽越大同时访问网站的人数就越多,下载的速度就越快。100M独享带宽指的是一台服务器独自享用这100M的带宽,而100M共享带宽指的是一个机柜的用户一共享用这100M的带宽。 如果不知道该选多大的带宽资源的话,可以咨询IDC服务商,让业务员根据业务情况推荐,可以多作几家对比。 3、选定合适的防御 防御是棋牌游戏服务器最看重的点之一。一般来说高防服务器指的是独立单个防御50G以上的服务器类型,防御的大小选择要密切衡量自己的成本和日常被攻击的情况,防御高低不同的价格从几百到几千不等。总之就是要综合考虑,选择比受到的攻击大一点的防御。 4、租用的机房 选定租用的机房,选择防御比较高的机房比较好。西安机房是国内防御比较好的机房,当然还有其他机房。选择机房,我们要看除了看机房承诺的防御外要看机房的资质,机房的配置,包括发电机、线路,机房等级等等。 5、服务器配置的选择 一开始租用高防机器建议先使用默认配置,一般选择8核CPU和8G内存就可以满足了,当然具体情况还需看自己的业务需求,根据自己业务需求作调整。

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