Huber is a Portfolio Management Company providing industrial products &amp; engineered materials solutions. Explore the products we bring to your everyday life.

Industrial Products and Engineered Materials Solutions PMC | Huber

HUBER TECHNOLOGY Sarl, France: Notre société est le fabricant leader européen de ces procédés et a réalisé plus de 40.000 installations à travers le monde. Dégrilleur, compacteur à refus, tamis rotatif, classificateur et laveur de sables, traitement des sables de curage de réseaux, ...

HUBER TECHNOLOGY Sarl, France - LE SPECIALISTE DE LA SEPARATION ...

Expert Contracting, Remodeling, Homebuilding, Additions Since 1972, Serving the Baltimore and Surrounding Areas.

Huber Company

J.M. Huber Corporation is a corporation that produces consumer and industrial products. The J.M. Huber Corporation received IMD-Lombard Odier Global Family Business Award for 2013.

J.M. Huber Corporation - Wikipedia

J.M. Huber Corporation, one of the largest family-owned companies in the US, services customer around the world. Learn more about us here.

J.M. Huber Corporation | About J.M. Huber Corporation

J.M. Huber Corporation | 12,741 followers on LinkedIn. Improving Today for a Better Tomorrow. | J.M. Huber Corporation, headquartered in Atlanta, Georgia (U.S.), operates as a Portfolio Management Company, which serves as an effective organizational structure for a diverse range of businesses.

J.M. Huber Corporation | LinkedIn

Detaillied information on HUBER SE and the world-wide HUBER group: The HUBER Group is successfully operating in the environmental engineering sector. With its more than 50 subsidiaries, representative offices and agents, HUBER is among the worldwide leading suppliers in the field of ...

The HUBER Company - HUBER SE

Products for residential, multifamily and light commercial projects. AdvanTech® subflooring and ZIP System® sheathing and tape provide dependable performance.

Building Systems for Roofs, Walls & Floors | Huber Engineered Woods

As an international company we support our customers worldwide with efficient and innovative solutions and processes, competent advice and reliable service. More than 50,000 installations of HUBER plants world-wide contribute to a sustainable use of water, energy and resources.

HUBER Technology Inc. - Huber Technology Inc.

Huber&#x27;s family of solutions provides industrial products and engineered materials. Learn more about the solutions Huber brings to a multitude of industries.

Specialty Ingredients and Engineered Materials Solutions | Huber

Industry experts in developing and manufacturing engineered materials and inorganic materials widely used in industrial, paper and consumer based applications.

Huber Engineered Materials | Inorganic Materials | Advanced ...

Founded in 1883, the J.M. Huber Corporation has grown to be one of the largest family-owned companies in the U.S. We’re now a global company with approximately 4,000 employees in more than 20 countries, all of whom are guided by a spirit of creativity and innovation that transforms ideas ...

About | Huber Engineered Woods

Huber Engineered Materials (HEM), a specialty engineered materials company owned by the J.M. Huber Corporation announced on April 1 that it has completed the acquisition of Miller Chemical &amp; Fertilizer, LLC (Miller) from Dunes Point Capital, LP.

Miller Chemical & Fertilizer Acquired by Huber Corporation | Ag ...

To continue, please click the box below to let us know you&#x27;re not a robot · Please make sure your browser supports JavaScript and cookies and that you are not blocking them from loading. For more information you can review our Terms of Service and Cookie Policy

J.M. Huber Corporation: Private Company Information - Bloomberg

Huber Materials is a global leader in industrial chemical manufacturing and well-respected among all chemical manufacturing companies.

Huber Engineered Material | Industrial Chemical Manufacturing

J.M. Huber Corporation’s diverse portfolio of businesses—CP Kelco, Huber Engineered Materials, Huber Engineered Woods and Huber Resources Corp.—create

JM Huber | Company Overview & News

As a Hol-Mac Company, Keith Huber Corporation is dedicated to providing customers with the type of service and attention to quality that can only be expected from a world-class manufacturer. All of us at Keith Huber Corporation is committed to listening to our customer’s needs; after all, ...

Keith Huber Corporation

J.M. Huber Corporation. 312 likes · 1 talking about this. At J.M. Huber Corporation, we’re improving today for a better tomorrow.

J.M. Huber Corporation

We invite you to contact Huber ... hubermaterials@huber.com 1-866-JMHUBER (1-866-564-8237) Product Highlight: MoldX Optimized Alumina Trihydrate (ATH) ... Get the product support and information needed to be successful- our technical sales representatives bring an intensive knowledge base and commitment to service that will directly benefit any company’s bottom ...

J.M. Huber Corporation - Composites One

As an expert in the development and manufacturing of specialty engineered materials and agricultural solutions, Huber Engineered Materials (HEM) not only takes great pride in the quality of the inorganic materials and products we produce, but also our commitment to superior customer service ...

Huber Mobile

Huber

https://www.youtube.com/watch?v=eTBAxD6lt2g&embedable=true&transcript=true

Full Transcript of Andrew Huberman on the Lex Fridman Podcast 

Transcripción completa de Andrew Huberman en el podcast de Lex Friedman

Transcription complète d'Andrew Huberman sur le podcast Lex Friedman

लेक्स फ्रीडमैन पॉडकास्ट पर एंड्रयू ह्यूबरमैन की पूर्ण प्रतिलेख

レックス・フリードマン・ポッドキャストでのアンドリュー・ヒューバーマンの全文転写

Transcrição completa de Andrew Huberman no Lex Friedman Podcast

Bản ghi đầy đủ của Andrew Huberman trên Podcast Lex Friedman

安德鲁·休伯曼 (Andrew Huberman) 在莱克斯·弗里德曼 (Lex Friedman) 播客上的完整文字记录

Using Scanned Mesh Data for Auto-Digitized 3D Modeling: Conclusion & Future Work and References

This is paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Leah Chrestien, Czech Technical University in Prague; (2) Tomá˘s Pevný, Czech Technical University in Prague, and Gen Digital, Inc.; (3) Stefan Edelkamp, Czech Technical University in Prague; (4) Antonín Komenda, Czech Technical University in Prague. Table of Links Abstract & Introduction Preliminaries Conditions on strictly optimally efficient heuristic Loss functions Related Work Experiments Conclusion and References Appendix 7 Conclusion The motivation of this paper stemmed from the observation that even the cost-to-goal, considered to be an optimal heuristic, fails to guarantee a strictly optimally efficient search. Since a large body of existing research optimizes this quantity, we are effectively lost with respect to what should be optimized. To fill this gap, we have stated the necessary and sufficient conditions guaranteeing the forward search to be strictly optimally efficient. These conditions show that the absolute value of the heuristic is not important, but that the ranking of states in the Open list is what controls the efficiency. Ranking can be optimized by minimizing the ranking loss functions, but its concrete implementation needs to correspond to a variant of the forward search. In case of mismatch, the resulting heuristic can perform poorly, which has been demonstrated when the heuristic optimized for BGFS search was used with A* search. The other benefit of ranking losses is that from the point of view of statistical learning theory, they solve a simpler problem than ubiquitous regression in estimating the cost-to-goal. We do not question the existence of a strictly optimally efficient heuristic. Given our experiments, we believe that if the space of heuristic functions over which the loss is optimized is sufficiently rich, the result will be sufficiently close to the optimal for the needs of the search. 7.1 Acknowledgements This work has been supported by project numbers 22-32620S and 22-30043S from Czech Science Foundation and OP VVV project CZ.02.1.01/0.0/0.0/16_019/0000765 "Research Center for Informatics". This work has also received funding from the European Union’s Horizon Europe Research and Innovation program under the grant agreement TUPLES No 101070149. References [1] Forest Agostinelli, Stephen McAleer, Alexander Shmakov, and Pierre Baldi. Solving the rubik’s cube with deep reinforcement learning and search. Nature Machine Intelligence, 1(8):356–363, 2019. [2] Thomas Anthony, Zheng Tian, and David Barber. Thinking fast and slow with deep learning and tree search. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, editors, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 5360–5370, 2017. [3] Shahab Jabbari Arfaee, Sandra Zilles, and Robert C Holte. Learning heuristic functions for large state spaces. Artificial Intelligence, 175(16-17):2075–2098, 2011. [4] Jeff Bezanson, Alan Edelman, Stefan Karpinski, and Viral B Shah. Julia: A fresh approach to numerical computing. SIAM review, 59(1):65–98, 2017. [5] Mohak Bhardwaj, Sanjiban Choudhury, and Sebastian Scherer. Learning heuristic search via imitation. In Conference on Robot Learning, pages 271–280. PMLR, 2017. [6] Blai Bonet and Héctor Geffner. Planning as heuristic search. Artificial Intelligence. 2001 Jun; 129 (1-2): 5-33., 2001. [7] Vladimir Cherkassky, Xuhui Shao, Filip M Mulier, and Vladimir N Vapnik. Model complexity control for regression using vc generalization bounds. IEEE transactions on Neural Networks, 10(5):1075–1089, 1999. [8] Leah Chrestien, Tomáš Pevný, Antonín Komenda, and Stefan Edelkamp. Heuristic search planning with deep neural network using imitation, attention and curriculum learning. In PRLIJCAI workshop, 2021. [9] Lukás Chrpa. Combining learning techniques for classical planning: Macro-operators and entanglements. In ICTAI, pages 79–86. IEEE Computer Society, 2010. [10] Rina Dechter and Judea Pearl. The optimality of A* revisited. In Michael R. Genesereth, editor, AAAI, pages 95–99. AAAI Press, 1983. [11] Edsger W. Dijkstra. A note on two problems in connexion with graphs. Numerische Mathematik, 1:269–271, 1959. [12] Stefan Edelkamp. Automated creation of pattern database search heuristics. In Model Checking and Artificial Intelligence, pages 35–50, 2006. [13] Stefan Edelkamp and Stefan Schrödl. Heuristic Search - Theory and Applications. Academic Press, 2012. [14] Ariel Felner, Uzi Zahavi, Robert Holte, Jonathan Schaeffer, Nathan Sturtevant, and Zhifu Zhang. Inconsistent heuristics in theory and practice. Artificial Intelligence, 175(9):1570–1603, 2011. [15] Dieqiao Feng, Carla P. Gomes, and Bart Selman. Solving hard AI planning instances using curriculum-driven deep reinforcement learning. In Christian Bessiere, editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pages 2198–2205. ijcai.org, 2020. [16] Patrick Ferber, Malte Helmert, and Jörg Hoffmann. Neural network heuristics for classical planning: A study of hyperparameter space. In ECAI, pages 2346–2353. IOS Press, 2020. [17] Santiago Franco, Álvaro Torralba, Levi H. S. Lelis, and Mike Barley. On creating complementary pattern databases. In IJCAI, pages 4302–4309, 2017. [18] Caelan Reed Garrett, Leslie Pack Kaelbling, and Tomás Lozano-Pérez. Learning to rank for synthesizing planning heuristics. page 3089–3095, 2016. [19] Edward Groshev, Aviv Tamar, Maxwell Goldstein, Siddharth Srivastava, and Pieter Abbeel. Learning generalized reactive policies using deep neural networks. 2018. [20] Arthur Guez, Théophane Weber, Ioannis Antonoglou, Karen Simonyan, Oriol Vinyals, Daan Wierstra, Rémi Munos, and David Silver. Learning to search with mctsnets. In International conference on machine learning, pages 1822–1831. PMLR, 2018. [21] Peter E Hart, Nils J Nilsson, and Bertram Raphael. A formal basis for the heuristic determination of minimum cost paths. IEEE transactions on Systems Science and Cybernetics, 4(2):100–107, 1968. [22] Patrik Haslum, Adi Botea, Malte Helmert, Blai Bonet, and Sven Koenig. Domain-independent construction of pattern database heuristics for cost-optimal planning. In AAAI, pages 1007–1012, 2007. [23] Malte Helmert and Gabriele Röger. How good is almost perfect? In AAAI, pages 944–949. AAAI Press, 2008. [24] Robert C. Holte and Sandra Zilles. On the optimal efficiency of cost-algebraic A. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, pages 2288–2295. AAAI Press, 2019. [25] Michael Katz, Shirin Sohrabi, Horst Samulowitz, and Silvan Sievers. Delfi: Online planner selection for cost-optimal planning. IPC-9 planner abstracts, pages 57–64, 2018. [26] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv:1412.6980, 2014. [27] Donald E Knuth and Ronald W Moore. An analysis of alpha-beta pruning. Artificial intelligence, 6(4):293–326, 1975. [28] Richard E. Korf. Iterative-deepening-A*: An optimal admissible tree search. In IJCAI, pages 1034–1036. Morgan Kaufmann, 1985. [29] Richard E Korf. Macro-operators: A weak method for learning. Artificial intelligence, 26(1):35–77, 1985. [30] Šimon Mandlík, Matej Ra ˇ cinsk ˇ y, Viliam Lis ` y, and Tomáš Pevn ` y. Jsongrinder. jl: automated ` differentiable neural architecture for embedding arbitrary json data. Journal of Machine Learning Research, 23(298):1–5, 2022. [31] Drew V. McDermott. A heuristic estimator for means-ends analysis in planning. In AIPS, pages 142–149. AAAI, 1996. [32] David Ménager, Dongkyu Choi, Mark Roberts, and David W. Aha. Learning planning operators from episodic traces. In 2018 AAAI Spring Symposia. AAAI Press, 2018. [33] Ionut Moraru, Stefan Edelkamp, Santiago Franco, and Moisés Martínez. Simplifying automated pattern selection for planning with symbolic pattern databases. In KI, pages 249–263, 2019. [34] Christian Muise. Collection of pddl files of classical problems. https://github.com/AI-Planning/classical-domains, 2023. [35] XuanLong Nguyen, Martin J Wainwright, and Michael I Jordan. On surrogate loss functions and f-divergences. The Annals of Statistics, pages 876–904, 2009. [36] Laurent Orseau, Levi Lelis, Tor Lattimore, and Théophane Weber. Single-agent policy tree search with guarantees. Advances in Neural Information Processing Systems, 31, 2018. [37] Laurent Orseau and Levi HS Lelis. Implementation of the algorithms described in "policyguided heuristic search with guarantees" by l. orseau and l. lelis, published at aaai’21. https://github.com/levilelis/h-levin, 2021. [38] Laurent Orseau and Levi HS Lelis. Policy-guided heuristic search with guarantees. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 12382–12390, 2021. [39] Arthur L Samuel. Some studies in machine learning using the game of checkers. ii—recent progress. IBM Journal of research and development, 11(6):601–617, 1967. [40] Max-Philipp B. Schrader. gym-sokoban. https://github.com/mpSchrader/gym-sokoban, 2018. [41] Jendrik Seipp, Florian Pommerening, and Malte Helmert. New optimization functions for potential heuristics. In ICAPS, 2015. [42] Jendrik Seipp and Silvan Sievers. Fast downward stone soup 2023. [43] Jendrik Seipp, Álvaro Torralba, and Jörg Hoffmann. PDDL generators. https://doi.org/10.5281/zenodo.6382173, 2022. [44] William Shen. Strips-hgn. https://github.com/williamshen-nz/STRIPS-HGN, 2021. [45] William Shen, Felipe Trevizan, and Sylvie Thiébaux. Learning domain-independent planning heuristics with hypergraph networks. In ICAPS, volume 30, pages 574–584, 2020. [46] David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al. Mastering the game of go without human knowledge. nature, 550(7676):354–359, 2017. [47] Kristian Smith. Maze creator / solver. https://github.com/ravenkls/Maze-Generator-and Solver, 2022. [48] Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezny, Steven Schockaert, and Ondrej Kuzelka. Lifted relational neural networks: Efficient learning of latent relational structures. Journal of Artificial Intelligence Research, 62:69–100, 2018. [49] Simon Ståhlberg, Blai Bonet, and Hector Geffner. Learning general optimal policies with graph neural networks: Expressive power, transparency, and limits. In Akshat Kumar, Sylvie Thiébaux, Pradeep Varakantham, and William Yeoh, editors, Proceedings of the Thirty-Second International Conference on Automated Planning and Scheduling, ICAPS 2022, Singapore (virtual), June 13-24, 2022, pages 629–637. AAAI Press, 2022. [50] Ingo Steinwart. Consistency of support vector machines and other regularized kernel classifiers. IEEE transactions on information theory, 51(1):128–142, 2005. [51] Takeshi Takahashi, He Sun, Dong Tian, and Yebin Wang. Learning heuristic functions for mobile robot path planning using deep neural networks. In ICAPS, volume 29, pages 764–772, 2019. [52] Alvaro Torralba, Vidal Alcázar, Daniel Borrajo, Peter Kissmann, and Stefan Edelkamp. SymBA*: A symbolic bidirectional A* planner. In International Planning Competition, pages 105–108, 2014. [53] Sam Toyer, Sylvie Thiébaux, Felipe Trevizan, and Lexing Xie. Asnets: Deep learning for generalised planning. Journal of Artificial Intelligence Research, 68:1–68, 2020. [54] Vladimir Vapnik. Principles of risk minimization for learning theory. Advances in neural information processing systems, 4, 1991. [55] Marin Vlastelica, Michal Rolinek, and Georg Martius. Neuro-algorithmic policies enable fast combinatorial generalization. In International Conference on Machine Learning, pages 10575– 10585. PMLR, 2021. [56] Xuemei Wang. Learning planning operators by observation and practice. In Kristian J. Hammond, editor, AIPS, pages 335–340. AAAI, 1994. [57] Christopher Wilt and Wheeler Ruml. When does weighted A* fail? In International Symposium on Combinatorial Search, volume 3, 2012. [58] Christopher Makoto Wilt and Wheeler Ruml. Effective heuristics for suboptimal best-first search. J. Artif. Intell. Res., 57:273–306, 2016. [59] Yuehua Xu, Alan Fern, and Sungwook Yoon. Learning linear ranking functions for beam search with application to planning. Journal of Machine Learning Research, 10(7), 2009. [60] Ryo Yonetani, Tatsunori Taniai, Mohammadamin Barekatain, Mai Nishimura, and Asako Kanezaki. Path planning using neural A* search. In International Conference on Machine Learning, pages 12029–12039. PMLR, 2021. [61] Ziqi Zhang and Florian Geißer. Extending graph neural networks for generalized stochastic planning. In Bridging the Gap Between AI Planning and Reinforcement Learning. 2021. [62] Tan Zhi-Xuan. PDDL. jl: An Extensible Interpreter and Compiler Interface for Fast and Flexible AI Planning. PhD thesis, Massachusetts Institute of Technology, 2022. [63] Juntang Zhuang, Tommy Tang, Yifan Ding, Sekhar C Tatikonda, Nicha Dvornek, Xenophon Papademetris, and James Duncan. Adabelief optimizer: Adapting stepsizes by the belief in observed gradients. Advances in neural information processing systems, 33:18795–18806, 2020.

Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal: Conclusion and References

Systems Interoperability Types: The Conclusion to Our Tertiary Study

This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaoyu Ai, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia; (2) Robert Malaney, School of Electrical Engineering & Telecommunications, University of New South Wales, Sydney, NSW 2052, Australia. Table of Links Abstract & Introduction System Overview Overcoming the limitation of key reconciliation Final Key Rate Experimental Results Discussion Conclusion & References Appendix VII. CONCLUSION In this work, we have carried out a full-blown analysis and experimental implementation of a Slice Reconciliation scheme applied to a specific CV QKD protocol (with post-selection) under simulated channel conditions anticipated for satelliteto-Earth channels. We have provided the optimal solution for the classical reconciliation process for this CV-QKD protocol in the context of massive parallelisation under the finite key regime. More specifically, we have identified the optimal block length when a large-code block is to be subdivided so as to improve the final secure key rate in bits per second. Although our results were based on a specific CV-QKD protocol and a specific GPU architecture, the type of analysis we have introduced here will apply in general terms a large suite of CV-QKD protocols run over any form of architecture that offers massive parallelisation. Our results, therefore, pave the way to optimal reconciliation system design for a wide range of practical CV-QKD systems that operate in the finite key regime. As the demand on the finite key size grows (better security thresholds), and technology advances lead to larger quantum signalling rates, the importance of optimised multithreaded CV-QKD reconciliation will grow. REFERENCES [1] N. Hosseinidehaj, Z. Babar, R. Malaney, S. X. Ng, and L. Hanzo, “Satellite-Based Continuous-Variable Quantum Communications: Stateof-the-Art and a Predictive Outlook,” IEEE Communications Surveys & Tutorials, vol. 21, no. 1, 881–919, 2018. [2] C. H. Bennett and G. Brassard, “Quantum Cryptography: Public Key Distribution and Coin Tossing,” in Proceedings of IEEE International Conference on Computers, Systems and Signal Processing,, vol. 175, 8, 1984. [3] H. Weinfurter, “Quantum Communication Experiments with Discrete Variables,” Quantum Information: From Foundations to Quantum Technology Applications, 369–381, 2016. [4] L. Gy ¨ongy ¨osi, L. Bacsardi, and S. Imre, “A Survey on Quantum Key Distribution,” Infocommunications Journal, vol. 11, no. 2, 14–21, 2019. [5] S. Pirandola, U. L. Andersen, L. Banchi, M. Berta, D. Bunandar, R. Colbeck, D. Englund, T. Gehring, C. Lupo, C. Ottaviani, et al., “Advances in Quantum Cryptography,” Advances in Optics and Photonics, vol. 12, no. 4, 1012–1236, 2020. [6] B. Korzh, C. C. W. Lim, R. Houlmann, N. Gisin, M. J. Li, D. Nolan, B. Sanguinetti, R. Thew, and H. Zbinden, “Provably Secure and Practical Quantum Key Distribution over 307 km of Optical Fibre,” Nature Photonics, vol. 9, no. 3, 163–168, 2015. [7] T. A. Eriksson, T. Hirano, B. J. Puttnam, G. Rademacher, R. S. Lu´ıs, M. Fujiwara, R. Namiki, Y. Awaji, M. Takeoka, N. Wada, et al., “Wavelength Division Multiplexing of Continuous Variable Quantum Key Distribution and 18.3 Tbit/s Data Channels,” Communications Physics, vol. 2, no. 1, 1–8, 2019. [8] S.-Y. Shen, M.-W. Dai, X.-T. Zheng, Q.-Y. Sun, G.-C. Guo, and Z.- F. Han, “Free-Space Continuous-Variable Quantum Key Distribution of Unidimensional Gaussian Modulation Using Polarized Coherent States in an Urban Environment,” Physical Review A, vol. 100, 012325, 2019. [9] L. Gyongyosi and S. Imre, “Secret Key Rates of Free-Space Optical Continuous-Variable Quantum Key Distribution,” International Journal of Communication Systems, vol. 32, no. 18, e4152, 2019. [10] P. Jouguet, S. Kunz-Jacques, and A. Leverrier, “Long-Distance Continuous-Variable Quantum Key Distribution with a Gaussian Modulation,” Physical Review A, vol. 84, no. 6, 062317, 2011. [11] P. Jouguet, S. Kunz-Jacques, T. Debuisschert, S. Fossier, E. Diamanti, R. All´eaume, R. Tualle-Brouri, P. Grangier, A. Leverrier, P. Pache, et al., “Field Test of Classical Symmetric Encryption with Continuous Variables Quantum Key Distribution,” Optics Express, vol. 20, no. 13, 14030–14041, 2012. [12] Q. Liao, Y. Guo, D. Huang, P. Huang, and G. Zeng, “Long-Distance Continuous-Variable Quantum Key Distribution Using Non-Gaussian State-Discrimination Detection,” New Journal of Physics, vol. 20, no. 2, 023015, 2018. [13] D. Guo, C. He, T. Guo, Z. Xue, Q. Feng, and J. Mu, “Comprehensive High-Speed Reconciliation for Continuous-Variable Quantum Key Distribution,” Quantum Information Processing, vol. 19, no. 9, 1–19, 2020. [14] Y. Zhang, Z. Chen, S. Pirandola, X. Wang, C. Zhou, B. Chu, Y. Zhao, B. Xu, S. Yu, and H. Guo, “Long-Distance Continuous-Variable Quantum Key Distribution over 202.81 km of Fiber,” Physical Review Letters, vol. 125, no. 1, 010502, 2020. [15] A. Leverrier, F. Grosshans, and P. Grangier, “Finite-Size Analysis of a Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 81, no. 6, 062343, 2010. [16] F. Furrer, T. Franz, M. Berta, A. Leverrier, V. B. Scholz, M. Tomamichel, and R. F. Werner, “Continuous Variable Quantum Key Distribution: Finite-Key Analysis of Composable Security against Coherent Attacks,” Physical Review Letters, vol. 109, no. 10, 100502, 2012. [17] S. Kish, E. Villase˜nor, R. Malaney, K. Mudge, and K. Grant, “Feasibility Assessment for Practical Continuous Variable Quantum Key Distribution over the Satellite-to-Earth Channel,” Quantum Engineering, vol. 2, no. 3, e50, 2020. [18] M. Tomamichel, C. C. W. Lim, N. Gisin, and R. Renner, “Tight FiniteKey Analysis for Quantum Cryptography,” Nature Communications, vol. 3, no. 1, 1–6, 2012. [19] Z. Yuan, A. Plews, R. Takahashi, K. Doi, W. Tam, A. Sharpe, A. Dixon, E. Lavelle, J. Dynes, A. Murakami, et al., “10-Mb/s Quantum Key Distribution,” Journal of Lightwave Technology, vol. 36, no. 16, 3427– 3433, 2018. [20] S.-K. Liao, W.-Q. Cai, W.-Y. Liu, L. Zhang, Y. Li, J.-G. Ren, J. Yin, Q. Shen, Y. Cao, Z.-P. Li, et al., “Satellite-to-Ground Quantum Key Distribution,” Nature, vol. 549, no. 7670, 43–47, 2017. [21] D. Lin, D. Huang, P. Huang, J. Peng, and G. Zeng, “High Performance Reconciliation for Continuous-Variable Quantum Key Distribution with LDPC Code,” International Journal of Quantum Information, vol. 13, no. 02, 1550010, 2015. [22] X. Wang, Y. Zhang, S. Yu, B. Xu, Z. Li, and H. Guo, “Efficient Rate-Adaptive Reconciliation for Continuous-Variable Quantum Key Distribution,” Quantum Information & Computation, vol. 17, no. 13– 14, 1123–1134, 2017. [23] C. Zhou, X. Wang, Y. Zhang, Z. Zhang, S. Yu, and H. Guo, “ContinuousVariable Quantum Key Distribution with Rateless Reconciliation Protocol,” Physical Review Applied, vol. 12, no. 5, 054013, 2019. [24] M. Milicevic, C. Feng, L. M. Zhang, and P. G. Gulak, “Quasi-Cyclic Multi-Edge LDPC Codes for Long-Distance Quantum Cryptography,” NPJ Quantum Information, vol. 4, no. 1, 1–9, 2018. [25] S.-S. Yang, Z.-G. Lu, and Y.-M. Li, “High-Speed Post-Processing in Continuous-Variable Quantum Key Distribution Based on FPGA Implementation,” Journal of Lightwave Technology, vol. 38, no. 15, 3935–3941, 2020. [26] H. Li and Y. Pang, “FPGA-Accelerated Quantum Computing Emulation and Quantum Key Distillation,” IEEE Micro, vol. 41, no. 4, 49–57, 2021. [27] C. Weedbrook, A. M. Lance, W. P. Bowen, T. Symul, T. C. Ralph, and P. K. Lam, “Quantum Cryptography without Switching,” Physical Review Letters, vol. 93, no. 17, 170504, 2004. [28] N. Hosseinidehaj, A. M. Lance, T. Symul, N. Walk, and T. C. Ralph, “Finite-Size Effects in Continuous-Variable Quantum Key Distribution with Gaussian Postselection,” Physical Review A, vol. 101, no. 5, 052335, 2020. [29] D. Dequal, L. T. Vidarte, V. R. Rodriguez, G. Vallone, P. Villoresi, A. Leverrier, and E. Diamanti, “Feasibility of Satellite-to-Ground Continuous-Variable Quantum Key Distribution,” NPJ Quantum Information, vol. 7, no. 1, 1–10, 2020. [30] M. Bloch, A. Thangaraj, and S. W. McLaughlin, “Efficient Reconciliation of Correlated Continuous Random Variables Using LDPC Codes,” arXiv preprint cs/0509041, 2005. [31] P. Jouguet, D. Elkouss, and S. Kunz-Jacques, “High-Bit-Rate Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 90, no. 4, 042329, 2014. [32] H. Mani, T. Gehring, P. Grabenweger, B. Omer, C. Pacher, and ¨ U. L. Andersen, “Multiedge-Type Low-Density Parity-Check Codes for Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 103, no. 6, 062419, 2021. [33] H. Imai and S. Hirakawa, “A New Multilevel Coding Method Using Error-Correcting Codes,” IEEE Transactions on Information Theory, vol. 23, no. 3, 371–377, 1977. [34] U. Wachsmann, R. F. Fischer, and J. B. Huber, “Multilevel codes: Theoretical concepts and practical design rules,” IEEE Transactions on Information Theory, vol. 45, no. 5, 1361–1391, 1999. [35] R. D. Wesel, X. Liu, J. M. Cioffi, and C. Komninakis, “Constellation Labeling for Linear Encoders,” IEEE Transactions on Information Theory, vol. 47, no. 6, 2417–2431, 2001. [36] M. Bloch, A. Thangaraj, S. W. McLaughlin, and J.-M. Merolla, “LDPCBased Gaussian Key Reconciliation,” in IEEE Information Theory Workshop-ITW’06 Punta del Este, 116–120, IEEE, 2006. [37] A. Leverrier, R. All´eaume, J. Boutros, G. Z´emor, and P. Grangier, “Multidimensional Reconciliation for a Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 77, no. 4, 042325, 2008. [38] J. Fiur´aˇsek and N. J. Cerf, “Gaussian Postselection and Virtual Noiseless Amplification in Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 86, no. 6, 060302, 2012. [39] F. Darema, “The SPMD Model: Past, Present and Future,” in European Parallel Virtual Machine/Message Passing Interface Users’ Group Meeting, 1–1, Springer, 2001. [40] M. Pharr and W. R. Mark, “A SPMD Compiler for High-Performance CPU Programming,” in Innovative Parallel Computing (InPar), 1–13, IEEE, 2012. [41] J. Lodewyck, M. Bloch, R. Garc´ıa-Patr ´on, S. Fossier, E. Karpov, E. Diamanti, T. Debuisschert, N. J. Cerf, R. Tualle-Brouri, S. W. McLaughlin, et al., “Quantum Key Distribution over 25 km with an AllFiber Continuous-Variable System,” Physical Review A, vol. 76, no. 4, 042305, 2007. [42] F. Laudenbach, C. Pacher, C.-H. F. Fung, A. Poppe, M. Peev, B. Schrenk, M. Hentschel, P. Walther, and H. H ¨ubel, “Continuous-Variable Quantum Key Distribution with Gaussian Modulation—The Theory of Practical Implementations,” Advanced Quantum Technologies, vol. 1, no. 1, 1800011, 2018. [43] Y. Polyanskiy, H. V. Poor, and S. Verd ´u, “Channel Coding Rate in the Finite Blocklength Regime,” IEEE Transactions on Information Theory, vol. 56, no. 5, 2307–2359, 2010. [44] D. Slepian and J. Wolf, “Noiseless Coding of Correlated Information Sources,” IEEE Transactions on Information Theory, vol. 19, no. 4, 471–480, 1973. [45] T. Richardson and R. Urbanke, Modern Coding Theory. Cambridge University Press, 2008. [46] X. Ai, R. Malaney, and S. X. Ng, “A Reconciliation Strategy for RealTime Satellite-Based QKD,” IEEE Communications Letters, vol. 24, no. 5, 1062–1066, 2020. [47] S.-Y. Chung, T. J. Richardson, and R. L. Urbanke, “Analysis of SumProduct Decoding of Low-Density Parity-Check Codes Using a Gaussian Approximation,” IEEE Transactions on Information Theory, vol. 47, no. 2, 657–670, 2001. [48] V. A. Chandrasetty and S. M. Aziz, “FPGA Implementation of an LDPC Decoder Using a Reduced Complexity Message Passing Algorithm,” Journal of Networks, vol. 6, no. 1, 36, 2011. [49] A. Leverrier, “Composable Security Proof for Continuous-Variable Quantum Key Distribution with Coherent States,” Physical Review Letters, vol. 114, no. 7, 070501, 2015. [50] C. Lupo, C. Ottaviani, P. Papanastasiou, and S. Pirandola, “ContinuousVariable Measurement-Device-Independent Quantum Key Distribution: Composable Security against Coherent Attacks,” Physical Review A, vol. 97, no. 5, 052327, 2018. [51] N. Hosseinidehaj, N. Walk, and T. C. Ralph, “Optimal Realistic Attacks in Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 99, no. 5, 052336, 2019. [52] S. Pirandola, “Limits and Security of Free-Space Quantum Communications,” Physical Review Research, vol. 3, no. 1, 013279, 2021. [53] S. Pirandola, “Satellite Quantum Communications: Fundamental Bounds and Practical Security,” Physical Review Research, vol. 3, no. 2, 023130, 2021. [54] J. H. Mathews and K. D. Fink, Numerical Methods Using MATLAB, vol. 4. Pearson Prentice Hall Upper Saddle River, NJ, 2004. [55] A. Leverrier, “Security of Continuous-Variable Quantum Key Distribution via a Gaussian de Finetti Reduction,” Physical Review Letters, vol. 118, no. 20, 200501, 2017. [56] E. Villase˜nor, R. Malaney, K. A. Mudge, and K. J. Grant, “Atmospheric Effects on Satellite-to-Ground Quantum Key Distribution Using Coherent States,” in GLOBECOM 2020-2020 IEEE Global Communications Conference, 1–6, IEEE, 2020. [57] J. Mateo, Efficient Information Reconciliation for Quantum Key Distribution. PhD thesis, Universidad Politecnica de Madrid, 2011. [58] T. Tian, C. R. Jones, J. D. Villasenor, and R. D. Wesel, “Selective Avoidance of Cycles in Irregular LDPC Code Construction,” IEEE Transactions on Communications, vol. 52, no. 8, 1242–1247, 2004. [59] D. V. Nguyen, S. K. Chilappagari, M. W. Marcellin, and B. Vasic, “On the Construction of Structured LDPC Codes Free of Small Trapping Sets,” IEEE Transactions on Information Theory, vol. 58, no. 4, 2280– 2302, 2012. [60] S. Landner and O. Milenkovic, “Algorithmic and Combinatorial Analysis of Trapping Sets in Structured LDPC Codes,” in International Conference on Wireless Networks, Communications and Mobile Computing, vol. 1, 630–635, IEEE, 2005. [61] X. Wang, Y. Zhang, S. Yu, and H. Guo, “High Speed Error Correction for Continuous-Variable Quantum Key Distribution with Multi-Edge Type LDPC Code,” Scientific Reports, vol. 8, no. 1, 1–7, 2018. [62] Y. Li, X. Zhang, Y. Li, B. Xu, L. Ma, J. Yang, and W. Huang, “HighThroughput GPU Layered Decoder of Quasi-Cyclic Multi-Edge Type Low Density Parity Check Codes in Continuous-Variable Quantum Key Distribution Systems,” Scientific Reports, vol. 10, no. 1, 1–11, 2020. [63] F. Grosshans, “Collective Attacks and Unconditional Security in Continuous Variable Quantum Key Distribution,” Physical Review Letters, vol. 94, 020504, 2005. [64] M. Navascu´es, F. Grosshans, and A. Acin, “Optimality of Gaussian Attacks in Continuous-Variable Quantum Cryptography,” Physical Review Letters, vol. 97, no. 19, 190502, 2006. [65] S. Fossier, E. Diamanti, T. Debuisschert, R. Tualle-Brouri, and P. Grangier, “Improvement of Continuous-Variable Quantum Key Distribution Systems by Using Optical Preamplifiers,” Journal of Physics B: Atomic, Molecular and Optical Physics, vol. 42, no. 11, 114014, 2009. [66] P. Jouguet, S. Kunz-Jacques, E. Diamanti, and A. Leverrier, “Analysis of Imperfections in Practical Continuous-Variable Quantum Key Distribution,” Physical Review A, vol. 86, no. 3, 032309, 2012.

Optimised Multithreaded CV-QKD Reconciliation for Global Quantum Networks: Conclusion & References

Welcome to HackerNoon Writing Prompts! See all other stories published using this template . here To start, tell us a little bit about yourself Yo, peeps! A fellow content creator on this side. Gather 'round 'cause I stumbled upon this dope way to whip up AI Reels with avatars, and guess what? It's all free! No cap, just follow these steps, and you'll be able to make awesome reels using AI. Describe the life hack(s) you use on a regular basis First things first, kick those boring narrations to the curb. Head over to Clipchamp – it's free, by the way. Pick an AI voice that vibes with your style – deep, sassy, funny, you name it. Hit that record button, and boom, instant voiceover magic without spending a dime. Step 1: Voiceovers with Clipchamp Now, let's put a face to that killer voice. is the go-to spot, and yep, it's free too. Generate a slick AI avatar that matches your voiceover tone. Imagine having your own hype man, but way cooler. This step is where your personal style starts to shine. Step 2: Avatar with Leonardo.ai Leonardo.ai Time to make that avatar groove! Lalamu Studio is another gem in the free toolkit. It creates a killer lip-sync animation based on your audio. Your avatar's about to dance like it's the main act at a concert – and yes, still free. Step 3: Lip-Sync with Lalamu Studio Blurry mess? No more! Grab Vmake Video Enhancer – there are free trials to test the waters. This tool is your superhero, giving your video that crystal-clear upgrade. It's like your content just got a glow-up, all while staying budget-friendly. Step 4: Video Upscaling with Vmake Video Enhancer Subtitles are the key to global reach. Submagic is your move. It generates subtitles for your video – the free version has a watermark, but a bit of editing magic, and it's gone. Now, your content's ready to conquer the world. Step 5: Cool Subtitles with Submagic Back to Clipchamp for the finishing touches. Throw in some fire music, smooth transitions, and eye-catching text overlays. It's like adding the cherry on top of your content masterpiece. Step 6: Final Touches with Clipchamp There you have it, fam – a tutorial to make AI Reels with avatars for free. I found out about all these awesome tools from just one place. It is a website called . I tried and tested tens of tools present there and found the best results with the tools mentioned above. If you have suggestions for better tools, I would love to know them. Seekme.ai Tell me about them below in the comments. Finally, don't forget to experiment, get creative, and find your unique style. Trust me, this is a game-changer for broke creators like us. Give it a shot, and let the world see your epic content! Which part of life has it/they helped you with? It has helped me speed up my content creation game a lot. Just in doing the research to achieve this, I learned a lot more things that I regularly use now. Where did you first hear about it/them? I didn’t. I had been given the task of making a reel using AI avatars. While looking for it, I went to an AI tools directory, in my case Seekme.ai, and just tried and tested the tools I found there. It was really time-taking, but the final result was great. Attach a photo of the hack if applicable. Who are your favorite motivational speakers, online lecturers, and/or tinkerers? David Goggins. Which podcasts, books, and movies do you like and why? Andrew Huberman - The day I saw his episode on NSDR, I became an instant fan. Since then, I’ve been following his content regularly. Would you like to take a stab at answering some of these questions? The link for the template is , just start writing! Interested in what others had to say in their answers? Click . Interested in reading the content from all of our writing prompts? Click . HERE HERE HERE

How to Make AI Avatar Reels for Free

Table of Contents A Deep Dive into Voice Cloning with SoftVC VITS and Bert-VITS2 Prepare Dataset Extract from a Song UVR Workflows Preparation for vocal recording Cheapskate’s Audio Equipment Audacity workflows audio-slicer Cleaning dataset Match loudness so-vits-svc Set up environment Initialization Download pre-trained models Dataset Preparation Edit Configs Training Inference Audio Editing so-vits-svc-fork Installation Preparation Training Inference DDSP-SVC Preparation Training Inference Bert-vits2-V2.3 Initialization Preparation Transcription Training and Inference vits-simple-api Tweaks Share models In the , I tried a little bit of and found it interesting. So, I decided to train a usable model with my own voice. previous post TTS Generation WebUI This voice cloning project explores both SVC for Voice Changing and for Text-to-Speech. There is no one tool that does all jobs. VITS I have tested several tools for this project. Many of the good guides, like , , and , are in Chinese. So, I thought it would be useful to post my notes in English. this this this Although has been archived for a few months, probably due to oppression, it is still the tool for the best result. so-vits-svc Other related tools such as , , , and provide either faster/liter optimization, more features, or better interfaces. so-vits-svc-fork so-vits-svc-5.0 DDSP-SVC RVC But with enough time and resources, none of these alternatives can compete with the superior result generated by the original so-vits-svc. For TTS, a new tool called works fantastically and has already matured with its final release last month. It has some very different use cases, for example, audio content creation. Bert-VITS2 Prepare Dataset The audio files of the dataset should be in WAV format, 44100 Hz, 16bit, mono, 1-2 hours ideally. Extract From a Song is the easiest tool for this job. There is a that explains everything in detail. Ultimate Vocal Remover thread UVR Workflows Remove and extract Instrumental Model: VR - UVR(4_HP-Vocal-UVR) Settings: 512 - 10 - GPU Output and unclean vocal Instrumental Remove and extract background vocals. Model: VR - UVR(5_HP-Karaoke-UVR) Settings: 512 - 10 - GPU Output and unclean main vocal background vocal Remove reverb and noise. Model: VR - UVR-DeEcho-DeReverb & UVR-DeNoise Settings: 512 - 10 - GPU - No Other Only Output clean main vocal (Optional) Using RipX (non-free) to perform a manual fine-cleaning. Preparation for Vocal Recording It’s better to record in a treated room with a condenser microphone, otherwise, use a directional or dynamic microphone to reduce noise. Cheapskate’s Audio Equipment The very first time I got into music was during high school, with the blue Sennheiser MX500 and Koss Porta Pro. I still remember the first time I was recording a song that was on a Sony VAIO with Cool Edit Pro. Nowadays, I still resist spending a lot of money on audio hardware as an amateur because it is literally a money-sucking black hole. Nonetheless, I really appreciate the reliability of those cheap production equipment. The core part of my setup is a Behringer UCA202, and it’s perfect for my use cases. I bought it for $10 during a price drop. It is a so-called “Audio Interface” but basically just a sound card with multiple ports. I used RCA to 3.5mm TRS cables for my headphones, a semi-open K240s for regular output, and a closed-back HD669/MDR7506 for monitor output. All three mentioned headphones are under $100 for the normal price. And there are clones from Samson, Tascam, Knox Gear, and more out there for less than $50. For the input device, I’m using a dynamic microphone for the sake of my environmental noises. It is an SM58 copy (Pyle) + a Tascam DR-05 recorder (as an amplifier). Other clones such as SL84c or wm58 would do it too. I use an XLR to 3.5mm TRS cable to connect the microphone to the MIC/External-input of the recorder, and then use an AUX cable to connect between the line-out of the recorder and the input of the UCA202. It’s not recommended to buy an “audio interface” and a dedicated amplifier to replicate my setup. A $10 c-media USB sound card should be good enough. The Syba model that I owned is capable of “pre-amping” dynamic microphones directly and even some lower-end phantom-powered microphones. The setup can go extremely cheap ($40~60), but with UCA202 and DR-05, the sound is much cleaner. And I really like the physical controls, versatility, and portability of my old good digital recorder. Audacity Workflows Although when I was getting paid as a designer, I was pretty happy with Audition. But for personal use on a fun project, Audacity is the way to avoid the chaotic evil of Adobe. Noise Reduction Dereverb Truncate Silence Normalize audio-slicer Use or to slice the audio file into small pieces for later use. audio-slicer audio-slicer (gui) The default setting works great. Cleaning Dataset Remove those very short ones and re-slice those that are still over 10 seconds. In case of a large dataset, remove all that are less than 4 sec. In the case of a small dataset, remove those only under 2 sec. If necessary, perform a manual inspection for every single file. Match Loudness Use Audacity again with ; 0db should do it. Loudness Normalization so-vits-svc Set Up the Environment Virtual environment is essential to run multiple Python tools inside one system. I used to use VMs and Docker, but now, I found that is way quicker and handier than the others. anaconda Create a new environment for so-vits-svc, and activate it. conda create -n so-vits-svc python=3.8
conda activate so-vits-svc Then, install requirements. git clone https://github.com/svc-develop-team/so-vits-svc
cd so-vits-svc

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

#for linux
pip install -r requirements.txt

#for windows
pip install -r requirements_win.txt
pip install --upgrade fastapi==0.84.0
pip install --upgrade gradio==3.41.2
pip install --upgrade pydantic==1.10.12
pip install fastapi uvicorn Initialization Download pre-trained models pre-train wget https://huggingface.co/WitchHuntTV/checkpoint_best_legacy_500.pt/resolve/main/checkpoint_best_legacy_500.pt wget https://huggingface.co/lj1995/VoiceConversionWebUI/resolve/main/rmvpe.pt logs/44k wget https://huggingface.co/datasets/ms903/sovits4.0-768vec-layer12/resolve/main/sovits_768l12_pre_large_320k/clean_D_320000.pth wget https://huggingface.co/datasets/ms903/sovits4.0-768vec-layer12/resolve/main/sovits_768l12_pre_large_320k/clean_G_320000.pth logs/44k/diffusion wget https://huggingface.co/datasets/ms903/Diff-SVC-refactor-pre-trained-model/resolve/main/fix_pitch_add_vctk_600k/model_0.pt (Alternative) wget https://huggingface.co/datasets/ms903/DDSP-SVC-4.0/resolve/main/pre-trained-model/model_0.pt (Alternative) wget https://huggingface.co/datasets/ms903/Diff-SVC-refactor-pre-trained-model/blob/main/hubertsoft_fix_pitch_add_vctk_500k/model_0.pt pre-train/nsf_hifigan wget -P pretrain/ https://github.com/openvpi/vocoders/releases/download/nsf-hifigan-v1/nsf_hifigan_20221211.zip unzip -od pretrain/nsf_hifigan pretrain/nsf_hifigan_20221211.zip Dataset Preparation Put all Prepared audio.wav files into dataset_raw/character cd so-vits-svc
python resample.py --skip_loudnorm
python preprocess_flist_config.py --speech_encoder vec768l12 --vol_aug
python preprocess_hubert_f0.py --use_diff Edit Configs The file is located at configs/config.json : the frequency of printing log : the frequency of saving checkpoints : total steps : numbers of saved checkpoints, 0 for unlimited. : fp32 In my case, : the smaller the faster (rougher), the larger the slower (better). log interval eval interval epochs keep ckpts half_type batch_size Recommended batch_size per VRAM: 4=6G；6=8G；10=12G；14=16G；20=24G Keep default for configs/diffusion.yaml Training python cluster/train_cluster.py --gpu
python train_index.py -c configs/config.json
python train.py -c configs/config.json -m 44k
python train_diff.py -c configs/diffusion.yaml On training steps: Use to train the main model; usually, 20k-30k would be usable, and 50k and up would be good enough. This can take a few days depending on the GPU speed. train.py Feel free to stop it by , and it will be continued training by re-run anytime. ctrl+c python train.py -c configs/config.json -m 44k Use to train the diffusion model; training steps is recommended at 1/3 of the main model. train_diff.py Be aware of over-training. Use to monitor the plots to see if it goes flat. tensorboard --logdir=./logs/44k Change the from 0.0001 to 0.00005 if necessary. learning rate When done, share/transport these files for inference. config/ config.json diffusion.yaml logs/44k feature_and_index.pkl kmeans_10000.pt model_0.pt G_xxxxx.pt Inference It’s time to try out the trained model. I’d prefer Webui for the convenience of tweaking the parameters. But before firing it up, edit the following lines in for LAN access: webUI.py os.system("start http://localhost:7860")
app.launch(server_name="0.0.0.0", server_port=7860) Run ; then access its from a web browser. python webUI.py ipaddress:7860 The webui has no English localization, but would be helpful. Immersive Translate Most parameters would work well with the default value. Refer to and to make changes. this this Upload these 5 files: and its main model.pt config.json and its diffusion model.pt diffusion.yaml Either cluster model for speaking or feature retrieval for singing. kmeans_10000.pt feature_and_index.pkl is for speaking only, not for singing. Recommend when using. F0 predictor RMVPE is useful when singing a feminine song using a model with a masculine voice, or vice versa. Pitch change is the way of controlling the tone. Use to get the clearest speech, and use to get the closest tone to the model. Clustering model/feature retrieval mixing ratio 0.1 0.9 should be set around , it enhances the result at steps. shallow diffusion steps 50 30-100 Audio Editing This procedure is optional. Just for the production of a better song. I won’t go into details about this since the audio editing software, or so-called DAW (digital audio workstation), that I’m using is non-free. I have no intention to advocate proprietary software even though the entire industry is paywalled and closed-source. Audacity supports multitrack, effects, and a lot more. It does load some advanced VST plugins as well. It’s not hard to find tutorials on mastering songs with Audacity. Typically, the mastering process should be mixing/balancing, EQ/compressing, reverb, and imaging. The more advanced the tool is, the easier the process will be. I’ll definitely spend more time on adopting Audacity for my mastering process in the future, and I recommend everyone do so. so-vits-svc-fork This is a so-vits-svc with real-time support, and the models are compatible. Easier to use but does not support the Diffusion model. For dedicated real-time voice changing, a is recommended. fork voice-changer Installation conda create -n so-vits-svc-fork python=3.10 pip
conda activate so-vits-svc-fork

git clone https://github.com/voicepaw/so-vits-svc-fork
cd so-vits-svc-fork

python -m pip install -U pip setuptools wheel
pip install -U torch torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install -U so-vits-svc-fork
pip install click
sudo apt-get install libportaudio2 Preparation Put dataset .wav files into so-vits-svc-fork/dataset_raw svc pre-resample
svc pre-config Edit a in . This fork is a larger size than the original. batch_size configs/44k/config.json Training svc pre-hubert
svc train -t
svc train-cluster Inference Use GUI with . This requires a local desktop environment. svcg Or use CLI with for real time and for generating. svc vc svc infer -m "logs/44k/xxxxx.pth" -c "configs/config.json" raw/xxx.wav DDSP-SVC requires fewer hardware resources and runs faster than so-vits-svc. It supports both real-time and diffusion models (Diff-SVC). DDSP-SVC conda create -n DDSP-SVC python=3.8
conda activate DDSP-SVC

git clone https://github.com/yxlllc/DDSP-SVC
cd DDSP-SVC

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt Refer to the for the two files: Initialization section pretrain/rmvpe/model.pt
pretrain/contentvec/checkpoint_best_legacy_500.pt Preparation python draw.py
python preprocess.py -c configs/combsub.yaml
python preprocess.py -c configs/diffusion-new.yaml Edit configs/ batch_size: 32  (16 for diffusion)
cache_all_data: false
cache_device: 'cuda'
cache_fp16: false Training conda activate DDSP-SVC
python train.py -c configs/combsub.yaml
python train_diff.py -c configs/diffusion-new.yaml

tensorboard --logdir=exp Inference It’s recommended to use since it includes both DDSP and diffusion model. main_diff.py python main_diff.py -i "input.wav" -diff "model_xxxxxx.pt" -o "output.wav" Real-time GUI for voice cloning: python gui_diff.py Bert-vits2-V2.3 This is a TTS tool that is completely different from everything above. By using it, I have already created several audiobooks with my voice for my parents, and they really enjoy it. Instead of using the , I used the fork by for an easier setup. original v3u Initialization conda create -n bert-vits2 python=3.9
conda activate bert-vits2

git clone https://github.com/v3ucn/Bert-vits2-V2.3.git
cd Bert-vits2-V2.3

pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 torchaudio==2.0.2+cu118 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt Download pre-trained models (includes Chinese, Japanese, and English): wget -P slm/wavlm-base-plus/ https://huggingface.co/microsoft/wavlm-base-plus/resolve/main/pytorch_model.bin
wget -P emotional/clap-htsat-fused/ https://huggingface.co/laion/clap-htsat-fused/resolve/main/pytorch_model.bin
wget -P emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/ https://huggingface.co/audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim/resolve/main/pytorch_model.bin
wget -P bert/chinese-roberta-wwm-ext-large/ https://huggingface.co/hfl/chinese-roberta-wwm-ext-large/resolve/main/pytorch_model.bin
wget -P bert/bert-base-japanese-v3/ https://huggingface.co/cl-tohoku/bert-base-japanese-v3/resolve/main/pytorch_model.bin
wget -P bert/deberta-v3-large/ https://huggingface.co/microsoft/deberta-v3-large/resolve/main/pytorch_model.bin
wget -P bert/deberta-v3-large/ https://huggingface.co/microsoft/deberta-v3-large/resolve/main/pytorch_model.generator.bin
wget -P bert/deberta-v2-large-japanese/ https://huggingface.co/ku-nlp/deberta-v2-large-japanese/resolve/main/pytorch_model.bin Create a character model folder mkdir -p Data/xxx/models/ Download base models: !wget -P Data/xxx/models/ https://huggingface.co/OedoSoldier/Bert-VITS2-2.3/resolve/main/DUR_0.pth
!wget -P Data/xxx/models/ https://huggingface.co/OedoSoldier/Bert-VITS2-2.3/resolve/main/D_0.pth
!wget -P Data/xxx/models/ https://huggingface.co/OedoSoldier/Bert-VITS2-2.3/resolve/main/G_0.pth
!wget -P Data/xxx/models/ https://huggingface.co/OedoSoldier/Bert-VITS2-2.3/resolve/main/WD_0.pth

#More options
https://openi.pcl.ac.cn/Stardust_minus/Bert-VITS2/modelmanage/model_filelist_tmpl?name=Bert-VITS2_2.3%E5%BA%95%E6%A8%A1
https://huggingface.co/Erythrocyte/bert-vits2_base_model/tree/main
https://huggingface.co/OedoSoldier/Bert-VITS2-2.3/tree/main Edit by replacing all to train_ms.py bfloat16 float16 Edit for LAN access: webui.py webbrowser.open(f"start http://localhost:7860")
app.launch(server_name="0.0.0.0", server_port=7860) Edit for and Data/xxx/config.json batch_size spk2id Preparation Similar workflow as in the . previous section Remove noise and silence, normalization, then put the un-sliced WAV file into . Data/xxx/raw Edit for , , and . config.yml dataset_path num_workers keep_ckpts Run to slice the WAV file. python3 audio_slicer.py Clean the dataset ( ) by removing small files that are under 2 sec. Data/xxx/raw Transcription Install whisper pip install git+https://github.com/openai/whisper.git To turn off language auto-detection, set it to English only, and use model; edit as below: large short_audio_transcribe.py # set the spoken language to english
    print('language: en')
    lang = 'en'
    options = whisper.DecodingOptions(language='en')
    result = whisper.decode(model, mel, options)
	
    # set to use large model
    parser.add_argument("--whisper_size", default="large")

    #Solve error "Given groups=1, weight of size [1280, 128, 3], expected input[1, 80, 3000] to have 128 channels, but got 80 channels instead" while using large model
    mel = whisper.log_mel_spectrogram(audio,n_mels = 128).to(model.device) Run to start the transcription. python3 short_audio_transcribe.py Re-sample the sliced dataset: python3 resample.py --sr 44100 --in_dir ./Data/zizek/raw/ --out_dir ./Data/zizek/wavs/ Preprocess transcription: python3 preprocess_text.py --transcription-path ./Data/zizek/esd.list Generate BERT feature config: python3 bert_gen.py --config-path ./Data/zizek/configs/config.json Training and Inference Run to start training python3 train_ms.py Edit for model path: config.yml model: "models/G_20900.pth" Run to start webui for inference python3 webui.py vits-simple-api is a web frontend for using trained models. I use this mainly for its long text support which the original project doesn’t have. vits-simple-api git clone https://github.com/Artrajz/vits-simple-api
git pull https://github.com/Artrajz/vits-simple-api
cd vits-simple-api

conda create -n vits-simple-api python=3.10 pip
conda activate vits-simple-api && 

pip install -r requirements.txt (Optional) Copy pre-trained model files from to Bert-vits2-V2.3/ vits-simple-api/bert_vits2/ Copy and to Bert-vits2-V2.3/Data/xxx/models/G_xxxxx.pth Bert-vits2-V2.3/Data/xxx/config.json vits-simple-api/Model/xxx/ Edit for and as preferred config.py MODEL_LIST Default parameter Edit as below: Model/xxx/config.json "data": {
    "training_files": "Data/train.list",
    "validation_files": "Data/val.list",
	
  "version": "2.3" Check/Edit in as model_list config.yml [xxx/G_xxxxx.pth, xxx/config.json] Run python app.py Tweaks for tone, for randomness, for pronunciation, and for speed. and are self-explanatory SDP Ratio Noise Noise_W Length emotion style Share Models In its , there are a lot of VITS models shared by others. You can try it out first, and then download the desired models from . Hugging Face repo Files The is widely used in some content creation communities because of its high quality. It contains hundreds of characters, although only Chinese and Japanese are supported. Genshin model In , there are a lot of Bert-vits2 models that are made from popular Chinese streamers and VTubers. another repo There are already projects making AI Vtubers like and . I’m looking forward to how this technology can change the industry in the near future. this this https://techshinobi.org/posts/voice-vits/https://techshinobi.org/posts/voice-vits/

HUBER

huber.co.in

-- employees

Since n.d.

COMPANY RANKING

HUBER

EVERGREEN INDEX #5010

Representing this company? Click here to claim and customize this page!

Huber WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

HUBER

huber.co.in

-- employees

Since n.d.

COMPANY RANKING

HUBER

EVERGREEN INDEX #5010

RELATED COMPANIESRANK

STORIES ABOUT HUBER

Representing this company? Click here to claim and customize this page!

Huber WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!