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This is a personal website introducing my research activities. Please feel free to contact me on my e-mail address, or LinkedIn.

  1. YoYottaID software runs on Apple Mac OSX El Capitan 10.11, Yosemite 10.10.5, Mavericks 10.9.5 Mac OSX 10.11.4, 10.10.5 or 10.9.5 is needed for YoYottaID Conform and Transcode options. This version of YoYottaID does not support LTO tape drives or the LTFS protocol.
  2. TeamViewer Host is used for 24/7 access to remote computers, which makes it an ideal solution for uses such as remote monitoring, server maintenance, or connecting to a PC or Mac in the office or at home. Install TeamViewer Host on an unlimited number of computers and devices. As a licensed user, you have access to them all!
  3. This is a personal website introducing my research activities. Please feel free to contact me on my e-mail address, or LinkedIn. Yotaro Kubo received the B.E., M.E., and Dr.Eng. Degrees from Waseda University, Tokyo, Japan, in 2007, 2008, and 2010, respectively. He was a visiting scientist at RWTH Aachen University for six monthes in 2010.
  4. YoYottaID software runs on Apple Mac OSX El Capitan 10.11, Yosemite 10.10.5, Mavericks 10.9.5 Mac OSX 10.11.4, 10.10.5 or 10.9.5 is needed for YoYottaID Conform and Transcode options.

Short Bio

Dr. Yotaro Kubo received the B.E., M.E., and Dr.Eng. degrees from Waseda University, Tokyo, Japan, in 2007, 2008, and 2010, respectively. He was a visiting scientist at RWTH Aachen University for six monthes in 2010. After that period, he joined Nippon Telegraph and Telephone Corporation (NTT) and had been with NTT Communication Science Laboratories. From 2014 to 2019, he was with Amazon and developed/ investigated speech recognition for voice search and personal assistants. Since 2019, he is a research scientist at Google. His research interest includes generative/ discriminative hybrid modeling, kernel-based probabilistic models, and integration of probabilistic systems. He is a member of the IEEE, the International Speech Communication Association (ISCA), and the Acoustical Society of Japan (ASJ).

Research Interests

  • Machine Learning for Speech Signal and Spoken Language Processing
    • Generative/ Discriminative-hybrid training of hidden Markov models
    • Flat-direct classifiers for automatic speech recognition enhanced by using nonlinear feature transformation
    • Deep learning with discrete parameters/ variables for network structure estimation
  • Software architecture for efficient research

Publications

Refereed Journal Papers

Yota Access Mac Os

  • Y. Kubo, S. Watanabe, T. Hori, A. Nakamura 'Structural Classification Methods based on Weighted Finite-State Transducers for Automatic Speech Recognition,' IEEE Transactions on Audio, Speech, and Language Processing, Vol. 20, No. 8, pp. 2240 - 2251, Oct 2012. (IEEExplorer)
  • Y. Kubo, S. Okawa, A. Kurematsu, K. Shirai, 'Temporal AM-FM Combination for Robust Speech Recognition,' Speech Communication, Vol. 54, No. 5, pp. 716-725, May 2011.(Science Direct)
  • Y. Kubo, S. Watanabe, A. Nakamura, E. McDermott, T. Kobayashi, 'A Sequential Pattern Classifier Based on Hidden Markov Kernel Machine and Its Application to Phoneme Classification,' IEEE Journal of Selected Topics in Signal Processing, Vol. 4, No. 6, pp. 974-984, December 2010. (IEEExplorer)
  • Y. Kubo, S. Okawa, A. Kurematsu, K. Shirai, 'Recognizing Reverberant Speech Based on Amplitude and Frequency Modulation,' IEICE Trans. on Inf. and Syst., Vol. E-61-D, No., pp. 448-456, March 2008.
  • Y. Kubo, M. Honda, K. Shirai, T. Komori, S. Nobumasa, T. Takagi, 'An Improved High-quality MPEG-2/4 Advanced Audio Coding Encoder,' Acoustical Science & Technology, Vol. 29, No. 6, pp. 362-371, December 2008. (Full Text via JStage)
  • M. Delcroix, K. Kinoshita, T. Naktani, S. Araki, A. Ogawa, T. Hori, S. Watanabe, M. Fujimoto, T. Yoshioka, T. Oba, Y. Kubo, M. Souden, S.-J. Hahm, A. Nakamura, 'Speech recognition in living rooms: Integrated speech enhancement and recognition system based on spatial, spectral & temporal modeling of sounds,' Computer Speech and Language, Vol. 27, No. 3, pp. 851-873, Elsevier. (Science Direct)
  • M. Delcroix, T. Yoshioka, A. Ogawa, Y. Kubo, M. Fujimoto, N. Ito, K. Kinoshita, M. Espi, S. Araki, T. Hori, T. Nakatani, 'Strategies for distant speech recognitionin reverberant environments,' EURASIP Journal on Advances in Signal Processing, (2015) 2015: 60. https://doi.org/10.1186/s13634-015-0245-7.

Refereed Conference/ Workshop Papers

  • Y. Kubo, M. Bacchiani, 'Joint Phoneme-Grapheme Model for End-to-end Speech Recognition,' Proc. ICASSP-2020, Barcelona, Spain, May 2020.
  • Y. Kubo, G. Tucker, S. Wiesler, 'Compacting Neural Network Classifiers via Dropout Training,' Proc. NIPS Workshop on Efficient Methods for Deep Neural Networks, Barcelona, Spain, Dec 2016. (ArXiv)
  • Y. Kubo, J. Suzuki, T. Hori, A. Nakamura, 'Restructuring Output Layers of Deep Neural Networks Using Minimum Risk Parameter Clustering,' Proc. Interspeech 2014, Singapore, Sept 2014. [pdf]
  • Y. Kubo, T. Hori, A. Nakamura, 'A Method for Structure Estimation of Weighted Finite-State Transducers and Its Application To Grapheme-to-Phoneme Conversion,' Proc. Interspeech 2013, Lyon, France, August 2013.
  • Y. Kubo, T. Hori, A. Nakamura, 'Large Vocabulary Continuous Speech Recognition Based on WFST Structured Classifiers and Deep Bottleneck Features,' Proc. ICASSP 2013, Vancouver, Canada, May 2013. [pdf]
  • Y. Kubo, T. Hori, A. Nakamura, 'Integrating Deep Neural Networks into Structured Classification Approach based on Weighted Finite-State Transducers,' Proc. INTERSPEECH 2012, Portland, Oregon, U.S., September 2012. [pdf]
  • Y. Kubo, S. Watanabe, A. Nakamura, 'Decoding Network Optimization Using Minimum Transition Error Training,' Proc. ICASSP 2012, Kyoto, Japan, pp. 4197-4200, March 2012. [pdf]
  • Y. Kubo, S. Watanabe, A. Nakamura, S. Wiesler, R. Schlueter, H. Ney, 'Basis Vector Orthogonalization for an Improved Kernel Gradient Matching Pursuit Method,' Proc. ICASSP 2012, Kyoto, Japan, pp. 1909-1912, March 2012. [pdf]
  • Y. Kubo, S. Wiesler, R. Schlueter, H. Ney, S. Watanabe, A. Nakamura, T. Kobayashi 'Subspace Pursuit Method for Kernel-Log-Linear Models,' Proc. ICASSP 2011, Prague, Czech, May 2011. [pdf]
  • Y. Kubo, S. Watanabe, A. Nakamura, T. Kobayashi, 'A Regularized Discriminative Training Method of Acoustic Models Derived by Minimum Relative Entropy Discrimination,' Proc. INTERSPEECH-2010, Makuhari, Japan, September 2010. [pdf]
  • Y. Kubo, S. Okawa, A. Kurematsu, K. Shirai, 'A Comparative Study on AM and FM Features,' Proc. Interspeech-2008, Brisbane, September 2008. [pdf]
  • Y. Kubo, S. Okawa, A. Kurematsu, K. Shirai, 'Independent Feature Selection Algorithms for the Creation of Multistream Speech Recognizers,' Proc. ITRW on Speech Analysis and Processing for Knowledge Discovery, Aalborg, June 2008.
  • Y. Kubo, S. Okawa, A. Kurematsu, K. Shirai, 'Noisy Speech Recognition Using Temporal AM-FM Combination,' Proc. ICASSP-2008, Las Vegas, pp. 4709-4712, April 2008. [pdf]
  • Y. Kubo, S. Okawa, A. Kurematsu, K. Shirai, 'A Study on Temporal Features Derived by Analytic Signal,' Proc. Interspeech-2007, Antwerpen, pp. 1130-1133, September 2007. [pdf]
  • M. Espi, M. Fujimoto, Y. Kubo, T. Nakatani, 'Spectrogram Patch Based Acoustic Event Detection and Classification in Speech Overlapping Conditions,' Proc. HSCMA, Nancy, France, May 2014.
  • M. Delcroix, T. Yoshioka, A. Ogawa, Y. Kubo, M. Fujimoto, N. Ito, K. Kinoshita, M. Espi, T. Hori, T. Nakatani, A. Nakamura, 'Linear Prediction-Based Dereverberation With Advanced Speech Enhancement and Recognition Technologies for The Reverb Challenge,' Proc. REVERB Workshop, Florence, Italy, May 2014.
  • M. Fujimoto, Y. Kubo, T. Nakatani, 'Unsupervised non-parametric Bayesian modeling of non-stationary noise for model-based noise suppression,' Proc. ICASSP 2014, Frolence, Italy, May 2014.
  • T. Hori, Y. Kubo, A. Nakamura, 'Real-time one-pass decoding with recurrent neural network language model for speech recognition,' Proc. ICASSP 2014, Frolence, Italy, May 2014.
  • M. Blondel, Y. Kubo, N. Ueda, 'Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion,' Proc. AISTATS 2014, Reykjavik, Iceland, April 2014.
  • M. Delcroix, Y. Kubo, T. Nakatani, A. Nakamura, 'Is Speech Enhancement Pre-Processing Still Relevant When Using Deep Neural Networks for Acoustic Modeling?' Proc. Interspeech 2013, Lyon, France, August 2013.
  • S. Watanabe, Y. Kubo, T. Oba, T. Hori, A. Nakamura, 'Bag of Arcs: New Representation of Speech Segment Features Based on Finite State Machines,' Proc. ICASSP 2012, Kyoto, Kapan, pp. 4201-4204, March 2012.
  • M. Delcroix, K. Kinoshita, T. Nakatani, S. Araki, A. Ogawa, T. Hori, S. Watanabe, M. Fujimoto, T. Yoshioka, T. Oba, Y. Kubo, M. Souden, S.-J. Hahm, A. Nakamura, 'Speech recognition in the presence of highly non-stationary noise based on spatial, spectral and temporal speech/ noise modeling combined with dynamic variance adaptation,' Proc. CHiME (Computational Hearing in Multisource Environments) 2011, September 2011.
  • S. Wiesler, A. Richard, Y. Kubo, R. Schlueter, H. Ney, 'Feature Selection for Log-Linear Acoustic Models,' Proc. ICASSP 2011, Prague, Czech, May 2011.

Theses

  • Y. Kubo, 'Automatic Speech Recognition Based on Temporal Analysis of Amplitude and Frequency Modulation,' Master of Informatics and Computer Science, Waseda University, 2008 (written in Japanese).
  • Y. Kubo, 'Regularized Discrimination of High-Dimensional Signal Representations for Automatic Speech Recognition,' Doctor of Engineering, Waseda University, 2010 (pdf)(HTML)

Tutorial Articles (in Japanese)

  • Y. Kubo, '音声認識のための深層学習 (Deep Learning for Speech Recognition),' Journal of the Japanese Society for Artificial Intelligence, Vol. 29, No. 1, pp. 62-71, Jan 2014.
  • T. Hori, S. Araki, Y. Kubo, A. Ogawa, T. Oba, A. Nakamura, '自然な会話を聞き取る音声認識技術 (Speech Recognition Technologies for Natural Conversation Scenes),' Nikkei Electronics, 2013.10.24, Oct 2013.
  • Y. Kubo, A. Ogawa, T. Hori, A. Nakamura, 'Speech Recognition Based on Unified Model of Acoustic and Language Aspects of Speech,' NTT Technical Review, Vol.11, No.12. (English Version in 'NTT Technical Review') or (Japanese Version in 'NTT技術ジャーナル')
  • Y. Kubo, 'ディープラーニングによるパターン認識 (Deep Learning for Pattern Recognition),' IPSJ Magazine, Vol. 54, No. 5, pp. 500-508, May 2013 (IPSJ Digital Library).
  • K. Shirai, T. Kobayashi, M. Abe, K. Iwata, R. Imai, H. Kikuchi, K. Ohtsuki, H. Fujisawa, M. Honda, Y. Hayashi, K. Mano, T. Takezawa, S. Takahashi, S. Okawa, K. Hoashi, N. Masaki, N. Osaka, '音声言語処理の潮流 (The Tide of Spoken Language Processing),' Corona Publishing, Mar 2010 (I wrote explanations about tandem approach and neural networks).
  • Y. Kubo, 'Mac OS Xのアプリケーション開発 (Developing Applications for Mac OS X),' UNIX Magazine 2006.03, Mar 2006.

Book

  • S. Asoh, M. Yasuda, S. Maeda, D. Okanohara, T. Okatani, Y. Kubo, D. Bollegala (Ed: T. Kamishima), '深層学習 --Deep Learning--,' Kindai Kagakusya, Nov 2015. (written in Japanese; also available in Korean http://jpub.tistory.com/m/779)

Domestic Workshop Papers (Not refereed; excerpt)

Academic Activities

  • Member of IEEE (The Institute of Electrical and Electronics Engineers)
  • Member of ISCA (International Speech Communication Association)
  • Member of ASJ (The Acoustical Society of Japan)
  • Reviewer of the following scientific journals
    • IEEE Transaction on Signal Processing
    • IEEE Transaction on Audio, Speech and Language Processing
    • Speech Communication
    • IEICE Transactions on Information and Systems

Talks/ Lectures

  • 'A Speech Recognition Toolkit based on Python', EuroSciPy-2010, Paris, France, July 2010.
  • 'An application method of minimum relative entropy discrimination for hidden Markov models,' InterACT Talk (Karlsruhe University), Karlsruhe, Germany, Sep. 2010. (Host: Dr. Sebastian Stueker)
  • 'Subspace Pursuit Methods for Kernel-Log-Linear Models,' in National Institute of Information and Communications Technology (NICT), Kyoto, Japan, Nov. 2011.
  • 'High-Dimensional Log-Linear Models for Automatic Speech Recognition,' in Microsoft Research Asia, Beijing, China, Jan. 2012.
  • 'Python in Automatic Speech Recognition Research (音声認識研究におけるPython),' Tokyo.SciPy #004 (aka Kan.SciPy #001), Jun. 2012.
  • 'Automatic Recognition of Conversational Speech,' in Microsoft Research Redmond, WA, USA, Sep. 2012. (with Dr. Seong-Jun Hahm)
  • 'Basics and Outlooks of Deep Learning (ディープラーニングの基礎と展望),' in ALAGIN Young Researchers' Workshop (Tokyo University), Tokyo, Japan, Dec. 2012.
  • 'Recent Developments in Deep Neural Networks for Automatic Speech Recognition: Methods and Applications,' in Theme Workshop of FIRST Aihara Project (Tokyo University), Tokyo, Japan, Mar. 2013. (Host: Dr. Takaki Makino)
  • 'Recent developments in speech recognition technologies (音声認識技術の現在と最先端),' in Nara Institute of Science and Technology (NAIST), Nara, Japan, May. 2013. (in Japanese; Invited Lecture; Host: Dr. Graham Neubig)
  • 'Practical kernel methods for automatic speech recognition,' in Mitsubishi Electric Research Laboratories, MA, USA, May. 2013. (Host: Dr. Shinji Watanabe)
  • 'WFST-based structured classification for meeting recognition,' in SLS Seminar at Massachusetts Institute of Technology, MA, USA, May. 2013. (Host: Prof. James Glass)
  • 'Integration of structured classification and deep neural networks for automatic speech recognition,' in Midwest Speech and Language Days (Toyota Technological Institute Chicago), IL, USA, May. 2013. (Invited Talk; Host: Prof. Sadaoki Furui)
  • 'Deep Learning and its application to Automatic Speech Recognition (Deep Learningとその音声認識への応用)' in Waseda University, Tokyo, Japan, Jun. 2013. (in Japanese; Host: Prof. Tetsunori Kobayashi)
  • 'Basics of Deep Learning and Speech Recognition (深層学習と音声認識の基本)' in ALAGIN Speech Processing Seminar, Tokyo, Japan, Oct. 2013. (in Japanese)
  • 'Recent studies on Deep Learning for Speech Recognition (音声認識分野における深層学習技術の研究動向)' The 16th Information-Based Induction Sciences Workshop, Tokyo, Japan, Nov. 2013. (in Japanese; Invited Talk)
  • 'Deep Learning (ディープラーニング技術)' in NHK Science & Technology Research Laboratories, Tokyo, Japan, Nov. 2013. (in Japanese; Host: Dr. Shoei Sato)
  • 'Deep Learning and Its Application to Pattern Recognition Problems (深層学習とそのパターン認識への応用)' in CS Colloquium at Tsukuba University, Ibaraki, Japan, Dec. 2013. (in Japanese; Host: Dr. Hideitsu Hino)
  • 'Applications of Deep Learning in Speech Recognition (音声認識における深層学習の活用とその進展),' invited talk in Ongaku-Symposium 2014
  • 'Advances in Speech Recognition for Digital Assistants,' invited talk in Industry Forum in IEEE ICNC-2020.

Degrees

  • Doctor of Engineering from Waseda University, Tokyo, Japan (2010)
  • Master of Informatics and Computer Science from Waseda University, Tokyo, Japan (2008)
  • Bachelor of Informatics and Computer Science from Waseda University, Tokyo, Japan (2007)

Awards

  • IEICE ISS Young Researcher's Award in Speech Field, 2013.
  • The Itakura Award from the Acoustical Society of Japan (ASJ), 2013.
  • IEEE SPS Japan Chapter Student Paper Award, 2011.
  • The Yamashita SIG Research Award from the Information Processing Society of Japan (IPSJ), 2011.
  • The Awaya Award from the Acoustical Society of Japan (ASJ), 2010.
Yota For Mac Os

yotta is a tool that we’re building at mbed to helpourselves and others build better software for C-family languages by making iteasier to share and re-use software modules. It’s a command line tool yotta,but also a culture of building software components that do one thing well,declare a clear interface, and can be re-used.

Get started by installing yotta, and following thetutorial. yotta is still early in development, soif you have questions/feedback or issues, please report them on our githubissue tracker.


yotta is written in python,and distributed usingpip, the python packagemanager. You will need a working installation of both python and pip to installyotta, as well as a working development environment for compiling software, including:

  • CMake, the build system that yotta uses.
  • a compiler, to actually compile the code into working programs.

yotta supports compiling with different compilers by specifying differenttargets for the compilation, and a compilationtarget may have its own specific requirements.

To install yotta, please follow the detailed installation instructions for youroperating system below:

If you have a complex python setup on your system, you may want to considerinstalling yotta in a virtualenv, in order to separateits dependencies from other python programs on your system.

To upgrade an existing installation to a new version, seeupgrading (the same for all systems).

If you have a restrictive firewall setup, then for yotta to be fullyfunctional you may also need to ensure that it can accessthe required domains.


# Installing On OS X

# Manual Installation

First install homebrew, a package manager for OS X that we’ll use toinstall all of yotta’s dependencies.

Next, tap the ARMmbed brewformulae, which lets brewinstall packages from the mbed team:

Now we can install everything that yotta needs:

And install yotta itself:

# Cross-compiling from OS X

To cross-compile, you need the arm-none-eabi-gcc cross-compiler. You caninstall this using homebrew, after tapping the ARMmbed homebrew packagerepository.

To use this compiler, you’ll need to select a supported cross-compilationtarget, such asfrdm-k64f-gcc, by runningyotta target frdm-k64f-gcc before building.

# Using Xcode’s compiler to build natively for OS X

To compile things natively you need to have the Xcode command line toolsinstalled. Install Xcode fromthe Mac app store, then run:

To use this compiler to build a module, you should run yotta targetx86-osx-native System font size for mac. before building. This selects the yotta target description forthe native compiler.

# Solving Common OS X installation problems

On OS X, if you get an unknown argument error when running pip install yotta, it means some of yotta’s dependencies have not yet been updated to support Xcode 5.1.To fix this, install yotta by running:


# Installing On Linux

# Installing Dependencies

First install yotta’s dependencies using your system’s package manager. Usewhatever 2.7.* python version is provided by your distribution (python 3support is currently experimental).

on Debian and Ubuntu:

and on Fedora Linux (tested on FC21):

or under cygwin (on windows), which presents a linux-like environment to yotta:

  • install the windows dependencies
  • install the libffi-developer and openssl-developer cygwin modules as bothbinary and source
  • install python and pip in cygwin

# Install yotta Itself

After installing the dependencies, install yotta itself using pip (you may needto use sudo for this, depending on your configuration):

If you experience problems with a systemwide yotta installation on linux, youcan try installing yotta in a virtualenv byfollowing these instructions.

You can use the following commands to allow the current user to override moduledependencies using yotta link withoutsudo:

# Cross-compiling from Linux

To cross-compile yotta modules for embedded targets, you first need install thearm-none-eabi-gcc compiler.

On most Linux distributions (although not Ubuntu), this can be done by running:

On Ubuntu it’s necessary to use the ARM-maintained gcc-arm-embeddedpackage, instead:

To use this compiler, you’ll need to select a supported cross-compilationtarget, such asfrdm-k64f-gcc, by runningyotta target frdm-k64f-gcc before building.

# Using clang to build natively for Linux

Install a native compiler, such as clang:

To use this compiler to build a module, you should run yotta targetx86-linux-native before building. This selects the yotta target description forthe native compiler.

# Solving Common Linux installation problems

If you are having trouble with pip not installing yotta, try running sudo pip install -U pip to update your pip installation. Check that your pip installation is up to date by running pip -V, you should get a response of 7.1.2 or greater.

On Ubuntu the default pip installation python-pip is out of date (1.5.2) and cannot upgrade itself via sudo pip install -U pip. To solve this you will need to install pip from easy_install by running easy_install pip. You should then be able to install yotta by running pip2 install yotta.

If you encounter problems with the cryptography library, e.g. 'module' object has no attribute 'X509_up_ref', manually upgrading the cryptography library may fix your issue:

You can also try installing pip from the Pypy registry if everything else fails.


# Installing on Windows

# Manual Installation

  1. Install python. You must install python 2.7.9 or later for yotta to work on windows. Select either the x86-64 installer if you use 64-bit windows, or the x86 installer if you use 32-bit windows.

    During installation, be sure to select the “add to path” option. This will let you run python easily from a command prompt.

  2. Install CMake. yotta uses CMake togenerate makefiles that control the build. Select the latest availableversion, currently 3.2.1 The 32-bitversionwill work on all versions of windows. Be sure to check the “add cmake tothe path for current user” option during installation.

  3. Install Ninja, the small and extremely fast build system that yottauses. Download the release archive from the releasespage,and extract it to a directory (for example C:ninja).

  4. Add the directory you installed Ninja in to your path.

  5. Install the arm-none-eabi-gcc cross-compiler inorder to build software to run on embedded devices.

  6. Finally, open cmd.exe and run pip install -U yotta to install yottaitself.

# Cross-compiling from Windows

To use yotta to cross-compile binaries to run on embedded hardware, you need tofirst install the arm-none-eabi-gcccompiler. At the time of writing this,the latest version used for cross-compiling with yotta is gcc4.9.Download and install it, then add the bin/ subdirectory of the installationdirectory to your path. After you do that, you should be able to open cmd.exeand run arm-none-eabi-gcc from the command prompt. If that doesn’t work, makesure that your path is properly set.

To use this compiler, you’ll need to select a supported cross-compilationtarget, such asfrdm-k64f-gcc, by runningyotta target frdm-k64f-gcc before building.

# Building programs natively to run on windows

yotta does not yet allow compiling programs to run on windows. If you areadventurous and get it working, submit a pullrequest to update these docs.

Yota For Mac Os 10.13

# Solving Common Windows Installation Problems

error: command ['ninja'] failed

If you get an error when running yotta build which looks something like this:

This is caused by re-trying a yotta build after fixing a missingcross-compiler installation. After completing theinstallation of the compiler, you’ll need to run yotta cleanbefore running build again.

# Adding things to your PATH in windows

Your PATH environment variable holds the location of programs that can beeasily executed by other programs. If yotta fails to find one of itsdependencies (such as cmake.exe) the first thing to check is that you haveadded the directory that contains the dependencies executable to the PATH. Toadd things to your path:

  1. Right click on Computer, select Properties
  2. Select Advanced System Settings
  3. Select the Advanced tab
  4. Click the Environment Variables button

  5. Find the Path variable, edit it, and append the path you want to add,preceded by a semicolon, for example: ;C:Pathtowherever

    NOTE: be careful not to add any spaces before or after the semicolon,this can cause commands to fail later.

  6. finally, close then re-open any open cmd.exe windows

Yota Mac Os


# Installing in a Virtualenv

Virtualenv is a way of separatingdifferent python programs installed on the same system from each other. If youhave a complex python environment on your system it’s recommended that youinstall yotta inside a virtualenv. To do this, first install the non-pythondependencies following the normal instructions for your platform, then:

  1. Ensure you have virtualenv itself installed. It can be installed with:

    Check that your installation is succesful by running virtualenv --version.

  2. Create a directory to use for the yotta virtualenv:

  3. activate the new virtualenv:

  4. install yotta in the virtualenv:

  5. (optional) add the binary directory of your virtualenv to your PATH: (ifyou omit this step, you will need to run yotta as./path/to/yotta-venv/bin/yotta instead of simply yotta.

Now yotta should work as normal. You will need to activate the virtualenvany time you want to run yotta commands (you can deactivate it afterwards, bysimply running deactivate).

Yota Modem Mac Os


# Upgrading yotta (all platforms)

To update yotta itself, run:

This will update yotta to the latest available version, and then install anymissing dependencies required by the new version.

You can also run:

This will also attempt to update all of yotta’s dependencies to their latestversions.

Sierra

On Linux and OS X you may have to run these commands as sudo pip .., ifpermission is denied.


# Setting up Tab Completion

yotta uses argcomplete to provide tabcompletion, so you can set up completion on Linux and OS X by adding thefollowing to your .bashrc or .bash_profile file:

For more detailed instructions, see the argcompletedocumentation.