text:
No easy way to check whether JuliusLib is installed

JuliusLib, the static library distributed by Julius, doesn't provide pkg-config files so that build projects can transparently check whether the target system has Julius installed or not.

The Pull Request above adapts JuliusLib to generate a distro-agnostic, versioned .pc file, since Julius has major version bumps. Please, let me know if there's anything else I can do here :)

Thanks! I've incorporated it into 4.4.

The cflags and libs for libsent changes at compilation time, so the .pc file should contain flags determined at configuration process by libsent/configure.  I guess libsent should also have .pc file like libjulius, and determine dependency from libjulius to libsent.

Thanks for the comments. I was avoiding adding another .pc file, because I mistakenly thought it'd be an overkill. I'm actually happy to learn you want a .pc file for libsent as well, because that's the correct way to solve this problem.
Just updated the Pull Request with your comments.

Merged.  Thanks for your contribution!

authors: GeorgesStavracas nitslp-ri

repo: julius-speech/julius


text:
Setup spreadsheet/CSV to locale JSON pipeline

Write a script to generate JSON from CSV

Given a spreadsheet (an online Google Spreadsheet, or a .csv file) with a few columns:

Key to an element in the code (text, button, popup, error message, etc.) that needs translation
English text to be displayed
Japanese text to be displayed
(Eventually in the future support for other columns for other languages)

that will be handled by translators so that they don't have to modify the code directly.
This can then be exported as .csv and it should be possible to generate a proper .json file to be used directly by react-intl.
See en.json and ja.json translation files for reference for keys and translations.
A few other links/references:

Example for Swifty Localization (note: the keys we are using so far are formatted as element.subelement, and not element_subelement)
Github Project to fetch translation from spreadsheet and convert to react-intl compatible .json
Internationalization of React App using Google Sheet and react-i18n (we are using react-intl so not directly applicable)
Automating Internationalization with Google Spreadsheet and i18next (we are using react-intl so not directly applicable)

Future extra improvements:

Have an automated pipeline to generate and update .json file every time the spreadsheet is updated.

source: gharchive/issue

authors: atparkweb quesiu

repo: juliwithoutthee/mymizu-web

license_type: permissive

license_source: github-api


text:
if master branch passes all tests, wercker pushes to docker hub

This requires protected environment variables in wercker.

https://app.wercker.com/#build/54c82d2dcc09f996372a2cbc is clean
:ship:

source: gharchive/pull-request

repo: jumanjihouse/hooktftp-runtime

license: mit

license_source: bigquery


text:
Why the rate of A-to-G deteced by SPRINT is very low ?

Hello,
I downloaded mouse RNA-Seq data from NCBI as test data to run SPRINT. The accession code of my downloaded data is SRR1920270.
Firstly, I run SPRINT without adding repeats bed file after trim the downloaded data using Trimmomatic-0.32. There is my command below:
~/bin/SPRINT-0.1.8/bin/sprint prepare
-t  ~/test/genome_file/Mus_musculus.GRCm38.96.gtf
~/test/genome_file/Mus_musculus.GRCm38.96.genome.fa
~/bin/SPRINT-0.1.8/samtools_and_bwa/bwa
~/bin/SPRINT-0.1.8/bin/sprint main -c 6 -p 10
-1 ~/test/SRR1920270-1/SRR1920270_R1.clean.fastq
-2 ~/test/SRR1920270-1/SRR1920270_R2.clean.fastq
~/test/genome_file/Mus_musculus.GRCm38.96.genome.fa
~/test/SRR1920270
~/bin/SPRINT-0.1.8/samtools_and_bwa/bwa
~/bin/SPRINT-0.1.8/samtools_and_bwa/samtools

cut -f 4 SPRINT_identified_regular.res | sort | uniq -c
53   AC
1478 AG
20   AT
33   CA
41   CG
1354  CT
1206 GA
36   GC
31   GT
21   TA
1471 TC
41   TG
1 Type

The rate of  A-to-G is much less than 90% which is confict with the common conclusion in mouse. Additonally, the rates of C-to-T and G-to-A were comparable to the rate of A-to-G. I guess that perhaps SNVs were not removed in low complexity regions and simple repeats without providing repeat annotation file.  And then I ran SPRINT again with repeat annotation file downloaded from UCSC. Here is my second command:
~/bin/SPRINT-0.1.8/bin/sprint main -c 6 -p 10
-rp ~/test/genome_file/mm10.repeats.bed
-1 ~/test/SRR1920270-1/SRR1920270_R1.clean.fastq
-2 ~/test/SRR1920270-1/SRR1920270_R2.clean.fastq
~/test/genome_file/Mus_musculus.GRCm38.96.genome.fa
~/test/SRR1920270
~/bin/SPRINT-0.1.8/samtools_and_bwa/bwa
~/bin/SPRINT-0.1.8/samtools_and_bwa/samtools

cut -f 4 SPRINT_identified_regular.res | sort | uniq -c
15 AC
1117 AG
25 AT
28 CA
20 CG
597 CT
546 GA
21 GC
30 GT
21 TA
1039 TC
30 TG
1 Type

The rate of  A-to-G was increased but still much less than 90%. And the rates of C-to-T and G-to-A were also very high. I don't know where is the problem. Could you give me some help? Thanks!
Best regard,

Please use repeat annotation file (you can use mm10 as reference).

C-to-U rate can be high in some mouse tissues (please check our manuscript).

Hello,
Thanks for the reply.
I used repeat annotation file to run  SPRINT in my second tries (see obove).  The repeat annotation file downloaded from UCSC was processed using rp2bed.py firstly and then the generated bed file (mm10.repeats.bed) was used as input for SPRINT.
In my downloaded mouse reference genome file, there are no "chr" ahead the name of each chromosome. So I also removed "chr" in mm10.repeats.bed file before running SPRINT. But The rate of A-to-G was increased but still much less than 90%.  BTW,  SRR1920270 is RNA-Seq data of mouse ( CAST-EIJ strain) brain tissue.

Unlike primate, mouse can have high C-to-U rate.
However, it may caused by the condition or sequencing protocol of this sample.
Maybe you can try another dataset.

authors: jumphone qiuyixmm

repo: jumphone/SPRINT

license: MIT


text:
Refactoring: Move all Fedora specific tests into test_install_fedora.py

Adding helpers and fixtures to detect Fedora based
This is to cherry-pick a commit from https://github.com/junaruga/rpm-py-installer/pull/207 .
Thanks @dcermak
[a] Current master branch: https://travis-ci.org/junaruga/rpm-py-installer/builds/579532829
[b] This PR's travis: https://travis-ci.org/junaruga/rpm-py-installer/builds/579539128

fedora30

[a]

py37-cov: 127 passed, 2 deselected in 137.35 seconds
py27-cov: 127 passed, 2 deselected, 1 warnings in 96.03 seconds

[b]

py37-cov: 127 passed, 2 deselected in 130.71 seconds
py27-cov: 127 passed, 2 deselected, 1 warnings in 92.22 seconds

fedora26

[a]

py35: 125 passed, 2 skipped, 2 deselected in 99.20 seconds
py26: 126 passed, 1 skipped, 2 deselected in 78.64 seconds

[b]

py35: 125 passed, 2 skipped, 2 deselected in 97.44 seconds
py26: 126 passed, 1 skipped, 2 deselected in 76.53 seconds

intg

[a]: 2 passed, 127 deselected in 4.04 seconds
[b]: 2 passed, 127 deselected in 4.26 seconds

centos7

[a]

py36-cov: 127 passed, 2 deselected in 75.45 seconds
py34-cov: 126 passed, 1 skipped, 2 deselected in 58.48 seconds
py27-cov: 127 passed, 2 deselected, 1 warnings in 60.51 seconds

[b]

py36-cov: 127 passed, 2 deselected in 79.27 seconds
py34-cov: 126 passed, 1 skipped, 2 deselected in 62.22 seconds
py27-cov: 127 passed, 2 deselected, 1 warnings in 66.18 seconds

centos6

[a]

py27: 126 passed, 1 skipped, 2 deselected, 1 warnings in 47.74 seconds
py26: 126 passed, 1 skipped, 2 deselected in 43.64 seconds

[b]

py27: 93 passed, 34 skipped, 2 deselected, 1 warnings in 38.29 seconds
py26: 93 passed, 34 skipped, 2 deselected in 35.18 seconds

ubuntu_bionic

[a]

py36-cov: 90 passed, 37 skipped, 2 deselected in 28.25 seconds
py27-cov: 90 passed, 37 skipped, 2 deselected, 1 warnings in 25.49 seconds

[b]

py36-cov: 90 passed, 37 skipped, 2 deselected in 29.56 seconds
py27-cov: 90 passed, 37 skipped, 2 deselected, 1 warnings in 27.15 seconds

ubuntu_trusty

[a]

py34: 89 passed, 38 skipped, 2 deselected in 15.82 seconds
py27: 90 passed, 37 skipped, 2 deselected, 1 warnings in 24.02 seconds

[b]

py34: 89 passed, 38 skipped, 2 deselected in 15.35 seconds
py27: 90 passed, 37 skipped, 2 deselected, 1 warnings in 23.07 seconds

centos6: number of passed tests is changed from 126 to 93, why?

https://travis-ci.org/junaruga/rpm-py-installer/jobs/579539136
centos6 case, below tests/test_install_fedora.py tests are skipped. Why?
tests/test_install_fedora.py::test_rpm_init_is_ok[True] SKIPPED
tests/test_install_fedora.py::test_rpm_init_is_ok[False] SKIPPED
...

On CentOS6, there is no /etc/os-release file. But there is /etc/redhat-release file.
$ podman run -it --rm centos:6 bash
[root@b88979e1667f /]# cat /etc/os-release
cat: /etc/os-release: No such file or directory

[root@b88979e1667f /]# cat /etc/redhat-release
CentOS release 6.10 (Final)

centos6: number of passed tests is changed from 126 to 93, why?

https://travis-ci.org/junaruga/rpm-py-installer/jobs/579554891
Now after 2nd commit in the PR, the number in centos6 case is 126 like this. It's okay.

centos6

[b]

py27: 126 passed, 1 skipped, 2 deselected, 1 warnings in 44.65 seconds
py26: 126 passed, 1 skipped, 2 deselected in 42.20 seconds

repo: junaruga/rpm-py-installer


text:
Fedora package documentation

The installation section says:

See Fedora's package documentation for more information.

I would like to set up my keybindings properly after having installed the fzf dnf package. But I cannot find the package documentation. Can someone help me please?

The docs effectively just say to add this to your .bashrc:
source /usr/share/fzf/shell/key-bindings.bash

authors: ianchanning orschiro

repo: junegunn/fzf


text:
:FZF not working with a path passed

In vim :FZF and :FZF ~ will all work fine but :FZF app/models shows nothing
Some other odd notes. If I try :FZF! app/models that works, but only once, any subsequent attempts will also show nothing

Ouch! Thanks for the report. I realized that it didn't work with relative paths. Fixed it. Let me know if you still see any issues.

It works on first go now :+1: But after I use it once, it changes the directory to that relative path and any subsequent runs will use that. This wasn't the behavior before as far as I remember. Is there a way to get it to not change?

It is not supposed to work that way. So you mean :pwd is changed after :FZF RELPATH?

Yes

Strange, it's handled in the code and I can't seem to reproduce the case. I just pushed another commit that may or may not fix your problem. Please update and let me know of the exact scenario that I can follow to reproduce the problem.

That worked! Thanks so much!

Great. Thanks!
Now I see that the previous code had a bug. It didn't restore the current path when FZF is started w/o tmux and user interrupted the process (ESC or CTRL-C). I should really be adding proper test cases for vim plugin.

authors: Randuin junegunn


text:
PlugInstall prompts PlugClean required

PlugInstall prompts incorrect PlugClean required messages.

wonder if it's the same problem like https://github.com/junegunn/vim-plug/issues/537

VIM - Vi IMproved 8.0 (2016 Sep 12, compiled Dec  9 2016 08:03:38)
MacOS X (unix) version
Included patches: 1-124
Compiled by root@Traviss-Mac-592.local
Huge version with MacVim GUI.  Features included (+) or not (-):
+acl             +cursorbind      +fullscreen      +modify_fname    +path_extra      +syntax          +viminfo
+arabic          +cursorshape     -gettext         +mouse           +perl/dyn        +tag_binary      +vreplace
+autocmd         +dialog_con_gui  -hangul_input    +mouseshape      +persistent_undo +tag_old_static  +wildignore
+balloon_eval    +diff            +iconv           +mouse_dec       +postscript      -tag_any_white   +wildmenu
+browse          +digraphs        +insert_expand   -mouse_gpm       +printer         -tcl             +windows
++builtin_terms  +dnd             +job             -mouse_jsbterm   +profile         +termguicolors   +writebackup
+byte_offset     -ebcdic          +jumplist        +mouse_netterm   +python/dyn      +terminfo        -X11
+channel         +emacs_tags      +keymap          +mouse_sgr       +python3/dyn     +termresponse    -xfontset
+cindent         +eval            +lambda          -mouse_sysmouse  +quickfix        +textobjects     +xim
+clientserver    +ex_extra        +langmap         +mouse_urxvt     +reltime         +timers          -xpm
+clipboard       +extra_search    +libcall         +mouse_xterm     +rightleft       +title           -xsmp
+cmdline_compl   +farsi           +linebreak       +multi_byte      +ruby/dyn        +toolbar         -xterm_clipboard
+cmdline_hist    +file_in_path    +lispindent      +multi_lang      +scrollbind      +transparency    -xterm_save
+cmdline_info    +find_in_path    +listcmds        -mzscheme        +signs           +user_commands
+comments        +float           +localmap        +netbeans_intg   +smartindent     +vertsplit
+conceal         +folding         +lua/dyn         +num64           +startuptime     +virtualedit
+cryptv          -footer          +menu            +odbeditor       +statusline      +visual
+cscope          +fork()          +mksession       +packages        -sun_workshop    +visualextra
   system vimrc file: "$VIM/vimrc"
     user vimrc file: "$HOME/.vimrc"
 2nd user vimrc file: "~/.vim/vimrc"
      user exrc file: "$HOME/.exrc"
  system gvimrc file: "$VIM/gvimrc"
    user gvimrc file: "$HOME/.gvimrc"
2nd user gvimrc file: "~/.vim/gvimrc"
       defaults file: "$VIMRUNTIME/defaults.vim"
    system menu file: "$VIMRUNTIME/menu.vim"
  fall-back for $VIM: "/Applications/MacVim.app/Contents/Resources/vim"
Compilation: clang -c -I. -Iproto -DHAVE_CONFIG_H -DFEAT_GUI_MACVIM -Wall -Wno-unknown-pragmas -pipe  -DMACOS_X_UNIX  -g -O2 -
U_FORTIFY_SOURCE -D_FORTIFY_SOURCE=1
Linking: clang   -L. -fstack-protector -L/usr/local/opt/libyaml/lib -L/usr/local/opt/readline/lib -L/usr/local/opt/libksba/lib
 -L/usr/local/opt/openssl/lib  -L. -fstack-protector -L/usr/local/opt/libyaml/lib -L/usr/local/opt/readline/lib -L/usr/local/o
pt/libksba/lib -L/usr/local/opt/openssl/lib   -L/usr/local/lib -o Vim -framework Cocoa -framework Carbon       -lm  -lncurses
-liconv -framework Cocoa   -fstack-protector  -L/System/Library/Perl/5.16/darwin-thread-multi-2level/CORE

Type:

[x] Bug
[ ] Enhancement
[ ] Feature Request
[ ] Question

OS:

[ ] All/Other
[ ] Linux
[x] OS X
[ ] Windows

Vim:

[x] Terminal Vim
[x] GVim
[ ] Neovim

Looks like you have some git configuration that affects the exit code of git command. cd into one of the plugin's directory and check the output of the following commands
git rev-parse --abbrev-ref HEAD 2>&1 && git config -f .git/config remote.origin.url
echo $?

The result is 0.

Hmm, ok. Please check if calling it from Vim gives a different result.
:echo system('git rev-parse --abbrev-ref HEAD 2>&1 && git config -f .git/config remote.origin.url')
:echo v:shell_error

Result is still 0.

That's strange.
:echo g:plugs['vim-snippets'].dir
:cd
:echo system(printf('cd %s && git rev-parse --abbrev-ref HEAD 2>&1 && git config -f .git/config remote.origin.url', g:plugs['vim-snippets'].dir))
:echo v:shell_error

These viml commands also return 0.
I cded into one of the problematic folder and use tig to checkout its commit history which show only one commit log.
I'm now removing all these problematic repos and rerun PlugInstall to check if this issue can be reproduced. Till now it seems something with the process of cloning from github.
I'll update more info when I find new infos.

The message is only printed when git command returns a non-zero status.
https://github.com/junegunn/vim-plug/blob/b50f9bb16b9832235beb60ed290393a5d03a4bf5/plug.vim#L1986
And I remember hearing from a user whose custom git configuration caused the issue.

I'll keep an eye on this problem, doubting it may be related to network conditions.
After reinstall the broken repos, everything is fine now except PlugClean command is a little slow, I'll  dive into this problem when I get extra time, thanks for your responsive response, you did a great job on this simplistic plugin manager, really appreciated your effort.

Why not just run :PlugClean? That works for me.

authors: junegunn noscripter rivalak

repo: junegunn/vim-plug


text:
How to manual load the plug one by one manually

Explain the problem here ...

Type:

[ ] Bug
[ ] Enhancement
[ ] Feature Request
[yes ] Question

OS:

[yes ] All/Other
[ ] Linux
[ ] OS X
[ ] Windows

Vim:

[ yes] Terminal Vim
[ yes] GVim
[ ] Neovim

Hi,
Thansk for your great plugin , it is very good software.
There is a questions about manual load.
If there are file vim plugin , A,B,C,D,E ,
At the begining , I only want to load  A and B ,so I can do the following
silent! call plug#begin(a:path)
    Plug 'A'
    Plug 'B'
silent! call plug#end()

then I want to add plugin C , I can do the following
silent! call plug#begin(a:path)
    Plug 'C'
silent! call plug#end()

But it is ugly , is there beautiful way to do it ?
Is there other way to remove B and A , and log D and E ?

Write Plug 'C', { 'on': [] } ("on nothing") and vim-plug won't load it until you call plug#load('C').

authors: irreallich junegunn


text:
🛑 kawaki is down

In 69250b0, kawaki (https://www.pegasusknight.com) was down:

HTTP code: 0
Response time: 0 ms

Resolved: kawaki is back up in 8886d81.

repo: junjanjon/WatchSomeSites


text:
Thank You, Quick Questions

Hi, love your work.
Notes Related to GMX Support:

Will there be support for Market Orders? When filling in different prices for opening and closing a position, it behaves similar to Limit Orders.

Opens and closes positions fine when the input price is close to the mark price.
For example, if the input price deviates too much from the mark price, it won't open / close the position.
Used Insomnia, great tool for checking webhooks.

Will there be support for different collaterals besides USDC?

Thank you very much for your work.

Hi, which exchange are you referring to? (dydx or Perpetual Protocol or GMX)
maybe we can talk in DM
https://twitter.com/CryptoIbuki

hey!
I'm considering supporting them, especially the second one(support collaterals besides USDC)
if users have needs
maybe we can talk in DM
https://twitter.com/CryptoIbuki

authors: jonathanhyunsung junta

repo: junta/tradingview-alert-connector


text:
Creating interactive cells...

Hi!
I am very new to this so please forgive me if this is trivial!
I want to make code cells in my Jupyter Book interactive, but when I try to do this the code chunk says 'connecting to kernel..'
I figure I am missing something obvious - do I need to provide my own link for Binder in the config document or by putting 'use_thebelab_button: true' should this be done automatically?
Here is the link to the book (with many other issues of my own for now!)
https://eturvey.github.io/practicebook/index.html
Thank you!
Emily

Thanks for opening an issue - the information for how to do this should be here:
https://jupyterbook.org/features/interactive_cells.html
but your issue made me discover that we're not documenting this feature properly :-)  sorry about that! but thanks for opening an issue
You're right that there are some other pieces of information to fill in, check out these lines for org/repo:
https://github.com/jupyter/jupyter-book/blob/master/jupyter_book/book_template/_config.yml#L65

Thank you so much - working now!
One more question: I would like to load the interactive workspace with some additional packages installed. From what I understood, this should mean I just need to include those package names in the requirements file, but this doesn't seem to be working. Is there anything else I need to change? Thank you!!!

Hi @eturvey !
It looks like your site configuration is causing the trouble -- trying to launch binder returns a 404. I think the error is arising here:
https://github.com/eturvey/practicebook/blob/7cce9e3044c51aa221f9d7ea76793751d7174974/_config.yml#L25-L26
If you update to be your site information, it should work ! So:
baseurl: /practicebook  # the subpath of your site, e.g. /blog. If there is no subpath for your site, use an empty string ""
url: eturvey.github.io                  # the base hostname & protocol for your site, e.g. http://example.com

Try that and let us know how it goes ! :star2:

Thank you for being so patient and helpful! I am not sure what level of knowledge you expect for people using Jupyter Book to start with, so I really appreciate the help! As someone who is coming in with clearly very little knowledge of what is going on I really like the step by step's with commands you offer in the Jupyter Book documentation. If you are aiming towards an audience with a less comprehensive understanding of github/jekyll I would maybe suggest including a little more information on the configuration page so people know which lines have to be messed with and which ones are ok to leave alone - just a thought! :)
I added your suggestion as well as the following lines to get it working:
binderhub_url                    : "https://mybinder.org"                              # The URL for your BinderHub. If no URL, use ""
binder_repo_base                 : "github.com"  # The site on which the textbook repository is hosted
binder_repo_org                  : "eturvey"  # The username or organization that owns this repository
binder_repo_name                 : "practicebook"  # The name of the repository on the web
binder_repo_branch               : "master"  # The branch on which your textbook is hosted.
Thank you again!

Hooray ! I'm so happy that worked for you :sparkles:

If you are aiming towards an audience with a less comprehensive understanding of github/jekyll I would maybe suggest including a little more information on the configuration page so people know which lines have to be messed with and which ones are ok to leave alone - just a thought! :)

It would be great to have more information there ! If you have any suggestion of what to add, a pull request with revised text would be greatly appreciated 😸
And if you need guidance through that process, happy to help !

authors: choldgraf emdupre eturvey

repo: jupyter/jupyter-book


text:
Revisit extension loading mechanism

Background
I've been doing a deep dive into the extension loading design and finding it lacks convention.
The most documented way to create an extension (and thus, probably the most used) is to write a load_jupyter_server_extension function.
Right now, the main ServerApp loads any modules listed in it's jpserver_extensions trait and looks for a load_jupyter_server_function. If it's not there, it skips the extension and throws a warning.
But there is also a toggling mechanism for enabling/disabling extensions. This requires an extra method to be defined in your extensions that makes them discoverable, namely _jupyter_server_extension_paths. This function returns a list of dictionaries which represents metadata for all extensions in an extension module (yes, you can define multiple extensions in a single package). Typically, these dictionaries has a module key:value pair that describes where the load_jupyter_server_function is defined for that extension. i.e.
def _jupyter_server_extension_paths():
     return [{
         'module': my_extension
     }]

There are various issues that arise here. Right now, extension loading only uses the load_jupyter_server_function and assumes its at the root of an extension module. On the other hand, the extension toggler only uses the _jupyter_server_extension_paths function to verify the existence of an extension (or multiple extensions). Having different, inconsistent entrypoints to extensions is extremely confusing.
Further, if _jupyter_server_extension_paths defines a different path to the load_jupyter_server_extension function, it cannot be loaded by the server. What's the purpose of the first function then?
I propose that we merge these two approaches and heavily document it.
Proposed change
Make ServerApp and the extension toggler require/use the _jupyter_server_extension_paths function to discover server extensions. If this function is not defined, the extension cannot be loaded (and warnings/errors will be raised).
Right now, if an extension author does not define a _jupyter_server_extension_paths, the extension can still discovered by the server even though the toggle mechanism throws "extension not found" error. I want to explicitly require extensions to define this function.

@Zsailer - thank you for spending the time and effort on this!  I think it's important that this be well-defined and I agree that removing ambiguity is the right approach.
Do you know why _jupyter_server_extension_paths() is underscore-prefixed?  My python naivete may be shining bright, but I thought that convention was reserved for private methods and this particular method is about as public as they come.  I'm hoping we could perhaps deprecate that method name and document the use of jupyter_server_extension_paths().  If the provided module doesn't have the new method, then we look for the original (perhaps logging a deprecation warning at that time).

Do you know why _jupyter_server_extension_paths() is underscore-prefixed?

Great question. I think this function is private because author of an extension would likely want this function invisible to their users. Yes, developers have to define this function when developing their extension, but their users should never call this function.
Put another way, this function is not a public API we (Jupyter server) provide; rather, it's a "protocol" we ask extension developers to follow to link into Jupyter Server. Users never see this machinery as a public API.
Does that seem reasonable?

Yes, that makes sense.  Then, by extension, shouldn't load_jupyter_server_extension be prefixed?  Still not clear on why the two would be handled differently in this aspect.

Yes. I think they should both be prefixed with _. 😎 This is another inconsistency of this mechanism. We may have to support the non-prefixed function name for a bit to maintain some backwards compatibility unfortunately.

Solved in #180!

authors: Zsailer kevin-bates


text:
File name not updated upon change in the side panel

When renaming a file, the side panel does not update.

How about we don't put the filename here?
It's taking up space, not giving any more information that we already have.

Right. Though we don't do this for notebooks with the debugger nor the cell metadata editor.

Though we don't do this for notebooks with the debugger

we do

Ah right! It needs to be started before we show the Notebook name.

authors: SylvainCorlay martinRenou trungleduc

repo: jupytercad/JupyterCAD


text:
Not working on Cloudflare?

Hi,
I'm playing with face api. My script works on local but not on prod. The only difference with my prod it's that my prod uses Cloudflare.
Cloudflare : https://hotgeart.com/face-api/ 👎
Random hosting : http://hotgeart.000webhostapp.com/ 👍
The code is really simple:

Upload

        <input
            id="queryImgUploadInput"
            type="file"
            class="waves-effect btn bold"
            onchange="loadImageFromUpload()"
            accept=".jpg, .jpeg, .png"
            />

The files are not corrupted, I have uploaded them several times via FTP.
Does anyone know what's going on?
Thanks

not corrupted, but served in a wrong way by cloudflare
just download them manually - they do not match (and this is nothing to do with face-api specifically):
# wget https://hotgeart.com/fhttps://hotgeart.com/face-api/models/ssd_mobilenetv1_model-shard1
Saving to: ‘ssd_mobilenetv1_model-shard1’

# wget http://hotgeart.000webhostapp.com/models/ssd_mobilenetv1_model-shard1
Saving to: ‘ssd_mobilenetv1_model-shard1.1’

ssd_mobilenetv1_model-shard1 size: 4194297
ssd_mobilenetv1_model-shard1.1 size: 4194304
option#1: dont host model weights on cloudflare, but download from a cdn
for example https://cdn.jsdelivr.net/npm/@vladmandic/face-api/models
option#2: configure cloudflare to serve specific records without additional processing
i don't use it, but it could be related to something like http request header encodeBody: "manual"

I confirm the option#1 is working. I'll put the code below for others users from Google since I'll probably delete my test page.
Here's the working code:
<!DOCTYPE html>

    <meta charset="UTF-8" />
    <meta http-equiv="X-UA-Compatible" content="IE=edge" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>Test Upload</title>

Upload

      <input
        id="queryImgUploadInput"
        type="file"
        class="waves-effect btn bold"
        onchange="loadImageFromUpload()"
        accept=".jpg, .jpeg, .png"
      />

authors: hotgeart vladmandic


text:
Try new reference assemblies package

As discussed previously, this is a nice cross-plat way to acquire reference assemblies.

I'm just checking if we should bother with the condition, doesn't matter afaict, but I want to do the simplest thing.
https://github.com/dotnet/designs/pull/33#issuecomment-489307873

1.0.0-preview.1
When does it come out of preview? Assuming that if it's around the .NET Core 3.0 time frame, that means 2019 Q3

That is my understanding, there are some issues people are seeing, so they'll be still fixing bugs until then.

repo: justeat/JustSaying


text:
🛑 Bitwarden is down

In 6efd5c9, Bitwarden (https://bitwarden.mkacg.com) was down:

HTTP code: 530
Response time: 59 ms

Resolved: Bitwarden is back up in 3e6da88 after 10 minutes.


text:
Mock all components

This adds all the routes to mock the services for all components in Storybook.
Links
#437
Developer considerations

On any task

[ ] Previous unit and e2e tests are passing

Developer QA steps
[ ] - All components should be returning mock data

I approved this, but do want to make a note about Gross Payment Chart's mock data.
We may need to add an optional prop to this component to allow us to provide a start/end date for the data.
The way this component works is to look at Today in date/time and grab the last 30 days of payment gross data to fill out the chart. So its always looking at TODAY, and the columns on the chart should represent the 30 days that happened before TODAY.
So, when we use mock data like this, it loads the chart just fine, but it is strange to look at the data and see that the data for the majority of the columns is several weeks or months behind the last column TODAY.
The reason I'd suggest us to add an optional prop is so we can have the chart looking nice and consistent, and still use the mockdata that is here without havving to update it everytime. IDK - maybe there is a better or easier solution to just update the mock data dates every time the data is loaded? 🤔

I approved this, but do want to make a note about Gross Payment Chart's mock data.
We may need to add an optional prop to this component to allow us to provide a start/end date for the data.
The way this component works is to look at Today in date/time and grab the last 30 days of payment gross data to fill out the chart. So its always looking at TODAY, and the columns on the chart should represent the 30 days that happened before TODAY.
So, when we use mock data like this, it loads the chart just fine, but it is strange to look at the data and see that the data for the majority of the columns is several weeks or months behind the last column TODAY.
The reason I'd suggest us to add an optional prop is so we can have the chart looking nice and consistent, and still use the mockdata that is here without havving to update it everytime. IDK - maybe there is a better or easier solution to just update the mock data dates every time the data is loaded? 🤔

Good observation!
Another option would be to use the proxyApi in Storybook, and if possible, only use mock data for this component for the Chromatic build, so we still would have screenshot tests.

authors: TiagoFuelber colganmeanor

repo: justifi-tech/web-component-library


text:
Add Intro to storybook

@Cherna for the React components, are you sure that the global styles aren't needed? Somehow the css variables need to be defined on :root. I am realizing that might actually be a problem, I think the React package SHOULD have some css files in it doesn't look like it does

Yeah, I understand it might not be the case right now, it was more of a "the way I think it should be".


text:
微博热搜话题有重复，多次出现混乱；知乎热搜部分标题有空格，导致超链接无效；

RT，辛苦大佬！不知是否方便抽空处理一下呢，期待能修复。

https://github.com/justjavac/zhihu-trending-top-search/commit/f2c657bfcd25cba05a2ba4d1084bde88ee79338c

微博热搜是根据 url 做的去重复，可能是因为微博的相同话题使用了不同的 url 导致的

微博热搜是根据 url 做的去重复，可能是因为微博的相同话题使用了不同的 url 导致的

看微博热搜重复的标题是相同的，是否能根据文本去重呢？这样看起来整洁方便很多。期待大佬评估一下

repo: justjavac/weibo-trending-hot-search


text:
Add support for headers and footers

This pull request adds support for the reading/writing headers and footers (headerFooter element). The HeaderFooter class exposes the raw content of the odd/even header/footer elements. All formatting codes must be handled manually.

@james-d-hasselman
LGTM
Thanks for this awesome feature.

@james-d-hasselman
Thanks for the PR.
But there seems to be an issue with Header and Footer #167 .

authors: james-d-hasselman justkawal

repo: justkawal/excel


text:
TPpDzM3NrhyqAAvBy2LAYemqqw9AFUXtfz

Before submission deletes this line:
THIS IS NOT A TOKEN LISTING REQUEST FORM. IF YOU DO NOT FOLLOW THE FORMAT OR MAKE A GENERIC TOKEN REQUEST YOUR ISSUE WILL BE DELETED WITHOUT COMMENT
YOUR JUSTLIST MUST FOLLOW THE JSON SPECIFICATION
https://github.com/justswaporg/justlists/blob/main/example.justlists.ts
Checklist

[ ] I understand that this is not the place to request a token listing.
[ ] I have tested that my JustList is compatible by pasting the URL into the add a list UI at justswap.org.
[ ] I understand that filing an issue or adding liquidity does not guarantee addition to the justlists website.

Please provide the following information for your token.
JustList URL must be HTTPS.
JustList URL:
JustList Name:
Link to the official homepage of the JustList manager:

Please submit the correct information, invalid information will cause the issue to close

repo: justswaporg/justlists


text:
Add JustLbc1qwe9fvgf2plszn7zd8w6c5gpphgxxsnqpkt6430pna6hyljf5g52qfdezuwist: {JustList name}

Before submission deletes this line:
THIS IS NOT A TOKEN LISTING REQUEST FORM. IF YOU DO NOT FOLLOW THE FORMAT OR MAKE A GENERIC TOKEN REQUEST YOUR ISSUE WILL BE DELETED WITHOUT COMMENT
YOUR JUSTLIST MUST FOLLOW THE JSON SPECIFICATION
https://github.com/justswaporg/justlists/blob/main/example.justlists.ts
Checklist

[x] I understand that this is not the place to request a token listing.
[x] I have tested that my JustList is compatible by pasting the URL into the add a list UI at justswap.org.
[x] I understand that filing an issue or adding liquidity does not guarantee addition to the justlists website.

Please provide the following information for your token.
JustList URL must be HTTPS.
JustList URL:
JustList Name:
Link to the official homepage of the JustList manager:

Please submit the correct information, invalid information will cause the issue to close


text:
Add gometalinter target to TravisCI

Hi @zwopir,
As told in #172, this add a Makefile target gometalinter, but run it only in TravisCI. This should save future related issues.
Thanks!

perfect. I'm more than fine with dropping go1.8

@zwopir It is failing because of one previous commit merged,
>> linting code
collector/nodes_response.go:319:6:warning: exported type NodeStatsFSIOStatsResponse should have comment or be unexported (golint)
collector/nodes_response.go:323:6:warning: exported type NodeStatsFSIOStatsDeviceResponse should have comment or be unexported (golint)
make: *** [gometalinter] Error 1

I will be fixing it today if not fixed yet.

just fixed it. Thanks for having a look at it

authors: mjtrangoni zwopir


text:
Add description of plugin "configuration"

Because I wasted 5mins of my life

I'm confused, how is YAML related to neon?

wrong repo lol, sorry

authors: NicolasCharpentier fprochazka

repo: juzna/intellij-neon


text:
Have metadata links ready

release dates
publisher

duplicated

duplicated

repo: jwestw/sdg-csv-data-filler


text:
repoParent empty?

Hi,
Consider this code:
import qualified Github.Repos as Github
import Data.Maybe
import Data.Functor

run = fmap (either (error.show) id)

main = do
    repos <- run $ Github.userRepos "nomeata" Github.Owner
    let forks = filter (fromMaybe False . Github.repoFork) repos
    mapM_ (print . Github.repoParent) forks

It prints all of my repos that are forks, and I would expect it to print the parent repository as well. Unfortunately, it does not (it prints a bunch of Nothings). So maybe #43 was not fixed after all?
This is using github-0.13.2.

Ok, curl -i https://api.github.com/users/nomeata/repos also shows no parent information. But curl -i https://api.github.com/repos/nomeata/hlint has. Do I have to run that through userRepo again?

Indeed, that works:
import qualified Github.Repos as Github
import Data.Maybe
import Data.Functor

run = fmap (either (error.show) id)

main = do
    repos <- run $ Github.userRepos "nomeata" Github.Owner
    let forks = filter (fromMaybe False . Github.repoFork) repos
    forks' <- mapM (run . Github.userRepo "nomeata" . Github.repoName) forks
    mapM_ (print . Github.repoParent) forks'

repo: jwiegley/github


text:
Non-Git directory manually provides commit hash

Sorry, I opened this PR directly without opening a Issue. This is my reason:

In some environments, the git clone will not be used, but by downloading the zip or tar.gz package. At this time, the failure to read the commit hash will fail to build. A concrete example is to build the Rust software via the openSUSE Build Service: https://build.opensuse.org/package/show/X11:terminals/alacritty.
On my local git version 2.21.0, the rustc_tools_util::get_commit_hash function will not return None even if it does not read the commit hash, but instead returns a Some(""), An empty string. This causes the expect to never be triggered to terminate the build.

If the rustc_tools_util::get_commit_hash function does not get a commit hash (empty string) then the environment variable is read. I think this will solve both problems at the same time.
Even if you decide not to merge this commit, I still hope to improve build.rs so that it can be built without the .git directory.
Thanks!

Opened a new PR: https://github.com/jwilm/alacritty/pull/2443

repo: jwilm/alacritty


text:
LGA and SA3 data

This data is now available.  Need to update it and automate publication process for SA4 as well

Now published


text:
Build fails since 1.8.1

The static build seems to fail following the 1.8.1 release, 1.8.0 is fine...
$ mdx-deck build deck.mdx
[mdx-deck] building
[mdx-deck] rendering static html
[err] ReferenceError: window is not defined
    at Appear.render (webpack:///./node_modules/mdx-deck/dist/Appear.js?:98:7)
    at processChild (/Users/mgibson/decks/state-battle/node_modules/react-dom/cjs/react-dom-server.node.development.js:2861:18)
    at resolve (/Users/mgibson/decks/state-battle/node_modules/react-dom/cjs/react-dom-server.node.development.js:2714:5)
    at ReactDOMServerRenderer.render (/Users/mgibson/decks/state-battle/node_modules/react-dom/cjs/react-dom-server.node.development.js:3098:22)
    at ReactDOMServerRenderer.read (/Users/mgibson/decks/state-battle/node_modules/react-dom/cjs/react-dom-server.node.development.js:3057:29)
    at renderToString (/Users/mgibson/decks/state-battle/node_modules/react-dom/cjs/react-dom-server.node.development.js:3524:27)
    at renderHTML (/Users/mgibson/decks/state-battle/node_modules/mdx-deck/lib/html.js:65:16)
    at process.internalTickCallback (internal/process/next_tick.js:77:7)
error Command failed with exit code 1.

Most probably due to the use of 'window' global here...
https://github.com/jxnblk/mdx-deck/commit/cc142d16edf42098e0dc54764c63e93e106c6232

repo: jxnblk/mdx-deck


text:
Add pcloud

Add pcloud as a cloud storage solution

pCloud – A cloud storage solution with a strong focus on security.

Added to File management

repo: jyguyomarch/awesome-productivity


text:
🛑 le Zata is down

In 02e52da, le Zata (http://zata.free.fr) was down:

HTTP code: 0
Response time: 0 ms

Resolved: le Zata is back up in 4301c08 after 44 minutes.

repo: jyhem/upptime-jyhem


text:
Doesn't work on Travis anymore

Travis currently has Java 7 as default, since the v6 release we required Java 8, since that is now the minimum supported by vnuvalidator. For Travis Java projects its possible to specify the JDK version: https://github.com/jzaefferer/grunt-html/blob/545caebe6858d8a624fd6a8320cba5424893b0db/.travis.yml#L10-L11
But this doesn't work for node (see https://github.com/jquery/jquery-ui/pull/1674 ), and I don't think its acceptable to ask everyone to change their node projects to java and specify the node dependency manually.
Would be nice if Travis would just update the default JDK to 8. Not sure what else to do. Any ideas, @XhmikosR, @jawshooah, @LaurentGoderre?

I haven't got the time to work around this myself, but it's an upstream issue nevertheless.
Having said that https://github.com/twbs/bootstrap/pull/19029
Another solution I want to try is https://github.com/travis-ci/travis-ci/issues/4090#issuecomment-184864134

Thanks @XhmikosR for the pointers!
Seems like this could work:
sudo: required
dist: trusty

That's supposed to have a JDK 1.8.0_60, so it should fix this issue. Will try that.

@jzaefferer: the Trusty image seems to work fine. I switched to that in a few of my projects.

@jzaefferer: shouldn't we close this? I mean, it's not an issue with grunt-html per se.

Example commit for a working setup: https://github.com/jquery/jquery-ui/commit/ff3769272bb5530b224297fa5d2add1865acbb7f
Let's add a note to the readme about this.

authors: XhmikosR jzaefferer

repo: jzaefferer/grunt-html


text:
Add support for BGP passwords

Hi - thanks for your work on this plugin.
Is your feature request related to a problem? Please describe.
We have many peerings with MD5 authentication configured on the peering. It would be great to be able to have this included in the session data in netbox-bgp
Describe the solution you'd like
Having an additional field for the password stored in encrypted form with the ability to decrypt would be great. But just having a Yes / No option to record the existence of a password on a peering would also be very helpful. This would help guide our config scripts if we need to try find the password we store elsewhere.

Creating a custom field wouldn't solve your need? on peer-goup or session object ?

Hi - ok I can proceed like that, thanks.


text:
merge from template-arlac77-github

package.json

chore(package): add true (publishConfig.provenance)

.github/workflows/ci.yml

chore: add coverallsapp/github-action@v2 (jobs.test-node.steps.uses)
chore: (jobs.release.permissions)
chore: add coverallsapp/github-action@v2 (jobs.finish.steps.uses)
chore: (permissions)

Pull Request Test Coverage Report for Build 7765536211
Warning: This coverage report may be inaccurate.
We've detected an issue with your CI configuration that might affect the accuracy of this pull request's coverage report.
To ensure accuracy in future PRs, please see these guidelines.
A quick fix for this PR: rebase it; your next report should be accurate.

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 86.721%

Totals

Change from base Build 7756063428:
0.0%

Covered Lines:
275

Relevant Lines:
313

💛  - Coveralls


text:
Sherpa support for Nemo ctc models via torchscript

Hi All,
Can we use Sherpa framework with Nemo ctc models via torchscript. If it is supported, could you please point me to appropriate links? If this is not supported, is it planned for future? And, if yes, any timelines for such support? @csukuangfj
Thanks
Manjunath

@uni-manjunath-ke
Do you have torchscript models available? If yes,  could you upload it to https://huggingface.co/
and we can support that.

I have ".nemo" models produced by finetuning of https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_hi_conformer_ctc_medium
How to I generate torchscript models for this. I will also have ".pt" checkpoints. Is it what is needed?
Is it really required to upload it to https://huggingface.co/ to get such support. Because, I may not be allowed to upload here.
Thanks

If you don't want to make the model public, we can tell you the changes that you need to make to support torchscript ctc models from nemo.

If you can make the model public (wherever you put, huggingface is a common place to put pre-trained models), we can make the changes and you can use it directly.

How to I generate torchscript models for this

We need a torchscript model either exported by torch.jit.trace() or torch.jit.script().
Sorry, I don't have experience about how to use nemo to obtain a torchscript model.

Sure, Thanks a lot. Will checkout and get back to you.

If you can provide a torchscript nemo ctc model, which does not need to be trained by yourself. Any publicly available torchscript nemo ctc model is fine.

Ok Thanks. I will try to get such model and share with you. Pls give me some time.

Hi @csukuangfj , I managed to convert ".nemo" to ".pt" torchscript file. However, when I tried to host it on sherpa framework, I get some errors. Could you please guide me on the changes that that I need to make to support torchscript ctc models on Sherpa.
The error stack trace is as below:
$sherpa/bin/offline_ctc_asr.py --nn-model /data/manju/tamil/exp/models/tamil_ctc_tl_dc_re_rs_tvs.pt --tokens /data/manju/tamil/exp/tokenizers/dc_re_rs_tvs_tokenizer_v2/tokenizer_spe_bpe_v512_max_6_pad/vocab.txt kkkk.wav
2023-02-22 09:18:36,955 INFO [offline_ctc_asr.py:312] {'nn_model': '/data/manju/tamil/exp/models/tamil_ctc_tl_dc_re_rs_tvs.pt', 'tokens': '/data/manju/tamil/exp/tokenizers/dc_re_rs_tvs_tokenizer_v2/tokenizer_spe_bpe_v512_max_6_pad/vocab.txt', 'HLG': None, 'lm_scale': 1.0, 'modified': True, 'search_beam': 20.0, 'output_beam': 8.0, 'min_active_states': 30, 'max_active_states': 10000, 'use_gpu': False, 'normalize_samples': True, 'sound_files': ['kkkk.wav']}
[F] /mnt/efs/manju/if/tools/sherpa/sherpa/csrc/symbol-table.cc:49:sherpa::SymbolTable::SymbolTable(const string&) 2023-02-22 09:18:37.351 Check failed: is.eof()
[ Stack-Trace: ]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/sherpa/lib/libsherpa_core.so(sherpa::GetStackTrace()+0x4b) [0x7f137ad5527b]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/sherpa/lib/libsherpa_core.so(sherpa::SymbolTable::SymbolTable(std::string const&)+0x18b0) [0x7f137ad58260]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/sherpa/lib/libsherpa_cpp_api.so(sherpa::OfflineRecognizerCtcImpl::OfflineRecognizerCtcImpl(sherpa::OfflineRecognizerConfig const&)+0x65) [0x7f137adbc325]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/sherpa/lib/libsherpa_cpp_api.so(sherpa::OfflineRecognizer::OfflineRecognizer(sherpa::OfflineRecognizerConfig const&)+0x158) [0x7f137adb5048]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/_sherpa.cpython-39-x86_64-linux-gnu.so(+0x1f166) [0x7f137adfb166]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/_sherpa.cpython-39-x86_64-linux-gnu.so(+0x45ea1) [0x7f137ae21ea1]
python3() [0x507f97]
python3(_PyObject_MakeTpCall+0x2ec) [0x4f0f1c]
python3() [0x505f43]
python3() [0x503312]
python3() [0x4f13d0]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/torchaudio/_torchaudio.so(+0x34685) [0x7f13a0a14685]
python3(_PyObject_MakeTpCall+0x2ec) [0x4f0f1c]
python3(_PyEval_EvalFrameDefault+0x5285) [0x4ed425]
python3() [0x4f8923]
python3(_PyEval_EvalFrameDefault+0x3d1) [0x4e8571]
python3() [0x4f8923]
python3(_PyEval_EvalFrameDefault+0x3d1) [0x4e8571]
python3() [0x4e729a]
python3(_PyEval_EvalCodeWithName+0x47) [0x4e6f27]
python3(PyEval_EvalCodeEx+0x39) [0x4e6ed9]
python3(PyEval_EvalCode+0x1b) [0x594f7b]
python3() [0x5c2457]
python3() [0x5be3b0]
python3() [0x456265]
python3(PyRun_SimpleFileExFlags+0x1a2) [0x5b81a2]
python3(Py_RunMain+0x37e) [0x5b570e]
python3(Py_BytesMain+0x39) [0x5890f9]
/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xf3) [0x7f1431eb6083]
python3() [0x588fae]
Traceback (most recent call last):
File "/mnt/efs/manju/if/tools/sherpa/sherpa/bin/offline_ctc_asr.py", line 359, in
main()
File "/mnt/efs/manju/if/tools/sherpa/sherpa/bin/offline_ctc_asr.py", line 315, in main
recognizer = create_recognizer(args)
File "/mnt/efs/manju/if/tools/sherpa/sherpa/bin/offline_ctc_asr.py", line 305, in create_recognizer
recognizer = sherpa.OfflineRecognizer(config)
RuntimeError:
Some bad things happened. Please read the above error messages and stack
trace. If you are using Python, the following command may be helpful:
  gdb --args python /path/to/your/code.py

(You can use `gdb` to debug the code. Please consider compiling
a debug version of sherpa.).

If you are unable to fix it, please open an issue at:

  https://github.com/k2-fsa/sherpa/issues/new

Thanks

--tokens /data/manju/tamil/exp/tokenizers/dc_re_rs_tvs_tokenizer_v2/tokenizer_spe_bpe_v512_max_6_pad/vocab.txt

What is the format of vocab.txt?
The expected format should like below
sym0 id0
sym1 id1
sym2 id2

e.g.
blk 0
a 1
b 2
c 3

Thanks @csukuangfj
I tried with tokenizer.vocab file generated from <Nemo_ROOT>/scripts/process_asr_text_tokenizer.py (https://github.com/NVIDIA/NeMo/blob/main/scripts/tokenizers/process_asr_text_tokenizer.py).
The format of tokenizer.vocab is like this:
   0
   0
்க      -0
ங்க      -1
▁s      -2
But, I still get this error:
sherpa/bin/offline_ctc_asr.py --nn-model /data/manju/tamil/exp/models/tamil_ctc_tl_dc_re_rs_tvs.pt --tokens /data/manju/tamil/exp/tokenizers/dc_re_rs_tvs_tokenizer_v2/tokenizer_spe_bpe_v512_max_6_pad/tokenizer.vocab kkkk.wav
2023-02-22 11:26:57,545 INFO [offline_ctc_asr.py:312] {'nn_model': '/data/manju/tamil/exp/models/tamil_ctc_tl_dc_re_rs_tvs.pt', 'tokens': '/data/manju/tamil/exp/tokenizers/dc_re_rs_tvs_tokenizer_v2/tokenizer_spe_bpe_v512_max_6_pad/tokenizer.vocab', 'HLG': None, 'lm_scale': 1.0, 'modified': True, 'search_beam': 20.0, 'output_beam': 8.0, 'min_active_states': 30, 'max_active_states': 10000, 'use_gpu': False, 'normalize_samples': True, 'sound_files': ['kkkk.wav']}
[F] /mnt/efs/manju/if/tools/sherpa/sherpa/csrc/symbol-table.cc:44:sherpa::SymbolTable::SymbolTable(const string&) 2023-02-22 11:26:58.010 Check failed: id2sym_.count(id) == 0 (1 vs. 0) Duplicated ID: 0
[ Stack-Trace: ]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/sherpa/lib/libsherpa_core.so(sherpa::GetStackTrace()+0x4b) [0x7f529559b27b]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/sherpa/lib/libsherpa_core.so(sherpa::SymbolTable::SymbolTable(std::string const&)+0x163c) [0x7f529559dfec]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/sherpa/lib/libsherpa_cpp_api.so(sherpa::OfflineRecognizerCtcImpl::OfflineRecognizerCtcImpl(sherpa::OfflineRecognizerConfig const&)+0x65) [0x7f5295602325]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/sherpa/lib/libsherpa_cpp_api.so(sherpa::OfflineRecognizer::OfflineRecognizer(sherpa::OfflineRecognizerConfig const&)+0x158) [0x7f52955fb048]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/_sherpa.cpython-39-x86_64-linux-gnu.so(+0x1f166) [0x7f5295641166]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/_sherpa.cpython-39-x86_64-linux-gnu.so(+0x45ea1) [0x7f5295667ea1]
python3() [0x507f97]
python3(_PyObject_MakeTpCall+0x2ec) [0x4f0f1c]
python3() [0x505f43]
python3() [0x503312]
python3() [0x4f13d0]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/torchaudio/_torchaudio.so(+0x34685) [0x7f52b9e5a685]
python3(_PyObject_MakeTpCall+0x2ec) [0x4f0f1c]
python3(_PyEval_EvalFrameDefault+0x5285) [0x4ed425]
python3() [0x4f8923]
python3(_PyEval_EvalFrameDefault+0x3d1) [0x4e8571]
python3() [0x4f8923]
python3(_PyEval_EvalFrameDefault+0x3d1) [0x4e8571]
python3() [0x4e729a]
python3(_PyEval_EvalCodeWithName+0x47) [0x4e6f27]
python3(PyEval_EvalCodeEx+0x39) [0x4e6ed9]
python3(PyEval_EvalCode+0x1b) [0x594f7b]
python3() [0x5c2457]
python3() [0x5be3b0]
python3() [0x456265]
python3(PyRun_SimpleFileExFlags+0x1a2) [0x5b81a2]
python3(Py_RunMain+0x37e) [0x5b570e]
python3(Py_BytesMain+0x39) [0x5890f9]
/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xf3) [0x7f534b2fc083]
python3() [0x588fae]
Traceback (most recent call last):
File "/mnt/efs/manju/if/tools/sherpa/sherpa/bin/offline_ctc_asr.py", line 359, in
main()
File "/mnt/efs/manju/if/tools/sherpa/sherpa/bin/offline_ctc_asr.py", line 315, in main
recognizer = create_recognizer(args)
File "/mnt/efs/manju/if/tools/sherpa/sherpa/bin/offline_ctc_asr.py", line 305, in create_recognizer
recognizer = sherpa.OfflineRecognizer(config)
RuntimeError:
Some bad things happened. Please read the above error messages and stack
trace. If you are using Python, the following command may be helpful

Left first solve the issues about tokens and then the issues about the models.
From the error log,

[F] /mnt/efs/manju/if/tools/sherpa/sherpa/csrc/symbol-table.cc:44:sherpa::SymbolTable::SymbolTable(const string&) 2023-02-22 11:26:58.010 Check failed: id2sym_.count(id) == 0 (1 vs. 0) Duplicated ID: 0

And from your posted
The format of tokenizer.vocab is like this:
<unk> 0
<pad> 0
்க -0
ங்க -1
▁s -2

You see <unk> and <pad> both have ID 0. That is not allowed. Please fix it first.

I fixed it, and my new file looks as below:
 0
 1
்க 2
But, I still end up getting below error:
[ Stack-Trace: ]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/sherpa/lib/libsherpa_core.so(sherpa::GetStackTrace()+0x4b) [0x7fedd07a427b]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/sherpa/lib/libsherpa_core.so(sherpa::SymbolTable::SymbolTable(std::string const&)+0x18b0) [0x7fedd07a7260]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/sherpa/lib/libsherpa_cpp_api.so(sherpa::OfflineRecognizerCtcImpl::OfflineRecognizerCtcImpl(sherpa::OfflineRecognizerConfig const&)+0x65) [0x7fedd080b325]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/sherpa/lib/libsherpa_cpp_api.so(sherpa::OfflineRecognizer::OfflineRecognizer(sherpa::OfflineRecognizerConfig const&)+0x158) [0x7fedd0804048]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/_sherpa.cpython-39-x86_64-linux-gnu.so(+0x1f166) [0x7fedd084a166]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/k2_sherpa-1.1-py3.9-linux-x86_64.egg/_sherpa.cpython-39-x86_64-linux-gnu.so(+0x45ea1) [0x7fedd0870ea1]
python3() [0x507f97]
python3(_PyObject_MakeTpCall+0x2ec) [0x4f0f1c]
python3() [0x505f43]
python3() [0x503312]
python3() [0x4f13d0]
/home/manjunath/anaconda3/envs/ice/lib/python3.9/site-packages/torchaudio/_torchaudio.so(+0x34685) [0x7fedef05e685]
python3(_PyObject_MakeTpCall+0x2ec) [0x4f0f1c]
python3(_PyEval_EvalFrameDefault+0x5285) [0x4ed425]
python3() [0x4f8923]
python3(_PyEval_EvalFrameDefault+0x3d1) [0x4e8571]
python3() [0x4f8923]
python3(_PyEval_EvalFrameDefault+0x3d1) [0x4e8571]
python3() [0x4e729a]
python3(_PyEval_EvalCodeWithName+0x47) [0x4e6f27]
python3(PyEval_EvalCodeEx+0x39) [0x4e6ed9]
python3(PyEval_EvalCode+0x1b) [0x594f7b]
python3() [0x5c2457]
python3() [0x5be3b0]
python3() [0x456265]
python3(PyRun_SimpleFileExFlags+0x1a2) [0x5b81a2]
python3(Py_RunMain+0x37e) [0x5b570e]
python3(Py_BytesMain+0x39) [0x5890f9]
/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xf3) [0x7fee80500083]
python3() [0x588fae]

Now it comes to the hard part.
please refer to
https://github.com/k2-fsa/sherpa/pull/198/commits/0e2c259144502fba9ff5d0b566944fa4b8e550ee
for how we add support for CTC models exported from torchaudio's pre-trained wav2vec 2.0 models.

Basically, you need to do the following:

Create a header file like below
https://github.com/k2-fsa/sherpa/blob/master/sherpa/csrc/offline-wav2vec2-ctc-model.h

Create a cpp implementation file like below
https://github.com/k2-fsa/sherpa/blob/master/sherpa/csrc/offline-wav2vec2-ctc-model.h

Change
https://github.com/k2-fsa/sherpa/blob/master/sherpa/cpp_api/offline-recognizer-ctc-impl.h

Change the following code to add support for your own model:
https://github.com/k2-fsa/sherpa/blob/92e3965cf1c00bbff31669f5d566d4e76500bc7f/sherpa/cpp_api/offline-recognizer-ctc-impl.h#L68-L89

@uni-manjunath-ke
Thanks!
One more thing: Could you provide a python script to show how to use this model?

What is the signature of the forward method of the mode? That is, what are the inputs and outputs?
What is the last layer of the model, e.g., nn.Linear, nn.Softmax, or nn.LogSoftmax ?
What are the feature extraction options of this model?  (You don't need to provide feature extraction code for the demo python script)

I will find some time to support it this weekend.

Sure, Thanks @csukuangfj
I have got this model by finetuning stt-hi-conformer-ctc-medium pretrained nemo model using the config file available at https://github.com/NVIDIA/NeMo/blob/main/examples/asr/conf/conformer/conformer_ctc_bpe.yaml
The encoder section in the config file contains the required data as below:
encoder:
target: nemo.collections.asr.modules.ConformerEncoder
feat_in: ${model.preprocessor.features}
feat_out: -1 # you may set it if you need different output size other than the default d_model
n_layers: 18 # total no of layers
d_model: 512 #--> o/p layer dimesion
The code-snippet for finetuning using Nemo pretrained models is as below:
import nemo
import nemo.collections.asr as nemo_asr
from pytorch_lightning.strategies import DDPStrategy
from jiwer import wer ##  libarry to compute WER
from statistics import mean  ## Used to compute mean WER
import pandas as pd #for IO
for wav file resampling & segmentation
from scipy.io import wavfile
to create directory if not exists
from pathlib import Path
import pytorch_lightning as pl
from omegaconf import DictConfig
basePath='/<home_user>/hindi/'
try:
from ruamel.yaml import YAML
except ModuleNotFoundError:
from ruamel_yaml import YAML
config_path = basePath + '/exp/config/hindi_custom_conformer_ctc_bpe.yaml'
yaml = YAML(typ='safe')
with open(config_path) as f:
params = yaml.load(f)
conf_ctc_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained(model_name="stt_hi_conformer_ctc_medium")
tokenDir = basePath + '/exp/tokenizers/tokenizer_dc_tvs_hdc_v3/tokenizer_spe_bpe_v512_pad/'
conf_ctc_model.change_vocabulary(new_tokenizer_dir=tokenDir, new_tokenizer_type="bpe")
tokenizer building ---
python /data/manju/hindi/scripts/process_asr_text_tokenizer.py   --manifest="/data/manju/hindi/exp/lists/train_hindi_dc_manifest.json"
--data_root="/data/manju/hindi/exp/tokenizer/"   --vocab_size=200   --tokenizer="spe"   --spe_type="bpe"  --spe_pad --log
train_manifest = basePath + '/exp/all_lists/lists/train_hindi_dc_TVS_HDC_manifest.json_small'
test_manifest = basePath + '/exp/all_lists/lists/test_hindi_dc_manifest.json_small'
valid_manifest = basePath + '/exp/all_lists/lists/valid_hindi_dc_TVS_HDC_manifest.json_small'
params['model']['train_ds']['manifest_filepath'] = train_manifest
params['model']['test_ds']['manifest_filepath'] = test_manifest
params['model']['validation_ds']['manifest_filepath'] = valid_manifest
Point to the data we'll use for fine-tuning as the training set
conf_ctc_model.setup_training_data(train_data_config=params['model']['train_ds'])
Point to the new validation data for fine-tuning
conf_ctc_model.setup_validation_data(val_data_config=params['model']['validation_ds'])
conf_ctc_model.setup_test_data(test_data_config=params['model']['test_ds'])
And now we can create a PyTorch Lightning trainer and call fit again.
modelSaveName = basePath + '/exp/models/hindi_tl_demo.nemo'
rootDir = basePath + '/exp/logs/'
trainer = pl.Trainer(strategy=DDPStrategy(find_unused_parameters=False),
accelerator='gpu',
devices=[2],
auto_lr_find = True,
auto_scale_batch_size = True,
val_check_interval = 0.25,
default_root_dir = rootDir,
max_epochs=3
)
trainer.fit(conf_ctc_model)
conf_ctc_model.save_to(modelSaveName)
Thanks

Hi @csukuangfj,
Please let us know, if there is any update on this? Thanks

Hi @csukuangfj,
Please let us know, if there is any update on this? Thanks

@uni-manjunath-ke
Please have a look at https://github.com/k2-fsa/sherpa/pull/332

Hi @csukuangfj, Thanks a lot.
I tried using this feature. Basically, it works fine if I use the command and wav files mentioned at https://github.com/k2-fsa/sherpa/pull/332 .
But, If I change the wave file to my own wave file, then it gives error as below. I am verifying on finetuned models, and keep you updated. Thanks
$ build/sherpa/bin/sherpa-offline --nn-model=../sherpa__notWorking/old/sherpa-nemo-ctc-en-citrinet-512/model.pt --tokens=../sherpa__notWorking/old/sherpa-nemo-ctc-en-citrinet-512/tokens.txt   --use-gpu=false --modified=false   --nemo-normalize=per_feature ./re_test__q_vNeC-nX4X0LWuORXDmp_l_0001.wav
[I] /mnt/efs/manju/if/tools/sherpa/sherpa/csrc/parse-options.cc:495:int sherpa::ParseOptions::Read(int, const char* const*) 2023-03-10 07:54:19.533 build/sherpa/bin/sherpa-offline --nn-model=../sherpa__notWorking/old/sherpa-nemo-ctc-en-citrinet-512/model.pt --tokens=../sherpa__notWorking/old/sherpa-nemo-ctc-en-citrinet-512/tokens.txt --use-gpu=false --modified=false --nemo-normalize=per_feature ./re_test__q_vNeC-nX4X0LWuORXDmp_l_0001.wav
[I] /mnt/efs/manju/if/tools/sherpa/sherpa/cpp_api/bin/offline-recognizer.cc:125:int main(int, char**) 2023-03-10 07:54:19.535 OfflineRecognizerConfig(ctc_decoder_config=OfflineCtcDecoderConfig(modified=False, hlg="", lm_scale=1, search_beam=20, output_beam=8, min_active_states=30, max_active_states=10000), feat_config=FeatureConfig(fbank_opts=FbankOptions(frame_opts=FrameExtractionOptions(samp_freq=16000, frame_shift_ms=10, frame_length_ms=25, dither=0, preemph_coeff=0.97, remove_dc_offset=True, window_type="povey", round_to_power_of_two=True, blackman_coeff=0.42, snip_edges=True, max_feature_vectors=-1), mel_opts=MelBanksOptions(num_bins=80, low_freq=20, high_freq=0, vtln_low=100, vtln_high=-500, debug_mel=False, htk_mode=False), use_energy=False, energy_floor=0, raw_energy=True, htk_compat=False, use_log_fbank=True, use_power=True, device="cpu"), normalize_samples=True, nemo_normalize="per_feature"), nn_model="../sherpa__notWorking/old/sherpa-nemo-ctc-en-citrinet-512/model.pt", tokens="../sherpa__notWorking/old/sherpa-nemo-ctc-en-citrinet-512/tokens.txt", use_gpu=False, decoding_method="greedy_search", num_active_paths=4)
[I] /mnt/efs/manju/if/tools/sherpa/sherpa/cpp_api/offline-recognizer-ctc-impl.h:172:void sherpa::OfflineRecognizerCtcImpl::WarmUp() 2023-03-10 07:54:20.864 WarmUp begins
[I] /mnt/efs/manju/if/tools/sherpa/sherpa/cpp_api/offline-recognizer-ctc-impl.h:185:void sherpa::OfflineRecognizerCtcImpl::WarmUp() 2023-03-10 07:54:21.294 WarmUp ended
[F] /mnt/efs/manju/if/tools/sherpa/sherpa/csrc/fbank-features.cc:34:std::pair<at::Tensor, float> sherpa::ReadWave(const string&, float) 2023-03-10 07:54:21.368 Failed to read ./re_test__q_vNeC-nX4X0LWuORXDmp_l_0001.wav
[ Stack-Trace: ]
./build/lib.linux-x86_64-cpython-39/sherpa/lib/libsherpa_core.so(sherpa::GetStackTrace()+0x4b) [0x7f119370ad0b]
build/sherpa/bin/sherpa-offline(sherpa::Logger::~Logger()+0x62) [0x56497c87f852]
./build/lib.linux-x86_64-cpython-39/sherpa/lib/libsherpa_core.so(+0x299ad) [0x7f11936c49ad]
./build/lib.linux-x86_64-cpython-39/sherpa/lib/libsherpa_cpp_api.so(sherpa::OfflineStream::AcceptWaveFile(std::string const&)+0x42) [0x7f1193778002]
build/sherpa/bin/sherpa-offline(+0xae8a) [0x56497c877e8a]
/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xf3) [0x7f114b9a1083]
build/sherpa/bin/sherpa-offline(+0xba5e) [0x56497c878a5e]
Aborted (core dumped)

./sherpa__notWorking/old/sherpa-nemo-ctc-en-citrinet-512/tokens.txt

How did you generate tokens.txt and models.pt?

(sherpa) manjunath@ip-10-40-6-153:/mnt/efs/manju/if/tools/sherpa$ soxi ./re_test__q_vNeC-nX4X0LWuORXDmp_l_0001.wav
Input File     : './re_test__q_vNeC-nX4X0LWuORXDmp_l_0001.wav'
Channels       : 1
Sample Rate    : 16000
Precision      : 25-bit
Duration       : 00:00:04.34 = 69440 samples ~ 325.5 CDDA sectors
File Size      : 278k
Bit Rate       : 512k
Sample Encoding: 32-bit Floating Point PCM

From your output:
Sample Encoding: 32-bit Floating Point PCM

Please convert it to 16-bit PCM.

Ok Thanks

Does it work now?

For simplicity, the code only deals with 16-bit PCM encoded WAVE files.
You can convert your input files manually outside of sherpa with tools like ffmpeg, sox, etc.

Hi @csukuangfj ,
Looping @titu1994 also for any suggestions.
It is failing with custom models.

I have converted '.nemo' to '.pt' file using  below commands:
conf_ctc_model = nemo_asr.models.EncDecCTCModelBPE.restore_from(restore_path=<.nemo FineTunedModelPath>)
conf_ctc_model.export(<path to save .pt file>)
This uses https://github.com/NVIDIA/NeMo/blob/main/scripts/export.py, script I believe.
Then I have converted tokens.txt to have  token with '0' in the begining, and remianing token starting from 1 to 512, as below:
   0
   1
்க      2

$build/sherpa/bin/sherpa-offline --nn-model=/data/manju/tamil/exp/models/noPad_tamil_ctc_tl_dc_re_rs_tvs.pt --tokens=/data/manju/tamil/exp/tokenizers/dc_re_rs_tvs_no_pad_data_tokenizer_v4/tokenizer_spe_bpe_v512_max_6/tokenizer.vocab_sherpa   --use-gpu=false --modified=false --nemo-normalize=per_feature ./16pcm_re_test__q_vNeC-nX4X0LWuORXDmp_l_0001.wav
[I] /mnt/efs/manju/if/tools/sherpa/sherpa/csrc/parse-options.cc:495:int sherpa::ParseOptions::Read(int, const char* const*) 2023-03-10 09:09:05.645 build/sherpa/bin/sherpa-offline --nn-model=/data/manju/tamil/exp/models/noPad_tamil_ctc_tl_dc_re_rs_tvs.pt --tokens=/data/manju/tamil/exp/tokenizers/dc_re_rs_tvs_no_pad_data_tokenizer_v4/tokenizer_spe_bpe_v512_max_6/tokenizer.vocab_sherpa --use-gpu=false --modified=false --nemo-normalize=per_feature ./16pcm_re_test__q_vNeC-nX4X0LWuORXDmp_l_0001.wav
[I] /mnt/efs/manju/if/tools/sherpa/sherpa/cpp_api/bin/offline-recognizer.cc:125:int main(int, char**) 2023-03-10 09:09:05.645 OfflineRecognizerConfig(ctc_decoder_config=OfflineCtcDecoderConfig(modified=False, hlg="", lm_scale=1, search_beam=20, output_beam=8, min_active_states=30, max_active_states=10000), feat_config=FeatureConfig(fbank_opts=FbankOptions(frame_opts=FrameExtractionOptions(samp_freq=16000, frame_shift_ms=10, frame_length_ms=25, dither=0, preemph_coeff=0.97, remove_dc_offset=True, window_type="povey", round_to_power_of_two=True, blackman_coeff=0.42, snip_edges=True, max_feature_vectors=-1), mel_opts=MelBanksOptions(num_bins=80, low_freq=20, high_freq=0, vtln_low=100, vtln_high=-500, debug_mel=False, htk_mode=False), use_energy=False, energy_floor=0, raw_energy=True, htk_compat=False, use_log_fbank=True, use_power=True, device="cpu"), normalize_samples=True, nemo_normalize="per_feature"), nn_model="/data/manju/tamil/exp/models/noPad_tamil_ctc_tl_dc_re_rs_tvs.pt", tokens="/data/manju/tamil/exp/tokenizers/dc_re_rs_tvs_no_pad_data_tokenizer_v4/tokenizer_spe_bpe_v512_max_6/tokenizer.vocab_sherpa", use_gpu=False, decoding_method="greedy_search", num_active_paths=4)
[I] /mnt/efs/manju/if/tools/sherpa/sherpa/cpp_api/offline-recognizer-ctc-impl.h:172:void sherpa::OfflineRecognizerCtcImpl::WarmUp() 2023-03-10 09:09:06.729 WarmUp begins
Aborted (core dumped)
Thanks

@uni-manjunath-ke
Could you use the code in
https://github.com/k2-fsa/sherpa/pull/332#issuecomment-1463507956
to test your model with torchscript to see if it works correctly before using sherpa.

Could you use the code in #332 (comment) to test your model with torchscript to see if it works correctly before using sherpa.

Hi @csukuangfj ,
I executed this code, and found that the results are similar to what have posted at #332 (comment)
Results obtained are:
print('samples', samples.shape, sample_rate)
features torch.Size([1, 80, 435]) tensor([435], device='cuda:0')
torch.Size([1, 109, 513])

Are you using a conformer ctc model?
Your subsampling factor is 435 / 109 = 4.
I need to change the code. please wait a minute.

Ya, I have used conformer ctc model.

Are you using a conformer ctc model?
Ya, I have used conformer ctc model.
Your subsampling factor is 435 / 109 = 4.
I need to change the code. please wait a minute.
Ok, Thanks

@uni-manjunath-ke
I has been fixed in https://github.com/k2-fsa/sherpa/pull/335
Please update your sherpa to the latest master and recompile.

Ok Thanks a lot

Hi @csukuangfj , I still get the same error after installing from latest master (git clone https://github.com/k2-fsa/sherpa.git).
sherpa$  build/sherpa/bin/sherpa-offline --nn-model=/data/manju/tamil/exp/models/noPad_tamil_ctc_tl_dc_re_rs_tvs.pt --tokens=/data/manju/tamil/exp/tokenizers/dc_re_rs_tvs_no_pad_data_tokenizer_v4/tokenizer_spe_bpe_v512_max_6/tokenizer.vocab_sherpa --use-gpu=false --modified=false --nemo-normalize=per_feature ./16pcm_re_test__q_vNeC-nX4X0LWuORXDmp_l_0001.wav
[I] /mnt/efs/manju/if/tools/sherpa/sherpa/csrc/parse-options.cc:495:int sherpa::ParseOptions::Read(int, const char* const*) 2023-03-10 13:07:24.965 build/sherpa/bin/sherpa-offline --nn-model=/data/manju/tamil/exp/models/noPad_tamil_ctc_tl_dc_re_rs_tvs.pt --tokens=/data/manju/tamil/exp/tokenizers/dc_re_rs_tvs_no_pad_data_tokenizer_v4/tokenizer_spe_bpe_v512_max_6/tokenizer.vocab_sherpa --use-gpu=false --modified=false --nemo-normalize=per_feature ./16pcm_re_test__q_vNeC-nX4X0LWuORXDmp_l_0001.wav
[I] /mnt/efs/manju/if/tools/sherpa/sherpa/cpp_api/bin/offline-recognizer.cc:125:int main(int, char**) 2023-03-10 13:07:24.965 OfflineRecognizerConfig(ctc_decoder_config=OfflineCtcDecoderConfig(modified=False, hlg="", lm_scale=1, search_beam=20, output_beam=8, min_active_states=30, max_active_states=10000), feat_config=FeatureConfig(fbank_opts=FbankOptions(frame_opts=FrameExtractionOptions(samp_freq=16000, frame_shift_ms=10, frame_length_ms=25, dither=0, preemph_coeff=0.97, remove_dc_offset=True, window_type="povey", round_to_power_of_two=True, blackman_coeff=0.42, snip_edges=True, max_feature_vectors=-1), mel_opts=MelBanksOptions(num_bins=80, low_freq=20, high_freq=0, vtln_low=100, vtln_high=-500, debug_mel=False, htk_mode=False), use_energy=False, energy_floor=0, raw_energy=True, htk_compat=False, use_log_fbank=True, use_power=True, device="cpu"), normalize_samples=True, nemo_normalize="per_feature"), nn_model="/data/manju/tamil/exp/models/noPad_tamil_ctc_tl_dc_re_rs_tvs.pt", tokens="/data/manju/tamil/exp/tokenizers/dc_re_rs_tvs_no_pad_data_tokenizer_v4/tokenizer_spe_bpe_v512_max_6/tokenizer.vocab_sherpa", use_gpu=False, decoding_method="greedy_search", num_active_paths=4)
[I] /mnt/efs/manju/if/tools/sherpa/sherpa/cpp_api/offline-recognizer-ctc-impl.h:182:void sherpa::OfflineRecognizerCtcImpl::WarmUp() 2023-03-10 13:07:26.130 WarmUp begins
Aborted (core dumped)

could you build a debug version of sherpa?
cd build
rm CMakeCache.txt
cmake -DCMAKE_BUILD_TYPE=Debug ..
make -j 6

and then use
gdb --args  \
 build/sherpa/bin/sherpa-offline --nn-model=/data/manju/tamil/exp/models/noPad_tamil_ctc_tl_dc_re_rs_tvs.pt --tokens=/data/manju/tamil/exp/tokenizers/dc_re_rs_tvs_no_pad_data_tokenizer_v4/tokenizer_spe_bpe_v512_max_6/tokenizer.vocab_sherpa --use-gpu=false --modified=false --nemo-normalize=per_feature ./16pcm_re_test__q_vNeC-nX4X0LWuORXDmp_l_0001.wav

(gdb) run

When it crashes, please use
(gdb) backtrace

and post the output of  thebacktrace.

By the way, how large is your vocabulary? If it's several thousand, please set the option: --modified=true

Conformer vocab is usually (128-1024)+1.
Citrinet is usually (256-1024)+1.
Asian languages use character encoder so roughly 5000 ish tokens.
Added some comment to note that Citrinet has 8x stride not 16x

run

Hi @csukuangfj , Thanks. Here is the output of gdb debugging. Meanwhile, as I understand Nvidia team is working on fixing torchscript export issues at https://github.com/k2-fsa/sherpa/issues/303 , Should wait for them to get back? Thanks
(sherpa) manjunath@ip-10-40-6-153:/mnt/efs/manju/if/tools/sherpa$ gdb --args   build/sherpa/bin/sherpa-offline --nn-model=/data/manju/tamil/exp/models/noPad_tamil_ctc_tl_dc_re_rs_tvs.pt --tokens=/data/manju/tamil/exp/tokenizers/dc_re_rs_tvs_no_pad_data_tokenizer_v4/tokenizer_spe_bpe_v512_max_6/tokenizer.vocab_sherpa --use-gpu=false --modified=false --nemo-normalize=per_feature ../16pcm_re_test__q_vNeC-nX4X0LWuORXDmp_l_0001.wav
GNU gdb (Ubuntu 9.2-0ubuntu1~20.04.1) 9.2
Copyright (C) 2020 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later http://gnu.org/licenses/gpl.html
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Type "show copying" and "show warranty" for details.
This GDB was configured as "x86_64-linux-gnu".
Type "show configuration" for configuration details.
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Reading symbols from build/sherpa/bin/sherpa-offline...
(No debugging symbols found in build/sherpa/bin/sherpa-offline)
(gdb) run
Starting program: /mnt/efs/manju/if/tools/sherpa/build/sherpa/bin/sherpa-offline --nn-model=/data/manju/tamil/exp/models/noPad_tamil_ctc_tl_dc_re_rs_tvs.pt --tokens=/data/manju/tamil/exp/tokenizers/dc_re_rs_tvs_no_pad_data_tokenizer_v4/tokenizer_spe_bpe_v512_max_6/tokenizer.vocab_sherpa --use-gpu=false --modified=false --nemo-normalize=per_feature ../16pcm_re_test__q_vNeC-nX4X0LWuORXDmp_l_0001.wav
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
[New Thread 0x7fff63d11000 (LWP 2520357)]
[New Thread 0x7fff63510000 (LWP 2520358)]
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[Thread 0x7fff554f4000 (LWP 2520386) exited]
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[Thread 0x7fff574f8000 (LWP 2520382) exited]
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[Thread 0x7fff584fa000 (LWP 2520380) exited]
[Thread 0x7fff54cf3000 (LWP 2520387) exited]
[Thread 0x7fff5b500000 (LWP 2520374) exited]
[Thread 0x7fff5c502000 (LWP 2520372) exited]
[Thread 0x7fff5cd03000 (LWP 2520371) exited]
[Thread 0x7fff5d504000 (LWP 2520370) exited]
[Thread 0x7fff5dd05000 (LWP 2520369) exited]
[Thread 0x7fff5e506000 (LWP 2520368) exited]
[Thread 0x7fff5ed07000 (LWP 2520367) exited]
[Thread 0x7fff5f508000 (LWP 2520366) exited]
[Thread 0x7fff5fd09000 (LWP 2520365) exited]
[Thread 0x7fff6050a000 (LWP 2520364) exited]
[Thread 0x7fff60d0b000 (LWP 2520363) exited]
[Thread 0x7fff6150c000 (LWP 2520362) exited]
[Thread 0x7fff61d0d000 (LWP 2520361) exited]
[Thread 0x7fff6250e000 (LWP 2520360) exited]
[Thread 0x7fff62d0f000 (LWP 2520359) exited]
[Thread 0x7fff63510000 (LWP 2520358) exited]
[Thread 0x7fff63d11000 (LWP 2520357) exited]
[I] /mnt/efs/manju/if/tools/sherpa/sherpa/csrc/parse-options.cc:495:int sherpa::ParseOptions::Read(int, const char* const*) 2023-03-13 07:30:49.461 /mnt/efs/manju/if/tools/sherpa/build/sherpa/bin/sherpa-offline --nn-model=/data/manju/tamil/exp/models/noPad_tamil_ctc_tl_dc_re_rs_tvs.pt --tokens=/data/manju/tamil/exp/tokenizers/dc_re_rs_tvs_no_pad_data_tokenizer_v4/tokenizer_spe_bpe_v512_max_6/tokenizer.vocab_sherpa --use-gpu=false --modified=false --nemo-normalize=per_feature ../16pcm_re_test__q_vNeC-nX4X0LWuORXDmp_l_0001.wav
[I] /mnt/efs/manju/if/tools/sherpa/sherpa/cpp_api/bin/offline-recognizer.cc:125:int main(int, char**) 2023-03-13 07:30:49.461 OfflineRecognizerConfig(ctc_decoder_config=OfflineCtcDecoderConfig(modified=False, hlg="", lm_scale=1, search_beam=20, output_beam=8, min_active_states=30, max_active_states=10000), feat_config=FeatureConfig(fbank_opts=FbankOptions(frame_opts=FrameExtractionOptions(samp_freq=16000, frame_shift_ms=10, frame_length_ms=25, dither=0, preemph_coeff=0.97, remove_dc_offset=True, window_type="povey", round_to_power_of_two=True, blackman_coeff=0.42, snip_edges=True, max_feature_vectors=-1), mel_opts=MelBanksOptions(num_bins=80, low_freq=20, high_freq=0, vtln_low=100, vtln_high=-500, debug_mel=False, htk_mode=False), use_energy=False, energy_floor=0, raw_energy=True, htk_compat=False, use_log_fbank=True, use_power=True, device="cpu"), normalize_samples=True, nemo_normalize="per_feature"), nn_model="/data/manju/tamil/exp/models/noPad_tamil_ctc_tl_dc_re_rs_tvs.pt", tokens="/data/manju/tamil/exp/tokenizers/dc_re_rs_tvs_no_pad_data_tokenizer_v4/tokenizer_spe_bpe_v512_max_6/tokenizer.vocab_sherpa", use_gpu=False, decoding_method="greedy_search", num_active_paths=4)
[I] /mnt/efs/manju/if/tools/sherpa/sherpa/cpp_api/offline-recognizer-ctc-impl.h:182:void sherpa::OfflineRecognizerCtcImpl::WarmUp() 2023-03-13 07:30:50.666 WarmUp begins
--Type  for more, q to quit, c to continue without paging--
Thread 1 "sherpa-offline" received signal SIGABRT, Aborted.
__GI_raise (sig=sig@entry=6) at ../sysdeps/unix/sysv/linux/raise.c:50
50        return ret;
(gdb)

could you build a debug version of sherpa?

cd build

rm CMakeCache.txt

cmake -DCMAKE_BUILD_TYPE=Debug ..

make -j 6

and then use

gdb --args  \

 build/sherpa/bin/sherpa-offline --nn-model=/data/manju/tamil/exp/models/noPad_tamil_ctc_tl_dc_re_rs_tvs.pt --tokens=/data/manju/tamil/exp/tokenizers/dc_re_rs_tvs_no_pad_data_tokenizer_v4/tokenizer_spe_bpe_v512_max_6/tokenizer.vocab_sherpa --use-gpu=false --modified=false --nemo-normalize=per_feature ./16pcm_re_test__q_vNeC-nX4X0LWuORXDmp_l_0001.wav

(gdb) run

When it crashes, please use

(gdb) backtrace

and post the output of  thebacktrace.

Please follow it to provide backtrace logs.

Thanks @csukuangfj
(gdb) backtrace
#0  __GI_raise (sig=sig@entry=6) at ../sysdeps/unix/sysv/linux/raise.c:50
#1  0x00007fffafe95859 in __GI_abort () at abort.c:79
#2  0x00007fffb013035a in __cxxabiv1::__terminate (handler=)
at /opt/conda/conda-bld/gcc-compiler_1654084175708/work/gcc/libstdc++-v3/libsupc++/eh_terminate.cc:48
#3  0x00007fffb01303c5 in std::terminate ()
at /opt/conda/conda-bld/gcc-compiler_1654084175708/work/gcc/libstdc++-v3/libsupc++/eh_terminate.cc:58
#4  0x00007fffb0130658 in __cxxabiv1::__cxa_throw (obj=, tinfo=0x7ffff7b39678 ,
dest=0x7fffe3c06430 c10::NotImplementedError::~NotImplementedError())
at /opt/conda/conda-bld/gcc-compiler_1654084175708/work/gcc/libstdc++-v3/libsupc++/eh_throw.cc:95
#5  0x00007fffe6f0c468 in torch::jit::InterpreterStateImpl::handleError(std::exception const&, bool, c10::NotImplementedError*, c10::optionalstd::string) () from /home/manjunath/anaconda3/envs/sherpa/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so
#6  0x00007fffe6f1a095 in torch::jit::InterpreterStateImpl::runImpl(std::vector<c10::IValue, std::allocatorc10::IValue >&) ()
from /home/manjunath/anaconda3/envs/sherpa/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so
#7  0x00007fffe6f0555f in torch::jit::InterpreterState::run(std::vector<c10::IValue, std::allocatorc10::IValue >&) ()
from /home/manjunath/anaconda3/envs/sherpa/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so
#8  0x00007fffe6ef59ea in torch::jit::GraphExecutorImplBase::run(std::vector<c10::IValue, std::allocatorc10::IValue >&) ()
from /home/manjunath/anaconda3/envs/sherpa/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so
#9  0x00007fffe6b7d49e in torch::jit::Method::operator()(std::vector<c10::IValue, std::allocatorc10::IValue >, std::unordered_map<std::string, c10::IValue, std::hashstd::string, std::equal_tostd::string, std::allocator<std::pair<std::string const, c10::IValue> > > const&) const () from /home/manjunath/anaconda3/envs/sherpa/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so
#10 0x00007ffff7ccf9b9 in c10::IValue torch::jit::Object::run_method<at::Tensor, at::Tensor>(std::string const&, at::Tensor&&, at::Tensor&&)
() from ./build/lib.linux-x86_64-cpython-39/sherpa/lib/libsherpa_core.so
#11 0x00007ffff7cce9df in sherpa::OfflineNeMoEncDecCTCModelBPE::Forward(at::Tensor, at::Tensor) ()
from ./build/lib.linux-x86_64-cpython-39/sherpa/lib/libsherpa_core.so
#12 0x00007ffff7ccec3f in sherpa::OfflineNeMoEncDecCTCModelBPE::WarmUp(at::Tensor, at::Tensor) ()
from ./build/lib.linux-x86_64-cpython-39/sherpa/lib/libsherpa_core.so
#13 0x00007ffff7f33d1a in sherpa::OfflineRecognizerCtcImpl::WarmUp() ()
from ./build/lib.linux-x86_64-cpython-39/sherpa/lib/libsherpa_cpp_api.so
#14 0x00007ffff7f32ca2 in sherpa::OfflineRecognizerCtcImpl::OfflineRecognizerCtcImpl(sherpa::OfflineRecognizerConfig const&) ()
from ./build/lib.linux-x86_64-cpython-39/sherpa/lib/libsherpa_cpp_api.so
--Type  for more, q to quit, c to continue without paging--
#15 0x00007ffff7f3aca0 in std::_MakeUniqsherpa::OfflineRecognizerCtcImpl::__single_object std::make_unique<sherpa::OfflineRecognizerCtcImpl, sherpa::OfflineRecognizerConfig const&>(sherpa::OfflineRecognizerConfig const&) ()
from ./build/lib.linux-x86_64-cpython-39/sherpa/lib/libsherpa_cpp_api.so
#16 0x00007ffff7f270b4 in sherpa::OfflineRecognizer::OfflineRecognizer(sherpa::OfflineRecognizerConfig const&) ()
from ./build/lib.linux-x86_64-cpython-39/sherpa/lib/libsherpa_cpp_api.so
#17 0x0000555555584e75 in ?? ()
#18 0x00007fffafe97083 in __libc_start_main (main=0x55555558492e, argc=7, argv=0x7fffffffdce8, init=, fini=,
rtld_fini=, stack_end=0x7fffffffdcd8) at ../csu/libc-start.c:308
#19 0x000055555558450e in ?? ()

Hi @csukuangfj , Pls let us know, if there is an update on this? Thanks

Hi @csukuangfj and @titu1994 , Pls let me know, if there is any update on this? Thanks, Sorry for reposting.

Hi @csukuangfj and @titu1994 , Pls let me know, if there is any update on this? Thanks, Sorry for reposting.

@uni-manjunath-ke
Sorry for the late reply and thanks for posting the debug info.
It is not clear what bad things happened from the backtrace.

Have you tried
https://github.com/k2-fsa/sherpa/pull/332#issuecomment-1463507956
and  does
log_probs = model(features, features_len)
print(log_probs.shape)

produce any output?

Also, are you using the fix from https://github.com/k2-fsa/sherpa/pull/332#issuecomment-1463660502
to export your conformer ctc model? If not, then could you describe how you export your NeMo model via torchscript?

Hi @csukuangfj ,

I have tried it and the output is given at:
https://github.com/k2-fsa/sherpa/issues/303#issuecomment-1463754807

I am exporting Nemo to torchscript as below:
m = nemo_asr.models.EncDecCTCModelBPE.from_pretrained('stt_en_conformer_ctc_small')
m.export("model.pt")

And, then using model.pt for decoding with sherpa. Thanks

I am exporting Nemo to torchscript as below:
m = nemo_asr.models.EncDecCTCModelBPE.from_pretrained('stt_en_conformer_ctc_small')
m.export("model.pt")

Have you had any problems with the code above?  I need to change the code of NeMo to get the export working.
Also, are you able to follow
https://k2-fsa.github.io/sherpa/cpp/pretrained_models/offline_ctc/nemo.html#how-to-convert-nemo-models-to-sherpa
to convert the following model
https://k2-fsa.github.io/sherpa/cpp/pretrained_models/offline_ctc/nemo.html#sherpa-nemo-ctc-en-conformer-small-english
to sherpa? Just want to make sure the entire pipeline is working from your side.

Hi @csukuangfj ,
I did not have errors while using export, pls find the logs:
[NeMo I 2023-03-23 09:17:49 mixins:170] Tokenizer SentencePieceTokenizer initialized with 512 tokens
[NeMo W 2023-03-23 09:17:49 modelPT:156] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.
Train config :
manifest_filepath: /PATH/train_manifest.json
sample_rate: 16000
batch_size: 8
shuffle: true
num_workers: 8
pin_memory: true
use_start_end_token: false
trim_silence: false
max_duration: 16
min_duration: 0.1
is_tarred: false
tarred_audio_filepaths: null
shuffle_n: 2048
bucketing_strategy: synced_randomized
bucketing_batch_size: null
[NeMo W 2023-03-23 09:17:49 modelPT:163] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s).
Validation config :
manifest_filepath: /PATH/valid_manifest.json
sample_rate: 16000
batch_size: 8
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: false
[NeMo W 2023-03-23 09:17:49 modelPT:169] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).
Test config :
manifest_filepath: /<path>/test_manifest.json
sample_rate: 16000
batch_size: 8
shuffle: false
num_workers: 8
pin_memory: true
use_start_end_token: false
[NeMo I 2023-03-23 09:17:49 features:267] PADDING: 0
[NeMo I 2023-03-23 09:17:50 save_restore_connector:243] Model EncDecCTCModelBPE was successfully restored from /data/manju/hindi/exp/models/cleaned_tokz_512_hindi_dc_tvs_hdc_tl.nemo.
[NeMo I 2023-03-23 09:17:50 export_utils:398] Swapped 108 modules
[NeMo W 2023-03-23 09:17:51 nemo_logging:349] /home/manjunath/anaconda3/envs/nemo/lib/python3.9/site-packages/nemo/collections/asr/modules/conformer_encoder.py:375: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if seq_length > self.max_audio_length:
[NeMo W 2023-03-23 09:17:51 nemo_logging:349] /home/manjunath/anaconda3/envs/nemo/lib/python3.9/site-packages/nemo/collections/asr/parts/submodules/multi_head_attention.py:670: UserWarning: floordiv is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').
center_pos = self.pe.size(1) // 2 + 1
[NeMo I 2023-03-23 09:19:29 exportable:89] Successfully exported EncDecCTCModelBPE to /data/<path>/model.pt

Gdb backtrace while running exported model in sherpa is as below:
#0  __GI_raise (sig=sig@entry=6) at ../sysdeps/unix/sysv/linux/raise.c:50
#1  0x00007fffafe95859 in __GI_abort () at abort.c:79
#2  0x00007fffb013035a in __cxxabiv1::__terminate (handler=) at /opt/conda/conda-bld/gcc-compiler_1654084175708/work/gcc/libstdc++-v3/libsupc++/eh_terminate.cc:48
#3  0x00007fffb01303c5 in std::terminate () at /opt/conda/conda-bld/gcc-compiler_1654084175708/work/gcc/libstdc++-v3/libsupc++/eh_terminate.cc:58
#4  0x00007fffb0130658 in __cxxabiv1::__cxa_throw (obj=, tinfo=0x7ffff7b39678 ,
dest=0x7fffe3c06430 c10::NotImplementedError::~NotImplementedError()) at /opt/conda/conda-bld/gcc-compiler_1654084175708/work/gcc/libstdc++-v3/libsupc++/eh_throw.cc:95
#5  0x00007fffe6f0c468 in torch::jit::InterpreterStateImpl::handleError(std::exception const&, bool, c10::NotImplementedError*, c10::optionalstd::string) ()
from /home/manjunath/anaconda3/envs/sherpa/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so
#6  0x00007fffe6f1a095 in torch::jit::InterpreterStateImpl::runImpl(std::vector<c10::IValue, std::allocatorc10::IValue >&) ()
from /home/manjunath/anaconda3/envs/sherpa/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so
#7  0x00007fffe6f0555f in torch::jit::InterpreterState::run(std::vector<c10::IValue, std::allocatorc10::IValue >&) ()
from /home/manjunath/anaconda3/envs/sherpa/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so
#8  0x00007fffe6ef59ea in torch::jit::GraphExecutorImplBase::run(std::vector<c10::IValue, std::allocatorc10::IValue >&) ()
from /home/manjunath/anaconda3/envs/sherpa/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so
#9  0x00007fffe6b7d49e in torch::jit::Method::operator()(std::vector<c10::IValue, std::allocatorc10::IValue >, std::unordered_map<std::string, c10::IValue, std::hashstd::string, std::equal_tostd::string, std::allocator<std::pair<std::string const, c10::IValue> > > const&) const () from /home/manjunath/anaconda3/envs/sherpa/lib/python3.9/site-packages/torch/lib/libtorch_cpu.so
#10 0x00007ffff7ccf9b9 in c10::IValue torch::jit::Object::run_method<at::Tensor, at::Tensor>(std::string const&, at::Tensor&&, at::Tensor&&) ()
from /mnt/efs/manju/if/tools/sherpa/build/lib.linux-x86_64-cpython-39/sherpa/lib/libsherpa_core.so
#11 0x00007ffff7cce9df in sherpa::OfflineNeMoEncDecCTCModelBPE::Forward(at::Tensor, at::Tensor) ()
from /mnt/efs/manju/if/tools/sherpa/build/lib.linux-x86_64-cpython-39/sherpa/lib/libsherpa_core.so
#12 0x00007ffff7ccec3f in sherpa::OfflineNeMoEncDecCTCModelBPE::WarmUp(at::Tensor, at::Tensor) ()
from /mnt/efs/manju/if/tools/sherpa/build/lib.linux-x86_64-cpython-39/sherpa/lib/libsherpa_core.so
#13 0x00007ffff7f33d1a in sherpa::OfflineRecognizerCtcImpl::WarmUp() () from /mnt/efs/manju/if/tools/sherpa/build/lib.linux-x86_64-cpython-39/sherpa/lib/libsherpa_cpp_api.so
#14 0x00007ffff7f32ca2 in sherpa::OfflineRecognizerCtcImpl::OfflineRecognizerCtcImpl(sherpa::OfflineRecognizerConfig const&) ()
from /mnt/efs/manju/if/tools/sherpa/build/lib.linux-x86_64-cpython-39/sherpa/lib/libsherpa_cpp_api.so
#15 0x00007ffff7f3aca0 in std::_MakeUniqsherpa::OfflineRecognizerCtcImpl::__single_object std::make_unique<sherpa::OfflineRecognizerCtcImpl, sherpa::OfflineRecognizerConfig const&>(sherpa::OfflineRecognizerConfig const&) () from /mnt/efs/manju/if/tools/sherpa/build/lib.linux-x86_64-cpython-39/sherpa/lib/libsherpa_cpp_api.so
#16 0x00007ffff7f270b4 in sherpa::OfflineRecognizer::OfflineRecognizer(sherpa::OfflineRecognizerConfig const&) ()
from /mnt/efs/manju/if/tools/sherpa/build/lib.linux-x86_64-cpython-39/sherpa/lib/libsherpa_cpp_api.so
#17 0x0000555555584e75 in ?? ()
#18 0x00007fffafe97083 in __libc_start_main (main=0x55555558492e, argc=7, argv=0x7fffffffdcd8, init=, fini=, rtld_fini=, stack_end=0x7fffffffdcc8)
at ../csu/libc-start.c:308
#19 0x000055555558450e in ?? ()

Hi @csukuangfj , If possible could you please try with my models:
Pls download hindi_tl_demo.nemo model from https://huggingface.co/manjunathke/dummy_hindi .  Then convert them to ".pt" & generate tokens.txt file. You can then check those models and we may come to know, if there is an issue.
Pls do not use the hindi_tl_demo.pt & hindi_tokens.txt  from https://huggingface.co/manjunathke/dummy_hindi They are old ones, and in correct.
Thanks

authors: csukuangfj titu1994 uni-manjunath-ke


text:
Downloading v1.22.2-k3s2/sha256sum-amd64.txt faild

Environmental Info:
K3s Version: v1.22.2+k3s2

Please see https://github.com/k3s-io/k3s/issues/5942#issuecomment-1203643893


text:
No module named request

Checklist for submitting an issue to KickThemOut:

[x] I have carefully read the README file and haven't managed to resolve my issue.
[x] I have searched the issues of this repo and believe that this is not a duplicate.
[x] I am running the latest version of KickThemOut.

OS name & version: Kali Linux 2018.2 amd64
Python version: 3.4
Scapy version: 3-0.25
Nmap version: 0.6.1
Link of Gist: https://gist.github.com/ParadoxalGamer/b5e7315dc21453755a0c46bbdc57f4b3

The soft tell me I don't have the request module but it's not in the requirements, what can I do to fix the problem ? (please remember i'm french and this is because I can write with some errors<br/ >

I'm having the same issue.
I read the requirements needed, so I went to install them. I was able to get python-scapy installed, so when I ran sudo python kickthemout.py, it gave me an error saying "no module named nmap." Okay, I installed python-nmap. Boom. I run sudo python kickthemout.py again, and it said "no module named request". Oh, I'll install python netifaces. I installed it, and when I ran sudo python kickthemout.py it kept giving me the error above.
Any ideas if I did something wrong?

Hi there. Apologies for the long delay. Try running pip3 install requests

Closing due to inactivity; assuming that the problem has been resolved. If not, please do not hesitate to reopen this issue.

authors: AspecTT ParadoxalGamer k4m4


text:
Multicluster setup (n > 2)

change the Makefile targets so that they run in a for loop
add tests for terratest for round-robin @ 3 clusters (failover is still pending)
change the helm chart to be able to set up more than one hostAlias entry for a pod
add simple script that generates the yaml configs for k3d clusters so that we are DRY

to verify the change locally i suggest:
# deploy 5 clusters
make deploy-full-local-setup CLUSTERS_NUMBER=5

# do some testing
for i in {0..20} ; do make test-round-robin | grep mess ; done

# cleanup
make destroy-full-local-setup CLUSTERS_NUMBER=5

another consequence of this PR is that when trying to incorporate a change in local helm chart (that hasn't been published yet), one can do make deploy-full-local-setup CHART=./chart/k8gb
Signed-off-by: Jirka Kremser jiri.kremser@gmail.com

please consider native iterators (see example):

I know about it, but I wanted to avoid 'dynamic targets' or pattern rules, or what is it called to make it simple. I like the way how you combine two strings together and then allow for "random access" in it so I've borrowed that for the geo tags to have it as one-liner (space separated) instead of those 10 lines and 10 different variables

We probably should fail fast in make terratest target if CLUSTER_NUMBER !=3 with a clear message that tests are expecting 3 clusters to avoid confusion in future.

it's there

Thanks for reviews!
terrascan & CodeQL checks are being stucked, but these were passing before, so merging..


text:
operator pulsar-operator (0.12.0)

Signed-off-by: Fushu Wang fswang@streamnative.io
Thanks submitting your Operator. Please check below list before you create your Pull Request.
New Submissions

[ ] Are you familiar with our contribution guidelines?
[ ] Have you packaged and deployed your Operator for Operator Framework?
[ ] Have you tested your Operator with all Custom Resource Definitions?
[ ] Have you tested your Operator in all supported installation modes?
[ ] Have you considered whether you want use semantic versioning order?
[ ] Is your submission signed?
[ ] Is operator icon set?

Updates to existing Operators

[x] Did you create a ci.yaml file according to the update instructions?
[x] Is your new CSV pointing to the previous version with the replaces property if you chose replaces-mode via the updateGraph property in ci.yaml?
[x] Is your new CSV referenced in the appropriate channel defined in the package.yaml or annotations.yaml ?
[x] Have you tested an update to your Operator when deployed via OLM?
[x] Is your submission signed?

Your submission should not

[x] Modify more than one operator
[x] Modify an Operator you don't own
[x] Rename an operator - please remove and add with a different name instead
[x] Modify any files outside the above mentioned folders
[x] Contain more than one commit. Please squash your commits.

Operator Description must contain (in order)

[x] Description about the managed Application and where to find more information
[x] Features and capabilities of your Operator and how to use it
[x] Any manual steps about potential pre-requisites for using your Operator

Operator Metadata should contain

[x] Human readable name and 1-liner description about your Operator
[x] Valid category name1
[x] One of the pre-defined capability levels2
[x] Links to the maintainer, source code and documentation
[x] Example templates for all Custom Resource Definitions intended to be used
[x] A quadratic logo

Remember that you can preview your CSV here.
--
1 If you feel your Operator does not fit any of the pre-defined categories, file an issue against this repo and explain your need
2 For more information see here

/merge possible

/merge possible

/merge possible

authors: FushuWang framework-automation


text:
operator project-quay (3.8.0-rc.6)

Add v3.8.0-rc.6 to community operators

/merge possible

/merge possible

authors: framework-automation quay-devel