Currently we only have build instructions, mentions of copying binaries around, and no mention of NuGet.

See this thread
https://groups.google.com/forum/?utm_medium=email&utm_source=footer#!msg/kaldi-help/1AMGQ54tn0E/kacpkoizAAAJ
for the problem-- for CUDA 10.1 the rpath we add for CUDA is not sufficient because they put the CUBLAS library somewhere else. Making the issue so we don't lose track of it.

Documentation Is:

• Missing
• Needed
• Confusing
• Not Sure?

E

1. Summary

It would be nice, if to SpeechRecognition documentation would be added links to these parameters of each speech recognition engines/API:

1. Languages by supporting engine/API
2. Pricing
3. Limits of free plan (if exists)

2. Argumentation

2.1. Common cause

Saving a users time.

2.2. Details

For e

when i run the example which using timit to build a lstm+ctc system,i meet a mistake,the error as follows：

can you give a suggest how to deal with it,thank very much.

Current documentation in README explains how to install the toolkit and how to run examples. However, I don't think this is enough for users who want to make some changes to the existing recipes or make their own new recipe. In that case, one needs to understand what run.sh does step by step, but I think docs for that are missing at the moment. It would be great if we provide documentation for:

For example, documentation of AudioToSpectrogramPreprocessor is missing.

We investigated that with @redoctopus and she has found that nemo_asr.rst doesn't include several modules.

https://github.com/NVIDIA/NeMo/blob/master/docs/sources/source/collections/nemo_asr.rst

So the bigger issue includes: taking a look at all implemented components and checking whether the associated rst files are

When following the manual install guide i had to install the following additional dependencies manually:

sudo apt-get install pulseaudio swig libpulse-dev libasound2-dev
sudo pip3 install pocketsphinx spidev

After, it all works great on Raspberry Pi Zero W!

We would benefit from tutorial how to convert DNN models trained either by HTK 3.5.2-BETA or by Kaldi. I am specifically interested how you converted DNN models trained by Kaldi later used in Japanese speech dictation published here.

Add the ability to answer weather forecast questions. DO NOT USE any API key because Dragonfire is an application that runs on the client's machine.

基于anaconda3安装：
1.为masr创建一个基于python3.6的虚拟环境，环境名称为masr
conda create -n masr python=3.6 pip numpy jupyter
2.切换到masr环境
source activate masr
3.安装几个必要的模块
pip install librosa
conda install pytorch-cpu==1.0.1 torchvision-cpu -c pytorch
pip install flask
4.解压ctcdecode.zip到anaconda3/envs/masr/lib/python3.6/site-packages/路径下，这样就不用去辛苦编译ctcdecode啦
[ctcdecode.zip](https://github.com/lukhy/masr/file

It seems this file was last updated for 1.0.3. It is out of sync with the actual code. For example the index.d.ts file says:

        /**
         * Set up artyom for the application. This function will set the default language used by artyom
         * or notice the user if artyom is not supported in the actual browser.
         * @see http://docs.ourcodeworld.com/projects/artyom-js/doc

@lfchener 我看报错这行代码是您最近添加的，我是pip安装的环境， 并按照readme 一步步执行的，但执行到infer时报错，是readme过时了吗？谢谢。
Traceback (most recent call last):
File "/home/shenxz/.IdeaIC2019.2/config/plugins/python-ce/helpers/pydev/pydevd.py", line 1415, in _exec
pydev_imports.execfile(file, globals, locals) # execute the script
File "/home/shenxz/code/DeepSpeech/infer.py", line 152, in
main()
File "/home/sh

Hi, I have received status: 5 from kaldi gstreamer, which is not mentioned in the documentation. I think it has something to do with the encoding, but can you please provide a comprehensive list of statuses that can be returned along with their meaning?