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py-kaldi-asr

(★ 169)

Some simple wrappers around kaldi-asr intended to make using kaldi's (online) decoders as convenient as possible.

파일 탐색기

최종 버전 다운로드 (.zip)

- dw961.wav
- fail1.wav
- gsp1.wav
- kaldi-asr.pc
- lsen1.wav
- single.wav
- asr_client.py
- asr_server.py
- chain_incremental.py
- chain_live.py
- chain_online.py
- chain_wavfile.py
- chain_wavfile3.py
- gmm_incremental.py
- __init__.py
- gmm.pyx
- gmm_wrappers.cpp
- gmm_wrappers.h
- nnet3.pyx
- nnet3_wrappers.cpp
- nnet3_wrappers.h
.gitignore
LICENSE
Makefile
MANIFEST.in
README.md
setup.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/gooofy/py-kaldi-asr

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd py-kaldi-asr

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install .

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

3. Make

보통

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Make Linux/macOS는 보통 기본 설치되어 있습니다. Windows는 별도 설치(예: MSYS2, WSL)가 필요합니다.

make

생성된 빌드 설정을 바탕으로 실제 컴파일을 진행해 실행 파일을 만듭니다.

✅ 에러 없이 끝나면 성공입니다. 생성된 실행 파일을 직접 실행해보세요.

// repository documentation

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# py-kaldi-asr

Some simple wrappers around kaldi-asr intended to make using kaldi's online nnet3-chain
decoders as convenient as possible. Kaldi's online GMM decoders are also supported.

Target audience are developers who would like to use kaldi-asr as-is for speech
recognition in their application on GNU/Linux operating systems.

Constructive comments, patches and pull-requests are very welcome.

Getting Started
===============

We recommend using pre-trained modules from the [zamia-speech](http://zamia-speech.org/) project
to get started. There you will also find a tutorial complete with links to pre-built binary packages
to get you up and running with free and open source speech recognition in a matter of minutes:

[Zamia Speech Tutorial](https://github.com/gooofy/zamia-speech#get-started-with-our-pre-trained-models)

Example Code
------------

Simple wav file decoding:

```python
from kaldiasr.nnet3 import KaldiNNet3OnlineModel, KaldiNNet3OnlineDecoder

MODELDIR    = 'data/models/kaldi-generic-en-tdnn_sp-latest'
WAVFILE     = 'data/dw961.wav'

kaldi_model = KaldiNNet3OnlineModel (MODELDIR)
decoder     = KaldiNNet3OnlineDecoder (kaldi_model)

if decoder.decode_wav_file(WAVFILE):

s, l = decoder.get_decoded_string()

print
    print u"*****************************************************************"
    print u"**", WAVFILE
    print u"**", s
    print u"** %s likelihood:" % MODELDIR, l
    print u"*****************************************************************"
    print

else:

print "***ERROR: decoding of %s failed." % WAVFILE
```

Please check the examples directory for more example code.

Requirements
============

* Python 2.7 or 3.5+
* NumPy
* Cython
* [kaldi-asr](http://kaldi-asr.org/ "kaldi-asr.org")

Setup Notes
===========

Source
------

At the time of this writing kaldi-asr does not seem to have an official way to
install it on a system.

So, for now we will rely on pkg-config to provide LIBS and CFLAGS for compilation:
Create a file called `kaldi-asr.pc` somewhere in your `PKG_CONFIG_PATH` that provides
this information - here is what such a file could look like (details depend on your OS environment):

```bash
kaldi_root=/opt/kaldi

Name: kaldi-asr
Description: kaldi-asr speech recognition toolkit
Version: 5.2
Requires: atlas
Libs: -L${kaldi_root}/tools/openfst/lib -L${kaldi_root}/src/lib -lkaldi-decoder -lkaldi-lat -lkaldi-fstext -lkaldi-hmm -lkaldi-feat -lkaldi-transform -lkaldi-gmm -lkaldi-tree -lkaldi-util -lkaldi-matrix -lkaldi-base -lkaldi-nnet3 -lkaldi-online2 -lkaldi-cudamatrix -lkaldi-ivector -lfst
Cflags: -I${kaldi_root}/src  -I${kaldi_root}/tools/openfst/include
```

make sure `kaldi_root` points to wherever your kaldi checkout lives in your filesystem.

ATLAS
-----

You may need to install ATLAS headers even if you didn't need them to compile Kaldi.

```
$ sudo apt install libatlas-dev
```

License
=======

My own code is Apache licensed unless otherwise noted in the script's copyright
headers.

Some scripts and files are based on works of others, in those cases it is my
intention to keep the original license intact. Please make sure to check the
copyright headers inside for more information.

Author
======

Guenter Bartsch <guenter@zamia.org><br/>
Kaldi 5.1 adaptation contributed by mariasmo https://github.com/mariasmo  
Kaldi GMM model support contributed by David Zurow https://github.com/daanzu  
Python > 3.5 support contributed by Jakob Kruse https://github.com/jakob1111996