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PaddleSpeech/speechx
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Hui Zhang cd1ced4ea0
add nnetout struct 		2 years ago
..
cmake add u2 nnet, u2 nnet main, codelab, and can compile 2 years ago
docker dir arch (#1347) 3 years ago
examples add nnetout struct 2 years ago
patch add copyright 3 years ago
speechx add nnetout struct 2 years ago
tools add u2 nnet, u2 nnet main, codelab, and can compile 2 years ago
.clang-format add u2 nnet, u2 nnet main, codelab, and can compile 2 years ago
.gitignore fix speech egs 3 years ago
CMakeLists.txt add u2 nnet, u2 nnet main, codelab, and can compile 2 years ago
README.md add u2 nnet, u2 nnet main, codelab, and can compile 2 years ago
build.sh refactor speechx cmake 2 years ago

README.md

SpeechX -- All in One Speech Task Inference

Environment

We develop under:

  • python - 3.7
  • docker - registry.baidubce.com/paddlepaddle/paddle:2.2.2-gpu-cuda10.2-cudnn7
  • os - Ubuntu 16.04.7 LTS
  • gcc/g++/gfortran - 8.2.0
  • cmake - 3.16.0

Please using tools/env.sh to create python venv, then source venv/bin/activate to build speechx.

We make sure all things work fun under docker, and recommend using it to develop and deploy.

Build

  1. First to launch docker container.
docker run --privileged  --net=host --ipc=host -it --rm -v $PWD:/workspace --name=dev registry.baidubce.com/paddlepaddle/paddle:2.2.2-gpu-cuda10.2-cudnn7 /bin/bash
  • More Paddle docker images you can see here.
  1. Create python environment.
bash tools/venv.sh
  1. Build speechx and examples.
source venv/bin/activate
./build.sh
  1. Go to examples to have a fun.

More details please see README.md under examples.

Valgrind (Optional)

If using docker please check --privileged is set when docker run.

  • Fatal error at startup: a function redirection which is mandatory for this platform-tool combination cannot be set up
apt-get install libc6-dbg
  • Install
pushd tools
./setup_valgrind.sh
popd

TODO

Deepspeech2 with linear feature

  • DecibelNormalizer: there is a small difference between the offline and online db norm. The computation of online db norm reads features chunk by chunk, which causes the feature size to be different different with offline db norm. In normalizer.cc:73, the samples.size() is different, which causes the different result.