Url: https://github.com/UFAL-DSG/alex-asr The Alex ASR software package basically is an incremental speech decoder using the Kaldi ASR toolkit. It can be used for various types of GMM-HMM acoustic models and nnet2 ones.
After having done a clone of its Git repository, you can compile it as described on the github page or if you'd like to compile it with NVIDIA CUDA GPU support (to also support acoustic models trained using GPUs), do the following:
<repo-dir>/prepare_env.sh
and change the line ./configure –shared' to './configure –shared –use-cuda=yes
under the section Configure Kaldi
<repo-dir>/Makefile
and add the following lines at the top:
CUDATKDIR = <add local path to CUDA dir here, usually /usr/local/cuda>
CUDA_INCLUDE= -I$(CUDATKDIR)/include
CUDA_LDFLAGS = -L$(CUDATKDIR)/lib64 -Wl,-rpath,$(CUDATKDIR)/lib64
CUDA_LDLIBS = -lcublas -lcudart -lcurand
CXXFLAGS
definition: \ <newline-here> -DHAVE_CUDA $(CUDA_INCLUDE)
LDFLAGS =
: $(CUDA_LDFLAGS) $(CUDA_LDLIBS)
python setup.py build
.<repo-dir>/libs/kaldi/tools/openfst
directory and run the following command manually: ./configure –prefix=`pwd` –enable-static –enable-shared –enable-far –enable-ngram-fsts –with-pic CXX=g++ CXXFLAGS=“-fPIC” LDFLAGS=“” LIBS=“-ldl”
python setup.py build
from the <repo-dir>
and the compilation process should now be successfully completed (i.e. without errors).
The above modifications were tested after having succesfully completed the default compile instructions on Alex ASR's github page.
Locally installed NVIDIA CUDA version was 7.5: https://developer.nvidia.com/cuda-75-downloads-archive.
Alex ASR was compiled against version hash 261fbb540a2dd40cb5248258dcbea77f4fdeee49 of the Kaldi ASR git repo instead of the one pointed at by Alex ASR's scripts.