Pascal API
We provide APIs for Object Pascal.
In other words, you can develop the following types of applications using Object Pascal:
Voice activity detection
Streaming speech recognition (i.e., real-time speech recognition)
Non-streaming speech recognition
on Windows, Linux, and macOS.
Hint
For macOS, both Apple Silicon (i.e., macOS arm64, M1/M2/M3) and Intel chips are supported.
Note
We will support text-to-speech, audio tagging, keyword spotting, speaker recognition, speech identification, and spoken language identification with object pascal later.
In the following, we describe how to use the object pascal API to decode files.
We use macOS below as an example. You can adapt it for Linux and Windows.
Hint
We support both static link and dynamic link; the example below uses
dynamic link. You can pass -DBUILD_SHARED_LIBS=OFF to cmake if you
want to use static link.
On the Windows platform, it supports only dynamic link though.
Install free pascal
Please visit https://wiki.freepascal.org/Installing_the_Free_Pascal_Compiler for installation.
To check that you have installed fpc successfully, please run:
fpc -h
which should print the usage information of fpc.
Build sherpa-onnx
mkdir -p $HOME/open-source
cd $HOME/open-source
git clone https://github.com/k2-fsa/sherpa-onnx
cd sherpa-onnx
mkdir build
cd build
cmake \
-DBUILD_SHARED_LIBS=ON \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_INSTALL_PREFIX=./install \
..
cmake --build . --target install --config Release
ls -lh install/lib
You should get the following two shared library files:
(py38) fangjuns-MacBook-Pro:build fangjun$ ls -lh install/lib/
total 59696
-rw-r--r-- 1 fangjun staff 25M Aug 14 14:09 libonnxruntime.1.17.1.dylib
lrwxr-xr-x 1 fangjun staff 27B Aug 14 14:18 libonnxruntime.dylib -> libonnxruntime.1.17.1.dylib
-rwxr-xr-x 1 fangjun staff 3.9M Aug 15 15:01 libsherpa-onnx-c-api.dylib
Non-streaming speech recognition from files
We use the Whisper tiny.en model below as an example.
Hint
We have hardcoded the model filenames in the code.
cd $HOME/open-source/sherpa-onnx
cd pascal-api-examples/non-streaming-asr/
wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-whisper-tiny.en.tar.bz2
tar xvf sherpa-onnx-whisper-tiny.en.tar.bz2
rm sherpa-onnx-whisper-tiny.en.tar.bz2
fpc \
-dSHERPA_ONNX_USE_SHARED_LIBS \
-Fu$HOME/open-source/sherpa-onnx/sherpa-onnx/pascal-api \
-Fl$HOME/open-source/sherpa-onnx/build/install/lib \
./whisper.pas
# It will generate a file ./whisper
The output logs of the above fpc command are given below:
Free Pascal Compiler version 3.2.2 [2021/05/16] for x86_64
Copyright (c) 1993-2021 by Florian Klaempfl and others
Target OS: Darwin for x86_64
Compiling ./whisper.pas
Compiling /Users/fangjun/open-source/sherpa-onnx/sherpa-onnx/pascal-api/sherpa_onnx.pas
Assembling sherpa_onnx
Assembling whisper
Linking whisper
ld: warning: dylib (/Users/fangjun/open-source/sherpa-onnx/build/install/lib//libsherpa-onnx-c-api.dylib) was built for newer macOS version (10.14) tha
n being linked (10.8)
1530 lines compiled, 3.8 sec
Explanation of the options for the fpc command:
-dSHERPA_ONNX_USE_SHARED_LIBSIt defines a symbol
SHERPA_ONNX_USE_SHARED_LIBS, which means we want to use dynamic link in the code. If you omit it, it will use static link. Please search for the stringSHERPA_ONNX_USE_SHARED_LIBSin the file https://github.com/k2-fsa/sherpa-onnx/blob/master/sherpa-onnx/pascal-api/sherpa_onnx.pas if you want to learn more.
-Fu$HOME/open-source/sherpa-onnx/pascal-apiIt specifies the unit search path. Our sherpa_onnx.pas is inside the directory
$HOME/open-source/sherpa-onnx/pascal-apiand we have to tellfpcwhere to find it.
-Fl$HOME/sherpa-onnx/build/install/libIt tells
fpcwhere to look forlibsherpa-onnx-c-api.dylib.
After running the above fpc command, we will find an executable file whisper
in the current directory, i.e., $HOME/open-source/sherpa-onnx/pascal-api-examples/non-streaming-asr/whisper:
(py38) fangjuns-MacBook-Pro:non-streaming-asr fangjun$ ls -lh ./whisper
-rwxr-xr-x 1 fangjun staff 2.3M Aug 16 12:13 ./whisper
If we run it:
(py38) fangjuns-MacBook-Pro:non-streaming-asr fangjun$ ./whisper
dyld[23162]: Library not loaded: @rpath/libsherpa-onnx-c-api.dylib
Referenced from: <3AE58F60-4925-335D-89A5-B30FD7D97D7E> /Users/fangjun/open-source/sherpa-onnx/pascal-api-examples/non-streaming-asr/whisper
Reason: tried: '/Users/fangjun/py38/lib/python3.8/site-packages/libsherpa-onnx-c-api.dylib' (no such file), '/usr/local/Cellar/ghostscript/9.55.0/lib/libsherpa-onnx-c-api.dylib' (no such file), '/Users/fangjun/py38/lib/python3.8/site-packages/libsherpa-onnx-c-api.dylib' (no such file), '/usr/local/Cellar/ghostscript/9.55.0/lib/libsherpa-onnx-c-api.dylib' (no such file), '/libsherpa-onnx-c-api.dylib' (no such file), '/System/Volumes/Preboot/Cryptexes/OS@rpath/libsherpa-onnx-c-api.dylib' (no such file), '/usr/local/lib/libsherpa-onnx-c-api.dylib' (no such file), '/usr/lib/libsherpa-onnx-c-api.dylib' (no such file, not in dyld cache)
Abort trap: 6
You can see it cannot find libsherpa-onnx-c-api.dylib.
At the compilation time, we have used -Fl$HOME/sherpa-onnx/build/install/lib
to tell the compiler fpc where to find libsherpa-onnx-c-api.dylib.
At the runtime, we also need to do something to tell the executable where to look
for libsherpa-onnx-c-api.dylib.
The following command does exactly that:
(py38) fangjuns-MacBook-Pro:non-streaming-asr fangjun$ export DYLD_LIBRARY_PATH=$HOME/open-source/sherpa-onnx/build/install/lib:$DYLD_LIBRARY_PATH
(py38) fangjuns-MacBook-Pro:non-streaming-asr fangjun$ ./whisper
TSherpaOnnxOfflineRecognizerResult(Text := After early nightfall, the yellow lamps would light up here and there the squalid quarter of the brothels., Tokens := [ After, early, night, fall, ,, the, yellow, lamps, would, light, up, here, and, there, the, squ, alid, quarter, of, the, bro, the, ls, .], Timestamps := [])
NumThreads 1
Elapsed 0.803 s
Wave duration 6.625 s
RTF = 0.803/6.625 = 0.121
Hint
If you are using Linux, please replace DYLD_LIBRARY_PATH with LD_LIBRARY_PATH.
Congratulations! You have successfully managed to use the object pascal API with Whisper for speech recognition!
You can find more examples at:
Colab notebook
We provide a colab notebook
for you to try this section step by step.