Added LibROSA installation guide.

SampleRNN/README.md

@@ -33,13 +33,51 @@ Once complete, let us install the library now:
 python3 setup.py develop
 ```
 
+## Additional Packages Needed
+To be able to run the SampleRNN application, the PYNQ OS is still missing some packages (i.e. LibROSA). The following are steps to install them.
+
+To begin with, download the LLVM 6.0.1 source code from [here](http://releases.llvm.org/download.html#6.0.1) and copy it onto the board, preferably in a Downloads folder. Unzip the file:
+```sh
+sudo su
+tar -xf llvm-6.0.1.src.tar.gz
+```
+Create a new directory and configure the LLVM build:
+```sh
+mkdir llvm_build
+cd llvm_build
+tmux
+cmake ~/Downloads/llvm-6.0.1.src -DLLVM_TARGETS_TO_BUILD="ARM" -DCMAKE_BUILD_TYPE="Release"
+```
+Begin the build:
+```sh
+cmake --build . -- -j1
+```
+Install once the build finishes and restart the board:
+```sh
+cmake --build . --target install
+shutdown -r now
+```
+Install additional packages, in this order as some packages which are installed with others need to be a different version to work:
+```sh
+sudo su
+pip3 install librosa==0.6.1
+pip3 install llvmlite==0.24.0
+pip3 install numba==0.39.0
+shutdown -r now
+```
+To test if this was successful, we should be able to import the package now:
+```sh
+sudo su
+python3
+import librosa
+```
+
 ## Running SampleRNN
 Once PyTorch is ported, the SampleRNN repo can be cloned, the steps can be followed on the repo but executed with the following command and **profiling.py** file, which is compatible with the accelerators designed and also profiles the application to monitor performance:
 ```sh
 python3 profiling.py --exp TEST64 --frame_size 16 4 --n_rnn 2 --dataset piano --cuda False --sample_length 16000 --dim 64
 ```
 This command generates one second of audio @ 16000Hz
 
-
 ## PYNQ-Z1 Image
 An image file with PyTorch v0.4.1 on PYNQ OS v2.3 can be downloaded from [here](). This also includes SampleRNN files.