[Demo of tensorflow environment configuraton (local)]

Setup

Clone this repo to your local machine, and add the speech_rec directory as a system variable to your ~/.profile. Instructions given for bash shell:

git clone https://github.com/AlexQianYi/speech_rec
cd speech_rec
echo "export speech_rec=${PWD}" >> ~/.profile
echo "export PYTHONPATH=${PWD}/src:${PYTHONPATH}" >> ~/.profile
source ~/.profile

Create a Conda environment (Install Conda first)

conda create --name tf-rnn python=3
source activate tf-rnn
cd $speech_rec
pip install -r requirements.txt

Install TensorFlow

If you have a NVIDIA GPU with already installed

pip install tensorflow-gpu==1.0.1

If you will be running TensorFlow on CPU only (e.g. a MacBook Pro), use the following command (if you get an error the first time you run this command read below):

pip install --upgrade\
 https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.1-py3-none-any.whl

Run unittests

The test example of whether RNN can run on your computer.

python $speech_rec/src/tests/train_framework/tf_train_ctc_test.py

Run RNN training

All configurations for the RNN training script can be found in $speech_rec/configs/neural_network.ini

python $speech_rec/src/train_framework/tf_train_ctc.py

NOTE: If you have a GPU available, the code will run faster if you set tf_device = /gpu:0 in configs/neural_network.ini

TensorBoard configuration

To visualize your results via tensorboard:

tensorboard --logdir=$speech_rec/models/nn/debug_models/summary/

• TensorBoard result can be found in your browser at http://localhost:6006.
• tf.name_scope is used to define parts of the network for visualization in TensorBoard. TensorBoard automatically finds any similarly structured network parts, such as identical fully connected layers and groups them in the graph visualization.
• Related to this are the tf.summary.* methods that log values of network parts, such as distributions of layer activations or error rate across epochs. These summaries are grouped within the tf.name_scope.
• See the official TensorFlow documentation for more details.

Data set

The data is separated into folders:

- Train: train-clean-100-wav (5 examples)
- Test: test-clean-wav (2 examples)
- Dev: dev-clean-wav (2 examples)