README.md

Source codes of NJU-LAMDA for the ChaLearn LAP 2016 challenge (The Code Follows MIT license.)

This package was developed by Mr. Chen-Lin Zhang ([email protected]). For any problem concerning the code, please feel free to contact Mr. Zhang. This packages are free for academic usage. You can run them at your own risk. For other purposes, please contact Prof. Jianxin Wu ([email protected]).

Operating System:

Ubuntu Linux

Requirements:

Matlab R2014b

GPU With CUDA Support

CUDNN

MatConvNet v1.0.20

LuaJIT

Torch7 (with cunn, cutorch support)

iTorch

python2.7

anaconda2

python_speech_features: https://github.com/jameslyons/python_speech_features

libxgboost

ipython

libffmpeg

Setup:

1. Extracting Images From Videos

First, please put the Test Video files into /test folder like training. For example, /test/testing80_01/a.mp4

Then, please use MATLAB and execute readdata_test.m. It will extract images from videos, and save as /test_jpg/a.mp4/1.jpg, etc.

2. Extract Audio Feature From Video

Suppose you have download all the data needed in the current folder, these will be many *.zip files.

Extract wav files from the mp4 video files

     shell extract.sh                # Unzip all the *.zip files.
     shell mp42wav.sh                # Extract wav files from mp4 files in the current directory.

Save these file into ./train_wav/ ./test_wav/ folders seperately

Extract audio features from wav, each into a csv file.

     python wav2logfbank.py

Concat those csv files into torch Tensors and save them to disk.

     cd ../data_logfbank/  
     th train2torch.lua              # Merge train csv files and save into disk.

3. Predicting Model From Images

First, in eval_val_reg_resnet.m,eval_val_reg_avg_max.m and eval_val_reg_avg_max_l28.m line 2, please change MatConvNet location to your own MatConvNet location.

Then, please execute the aforementioned three MATLAB files. You will get predictions_reg_avg_max_l28.csv, predictions_reg_avg_max.csv and predictions_reg_res.csv.

Besides, You can change the model number from epoch-1 to epoch-2, you will get different results.

4. Predict Model From Audio

jupyter notebook # Open itorch notebook

Open and run logreg.ipynb file # This is the main file to train the audio model.

# The trained model will be saved to disk to make prediction

Make predictions

    th predict.lua                  # Make predictions on the test data. 
                                    # Note the y_test.csv file must be provided.
                                    # The final predictions file is predictions.csv.

5. csv Order Transforming

In this Step, we change the generated “.csv” files in Step 3 and 4 into the correct order.

In MATLAB, please firstly use the “uiimport” command to import the generated “.csv” files.

Then, import correct examples like training_gt.csv or provided predictions_val.csv (only uiimport name column and rename it to VideoName_test).

Then, change the transform.m Line 1 to change the generated file name for the csv.

It will output the csv file in the correct order.

6. Score Fusion and Output

First, please use “uiimport” to import all the csv files into MATLAB (including redictions_reg_avg_max_l28.csv, predictions_reg_avg_max.csv, predictions_reg_res.csv and y_test.csv).

Then, please run the fusion.m. You will get the final output.