mica-gender-from-audio

Generate gender and SAD timestamps of audio based on neural network models trained in Keras. Input must be a text file containing full paths to either mp4/mkv media files or .wav audio files, and optionally the path to the directory where all the output will be stored (default=$proj_dir/expt).
Outputs will be a text file for each movie/audio file, each line of which will contain the start and end times for the speech segment followed by the gender (male/female). Frame level posteriors are also saved in the output directory.

Usage:

bash generate_gender_timestamps.sh [-h] [-c config_file] movie_paths.txt (out_dir)  
e.g.: bash generate_gender_timestamps.sh -c ./config.sh demo.txt DEMO  
where:   
-h              : Show help 
-c              : Configuration file
movie_paths.txt : Text file consisting of complete paths to media files (eg, .mp4/.mkv) on each line    
out_dir         : Directory in which to store all output files (default: "$PWD/gender_out_dir")

Example config file:

nj=4                # Number of files to process simultaneously 
feats_flag="y"      # "y"/"n" flag to keep kaldi-feature files after inference
wavs_flag="n"       # "y"/"n" flag to keep .wav audio files after inference
sad_overlap=0       # % overlap in SAD-segments (range: 0-1, 0 for no overlap) (single segment is 0.64s)
gender_overlap=0    # % overlap in GENDER-segments (single segment is 0.96s) 
uniform_seg_len=2.0   # Segment length for uniform speaker-segmentation

Dependencies :

kaldi                    :   ensure that all kaldi binaries are added to system path. If not,
                                 either add them to system path, or modify kaldi_root in 1st line of
                                 'path.sh' to reflect kaldi installation directory.
keras, tensorflow        :   required to load data, model and make VAD predictions.
Other python libraries required include numpy, scipy, resampy.
h5py==2.7.1
Keras==2.1.5
numpy==1.14.2
requests==2.18.4
resampy==0.2.0
scipy==1.0.0
six==1.12.0
tensorflow==1.4.1

This tool can be used for noise-robust gender identification from audio. Two parallel systems are implemented for this purpose:

Speech Activity Detection (SAD), and
Gender Identification (GID) of speech segments.

Both of the DNN-based systems make predictions at segment-level as opposed to traditional frame-level analysis. Segment duration for the SAD system is 0.64s (design choice) and for the GID system is 0.96s (pre-trained VGGish embeddings). For more details about the architecture and training procedures, please refer to the ICASSP '19 paper (SAD), and INTERSPEECH '18 paper (GID).

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