Gender Recognition using Voice

This repository is about building a deep learning model using TensorFlow to recognize gender of a given speaker's audio.

Requirements

• TensorFlow 2.x.x
• Scikit-learn
• Numpy
• Pandas
• Librosa

Installing the required libraries:

pip3 install -r requirements.txt

Dataset used

Mozilla's Common Voice large dataset is used here, and some preprocessing has been performed:

• Filtered out invalid samples.
• Filtered only the samples that are labeled in genre field.
• Balanced the dataset so that number of female samples are equal to male.
• Used Mel Spectrogram feature extraction technique to get a vector of a fixed length from each voice sample, the data folder contain only the features and not the actual mp3 samples (the dataset is too large, about 13GB).

If you wish to download the dataset and extract the features files (.npy files) on your own, preparation.py is the responsible script for that, once you unzip it, put preparation.py in the root directory of the dataset and run it.

This will take sometime to extract features from the audio files and generate new .csv files.

Training

You can customize your model in utils.py file under the create_model() function and then run:

python train.py

Recognizing

recognition.py is the code responsible for gender recognizing your audio files:

python recognition.py --help

Output:

usage: recognition.py [-h] [-f FILE]

Gender recognition script, this will load the model you trained, and perform inference on a sample you provide.

optional arguments:
-h, --help            show this help message and exit
-f FILE, --file FILE  The path to the file, preferred to be in WAV format

• For instance, to get gender of the file samples/fa_006.wav, you can:

  sudo python recognition.py --file "samples/fa_006.wav"
  

  Output:

  Result: female
  Probabilities:     Male: 6.27%     Female: 93.73%
  

  There are some audio samples in samples folder for you to test with, some it is grabbed from LibriSpeech dataset.