https://github.com/x4nth055/gender-recognition-by-voice
Building a Deep learning model that predicts the gender of a speaker using TensorFlow 2
https://github.com/x4nth055/gender-recognition-by-voice
Last synced: about 21 hours ago
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Building a Deep learning model that predicts the gender of a speaker using TensorFlow 2
- Host: GitHub
- URL: https://github.com/x4nth055/gender-recognition-by-voice
- Owner: x4nth055
- License: mit
- Created: 2020-06-09T12:08:09.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-04-25T08:55:32.000Z (over 2 years ago)
- Last Synced: 2025-04-14T19:12:35.919Z (6 months ago)
- Language: Python
- Homepage: https://www.thepythoncode.com/article/gender-recognition-by-voice-using-tensorflow-in-python
- Size: 87.3 MB
- Stars: 124
- Watchers: 5
- Forks: 44
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Gender Recognition using Voice
This repository is about building a deep learning model using TensorFlow 2 to recognize gender of a given speaker's audio. Read this [tutorial](https://www.thepythoncode.com/article/gender-recognition-by-voice-using-tensorflow-in-python) for more information.
## Requirements
- TensorFlow 2.x.x
- Scikit-learn
- Numpy
- Pandas
- PyAudio
- Librosa
Cloning the repository:
git clone https://github.com/x4nth055/gender-recognition-by-voice
Installing the required libraries:
pip3 install -r requirements.txt
## Dataset used
[Mozilla's Common Voice](https://www.kaggle.com/mozillaorg/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](https://librosa.github.io/librosa/generated/librosa.feature.melspectrogram.html) feature extraction technique to get a vector of a fixed length from each voice sample, the [data](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`](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`](utils.py) file under the `create_model()` function and then run:
python train.py
## Testing
[`test.py`](test.py) is the code responsible for testing your audio files or your voice:
python test.py --help
**Output:**
usage: test.py [-h] [-f FILE]
Gender recognition script, this will load the model you trained, and perform
inference on a sample you provide (either using your voice or a file)
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 `test-samples/27-124992-0002.wav`, you can:
python test.py --file "test-samples/27-124992-0002.wav"
**Output:**
Result: male
Probabilities: Male: 96.36% Female: 3.64%
There are some audio samples in [test-samples](test-samples) folder for you to test with, it is grabbed from [LibriSpeech dataset](http://www.openslr.org/12).
- To make inference on your voice instead, you need to:
python test.py
Wait until you see `"Please speak"` prompt and start talking, it will stop recording as long as you stop talking.