Speaker Identification using Neural Net.
Run the file in Google Colab.
This is a support repo for the main Project - Personalised Voice Assistant.
Dataset Source - Kaggle
Epochs = 30
Accuracy ~ 90%
To use the Speaker Identification from scratch, you will be needing a data set. For the testing version of our model we trained on the Speaker recognition dataset from kaggle.
Install the requirements by running
pip install -r requirements.txt or pip3 install -r requirements.txt
Download the Dataset or else create your own data set by speaking for 10 mins after running the file
python record.py or python3 record.py
And split the generated data.wav into 1 second files using the audio-clip.py file.
Run
python audio-clip.py or python3 audio-clip.py
And train the neural net by running the the speaker-identification.ipynb file or run
python speaker_identification.py or python3 speaker_identification.py
After training, save the generated model.h5 locally in the root folder, the repo. And run
python predict.py or python3 predict.py
(depending upon your environment) for real time predction.
This is a Purely open-source project, and feel free to suggest the changes.
To contribute Fork it, make the changes and push a pull request and we will get back to you.
Licensed under GNU General Public License v3.0