readme: ---
  library_name: keras
  tags:
  - SpeakerRecognition
  - Fast Fourier Transform (FFT)
  - Convnet
  - speech-recordings
  - SpeechClassification
  ---
  
  ## Model description
  This model helps to classify speakers from the frequency domain representation of speech recordings, obtained via Fast Fourier Transform (FFT).
  The model is created by a 1D convolutional network with residual connections for audio classification.
  
  This repo contains the model for the notebook [**Speaker Recognition**](https://keras.io/examples/audio/speaker_recognition_using_cnn/).
  
  Full credits go to [**Fadi Badine**](https://twitter.com/fadibadine)
  
  ## Dataset Used
  This model uses a [**speaker recognition dataset**](https://www.kaggle.com/kongaevans/speaker-recognition-dataset) of Kaggle
  
  ## Intended uses & limitations
  This should be run with `TensorFlow 2.3` or higher, or `tf-nightly`. 
  Also, The noise samples in the dataset need to be resampled to a sampling rate of 16000 Hz before using for this model so, In order to do this, you will need to have installed `ffmpg`.
  
  ## Training and evaluation data
  During dataset preparation, the speech samples & background noise samples were sorted and categorized into 2 folders - audio & noise, and then noise samples were resampled to 16000Hz & then the background noise was added to the speech samples to augment the data. After that, the FFT of these samples was given to the model for the training & evaluation part.
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  
  | name | learning_rate | decay | beta_1 | beta_2 | epsilon | amsgrad | training_precision |
  |----|-------------|-----|------|------|-------|-------|------------------|
  |Adam|0.0010000000474974513|0.0|0.8999999761581421|0.9990000128746033|1e-07|False|float32|
  
   ## Model Plot
  
  <details>
  <summary>View Model Plot</summary>
  
  ![Model Image](./model.png)
  
  </details>
  
  <center>
  Model By : <a href="https://github.com/robotjellyzone">Kavya Bisht</a>
  </center>