
## 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>
{"library_name": "keras", "tags": ["SpeakerRecognition", "Fast Fourier Transform (FFT)", "Convnet", "speech-recordings", "SpeechClassification"]}
keras tensorboard SpeakerRecognition Fast Fourier Transform (FFT) Convnet speech-recordings SpeechClassification has_space region:us
TAGS
#keras #tensorboard #SpeakerRecognition #Fast Fourier Transform (FFT) #Convnet #speech-recordings #SpeechClassification #has_space #region-us 

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.


Full credits go to Fadi Badine


Dataset Used
------------


This model uses a 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:



Model Plot
----------



View Model Plot
!Model Image




Model By : <a href="URL Bisht</a>

### Training hyperparameters


The following hyperparameters were used during training:



Model Plot
----------



View Model Plot
!Model Image




Model By : <a href="URL Bisht</a>
TAGS
#keras #tensorboard #SpeakerRecognition #Fast Fourier Transform (FFT) #Convnet #speech-recordings #SpeechClassification #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:



Model Plot
----------



View Model Plot
!Model Image




Model By : <a href="URL Bisht</a>
passage: TAGS
#keras #tensorboard #SpeakerRecognition #Fast Fourier Transform (FFT) #Convnet #speech-recordings #SpeechClassification #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:



Model Plot
----------



View Model Plot
!Model Image




Model By : <a href="URL Bisht</a>