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--- |
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library_name: tf-keras |
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tags: |
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- SpeakerRecognition |
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- Fast Fourier Transform (FFT) |
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- Convnet |
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- speech-recordings |
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- SpeechClassification |
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--- |
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## Model description |
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This model helps to classify speakers from the frequency domain representation of speech recordings, obtained via Fast Fourier Transform (FFT). |
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The model is created by a 1D convolutional network with residual connections for audio classification. |
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This repo contains the model for the notebook [**Speaker Recognition**](https://keras.io/examples/audio/speaker_recognition_using_cnn/). |
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Full credits go to [**Fadi Badine**](https://twitter.com/fadibadine) |
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## Dataset Used |
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This model uses a [**speaker recognition dataset**](https://www.kaggle.com/kongaevans/speaker-recognition-dataset) of Kaggle |
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## Intended uses & limitations |
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This should be run with `TensorFlow 2.3` or higher, or `tf-nightly`. |
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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`. |
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## Training and evaluation data |
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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. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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| name | learning_rate | decay | beta_1 | beta_2 | epsilon | amsgrad | training_precision | |
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|----|-------------|-----|------|------|-------|-------|------------------| |
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|Adam|0.0010000000474974513|0.0|0.8999999761581421|0.9990000128746033|1e-07|False|float32| |
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## Model Plot |
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<details> |
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<summary>View Model Plot</summary> |
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</details> |
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<center> |
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Model By : <a href="https://github.com/robotjellyzone">Kavya Bisht</a> |
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</center> |
