Open LLM List
speaker recognition
keras-ioIntroduction
This project provides a machine learning model for speaker recognition, utilizing a 1D convolutional network with residual connections. It classifies speakers based on the frequency domain representation of speech recordings, obtained through Fast Fourier Transform (FFT). The model is designed for audio classification and is implemented using TensorFlow Keras.
Architecture
The speaker recognition model employs a 1D convolutional neural network (CNN) with residual connections. This architecture processes audio data transformed into the frequency domain using FFT. The CNN is trained to distinguish different speakers by analyzing these frequency representations.
Training
Dataset
The model is trained using a speaker recognition dataset available on Kaggle. The dataset includes speech samples and background noise samples, which are sorted into separate folders for audio and noise. The noise samples are resampled to a 16000 Hz sampling rate before being mixed with the speech samples for data augmentation.
Procedure
The augmented audio data, after undergoing FFT, is used for training the model. The training process involves optimizing the model using the Adam optimizer with specific hyperparameters.
Hyperparameters
- Learning Rate: 0.001
- Decay: 0.0
- Beta_1: 0.9
- Beta_2: 0.999
- Epsilon: 1e-07
- Amsgrad: False
- Training Precision: float32
Guide: Running Locally
To run the speaker recognition model locally, follow these basic steps:
- Environment Setup: Ensure you have TensorFlow 2.3 or higher installed. The
tf-nightlyversion is also suitable. - Dependencies: Install
ffmpegfor audio processing, particularly for resampling noise samples to 16000 Hz. - Clone Repository: Obtain the model code from the repository.
- Prepare Data: Download the Kaggle speaker recognition dataset and organize it into audio and noise folders. Resample noise samples as needed.
- Train Model: Use the provided scripts to train the model on your local machine.
- Hardware: For efficient training, consider using cloud-based GPUs such as those offered by AWS, Google Cloud, or Azure.
License
The project and its resources are provided under the terms specified by the original authors and contributors. Ensure compliance with any licenses associated with TensorFlow, Keras, and other dependencies.
