This repository implements a speaker recognition model utilizing Mel-Frequency Cepstral Coefficients (MFCCs) and machine learning techniques. It aims to identify a specific target speaker (labeled as "1") amidst audio recordings containing the target speaker and other individuals (labeled as "0").
- samples: Training voice files named 0_voice and 1_voice based on speaker labels.
- tests: Testing voice files mirroring the structure of samples.
- app.ipynb: Jupyter Notebook for model training, evaluation, and analysis.
- generateDataSet.py: Python script extracting MFCCs and generating datasets (samples_dataset and tests_dataset).
- requirements.txt: Required dependencies for project execution.
- Data organization: Clear directory structure and naming conventions for efficient access and management.
- MFCC extraction: Extraction of MFCCs to capture speaker-specific vocal characteristics.
- Jupyter Notebook workflow: Interactive environment for model development and experimentation.
- Machine learning model: Implemented in app.ipynb for speaker identification.
Install the required dependencies:
pip install -r requirements.txt- Install dependencies listed in requirements.txt.
- Run generateDataSet.py to extract MFCCs and generate datasets.
- Open app.ipynb in Jupyter Notebook for training, evaluation, and analysis.