snr-calc 0.1.1

A package to calculate SNR from audio files

Signal to Noise Ratio Calculation -

A Python package for calculating the Signal-to-Noise Ratio (SNR) of audio files. It supports processing individual audio files or all audio files within a directory and allows users to specify the frequency range of the signal.

Table of Contents

• Features
• Installation
• Usage
  • Calculating SNR for a Single File
  • Calculating SNR for All Files in a Directory
• Requirements
• Contributing
• License

Features

• Calculate SNR for individual audio files or for all files in a directory.
• Supports custom signal frequency ranges and sample rates.
• Handles common audio formats like WAV, MP3, and FLAC.

Installation

1. Clone the repository or download the package.

2. Navigate to the package directory and run:

   pip install .
   

Alternatively, if the package is available on PyPI, you can install it directly using:

pip install snr_calc

Usage

Calculating SNR for a Single File

from snr_package.snr_calc import process_audio_file

# Specify the path to the audio file
audio_file_path = "path/to/your/audio_file.wav"

# Calculate SNR with a custom signal frequency range and sample rate
snr_value = process_audio_file(audio_file_path, signal_freq_range=(500, 4000), sample_rate=None)

# Display the SNR result
if snr_value is not None:
    print(f"SNR for {audio_file_path}: {snr_value} dB")
else:
    print("Failed to calculate SNR.")

Calculating SNR for All Files in a Directory

from snr_package.snr_calc import process_directory

# Specify the path to the directory
directory_path = "path/to/your/audio_directory"

# Calculate SNR for all audio files in the directory
snr_results = process_directory(directory_path, signal_freq_range=(500, 4000), sample_rate=None)

# Display the SNR results for each file
if snr_results:
    for file_name, snr_value in snr_results.items():
        print(f"File: {file_name}, SNR: {snr_value} dB")
else:
    print("No audio files found or failed to calculate SNR.")

Requirements

• Python 3.10 or higher
• Required Python packages:
  • librosa
  • numpy
  • scipy

These dependencies are automatically installed when you install the package via pip.

Contributing

Contributions are welcome! If you want to contribute to the development of this package:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeature).
3. Commit your changes (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature/YourFeature).
5. Create a Pull Request.

Please ensure your code passes all existing tests and add new tests if necessary.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Explanation

• Overview: Gives a brief description of the package and its purpose.
• Table of Contents: Provides easy navigation within the README.
• Features: Highlights key features of the package.
• Installation: Details how to install the package.
• Usage: Includes code snippets to demonstrate how to use the package for single files and directories.
• Requirements: Lists package dependencies.
• Contributing: Offers guidelines for developers who want to contribute to the package.
• License: States the license under which the package is distributed. Adjust it as needed.