bhargava-swara 0.0.7

A library for analysis and synthesis of Indian classical music

Bhargava Swara

A Python library for analyzing and visualizing Indian classical music, including raga, tala, tempo, tradition, ornaments, full analysis, and mel-frequency spectrograms.

Prerequisites

• Gemini API Key:
  This library uses Google's Gemini API for music analysis. To obtain a key, follow these steps:

  1. Sign up for a Google Cloud account.
  2. Enable the Generative AI API in the Google Cloud Console.
  3. Create an API key in the "Credentials" section.
     Refer to Google's Generative AI Docs for detailed instructions.

• Audio Files:
  Supported formats include WAV and MP3 for spectrogram generation.

Installation

Install the library using pip:

pip install bhargava_swara

Usage

Music Analysis

Analyze various aspects of Indian classical music using the Gemini API.

from bhargava_swara import (
    analyze_raga,
    analyze_tala,
    analyze_tempo,
    analyze_tradition,
    analyze_ornaments,
    analyze_music_full
)

# Set your Gemini API key
api_key = "YOUR_API_KEY"
audio = "path/to/audio.wav"

# Individual analyses
print(f"Raga: {analyze_raga(audio, api_key)}")
print(f"Tala: {analyze_tala(audio, api_key)}")
print(f"Tempo: {analyze_tempo(audio, api_key)}")
print(f"Tradition: {analyze_tradition(audio, api_key)}")
print(f"Ornaments: {analyze_ornaments(audio, api_key)}")

# Full analysis
print(f"Full Analysis:\n{analyze_music_full(audio, api_key)}")

Mel-Frequency Spectrogram Generation

Generate a mel-frequency spectrogram to visualize the frequency content of an audio file over time.

from bhargava_swara import generate_mel_spectrogram

# Define input and output paths
audio = "path/to/audio.wav"
output = "path/to/output_mel_spectrogram.png"

# Generate the spectrogram
generate_mel_spectrogram(audio, output, n_mels=128, fmax=8000)
print("Mel spectrogram generated successfully!")

Parameters:

• audio: Path to the input audio file (e.g., WAV or MP3).
• output: Path to save the PNG file (e.g., "spectrogram.png").
• n_mels: Number of mel bands (default: 128).
• fmax: Maximum frequency in Hz (default: 8000).

Dependencies

• google-generativeai>=0.1.0
• librosa>=0.10.0
• matplotlib>=3.7.0
• numpy>=1.24.0

Contributing

Contributions are welcome! Please submit a pull request or open an issue on the GitHub repository (if available).

License

This library is licensed under the MIT License. See the LICENSE file for details.

Change Log
==========

0.0.1 (26/03/2025)
-------------------
- First Release

===========

0.0.2 (27/03/2025)
-------------------
- Fixed missing module files in package

0.0.3 (27/03/2025)
-------------------
- Fixed ornaments detection file

0.0.4 (27/03/2025)
-------------------
- Fixed real time full analysis file

0.0.5 (27/03/2025)
-------------------
- Added mel-frequency spectogram generation

0.0.6 (27/03/2025)
-------------------
- seaborn included. README.txt updated

0.0.7 (27/03/2025)
-------------------
- mel-frequency spectogram issue resolved. README.md is created