bhargava-swara 0.0.6

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. You'll need to:

  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.

  See Google's Generative AI Docs (https://cloud.google.com/generative-ai/docs) for details.



- **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.



```python

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!")



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