Metadata-Version: 2.4
Name: bhargava_swara
Version: 0.0.15
Summary: A library for analysis and synthesis of Indian classical music
Home-page: 
Author: Kuchi Chaitanya Krishna Deepak
Author-email: kckdeepak29@gmail.com
License: MIT
Keywords: music,analysis,synthesis,carnatic,hindustani,indian classical music,raga,tala,spectograms,signal
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Education
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: google-generativeai>=0.1.0
Requires-Dist: librosa>=0.10.0
Requires-Dist: matplotlib>=3.7.0
Requires-Dist: numpy>=1.24.0
Requires-Dist: seaborn>=0.11.0
Requires-Dist: sounddevice>=0.4.0
Requires-Dist: scipy>=1.10.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
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Dynamic: license
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# Bhargava Swara

A Python library for analyzing and visualizing Indian classical music, including spectrogram generation (Mel-frequency, Chroma, CQT, and Cent filterbank), raga, tala, tempo, tradition, ornaments, and full analysis.

## Prerequisites

- **Gemini API Key:**  
  This library uses Google's Gemini API for music analysis. To obtain a key:  
  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](https://cloud.google.com/generative-ai/docs) for details

- **Audio Files:**  
  Supported formats include WAV and MP3 for analysis and spectrogram generation

## Installation

Install the library using pip:

```sh
pip install bhargava_swara
```

## Dependencies

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

## Spectrogram Generation

Generate various types of spectrograms to visualize different aspects of audio frequency content over time.

### Mel-Frequency Spectrogram
```python
from bhargava_swara import generate_mel_spectrogram

audio = "path/to/audio.wav"
output = "path/to/output_mel_spectrogram.png"
generate_mel_spectrogram(audio, output, n_mels=128, fmax=8000)
print("Mel spectrogram generated successfully!")
```

#### Spectrogram Parameters:
- `audio`: Path to the input audio file (e.g., WAV or MP3)
- `output`: Path to save the PNG file
- Mel-specific: `n_mels` (default: 128), `fmax` (default: 8000)

## Music Analysis

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

### Raga Analysis
```python
from bhargava_swara import analyze_raga

api_key = "YOUR_API_KEY"
audio = "path/to/audio.wav"
result = analyze_raga(audio, api_key)
print(f"Raga: {result}")
```

### Tala Analysis
```python
from bhargava_swara import analyze_tala

api_key = "YOUR_API_KEY"
audio = "path/to/audio.wav"
result = analyze_tala(audio, api_key)
print(f"Tala: {result}")
```

### Tempo Analysis
```python
from bhargava_swara import analyze_tempo

api_key = "YOUR_API_KEY"
audio = "path/to/audio.wav"
result = analyze_tempo(audio, api_key)
print(f"Tempo: {result}")
```

### Tradition Analysis
```python
from bhargava_swara import analyze_tradition

api_key = "YOUR_API_KEY"
audio = "path/to/audio.wav"
result = analyze_tradition(audio, api_key)
print(f"Tradition: {result}")
```

### Ornament Analysis
```python
from bhargava_swara import analyze_ornaments

api_key = "YOUR_API_KEY"
audio = "path/to/audio.wav"
result = analyze_ornaments(audio, api_key)
print(f"Ornaments: {result}")
```

### Full Music Analysis
```python
from bhargava_swara import analyze_music_full

api_key = "YOUR_API_KEY"
audio = "path/to/audio.wav"
result = analyze_music_full(audio, api_key)
print(f"Full Analysis:\n{result}")
```

## Music Synthesis

Create Indian classical music elements with ease.

### New Synthesis Capabilities
In this release, weâ€™ve added advanced synthesis capabilities to generate music based on analyzed audio input. Users can now synthesize melodies, rhythms, and drones using traditional Indian instruments.

#### Usage Example
```python
from bhargava_swara import AudioAnalyzer, InstrumentSynthesizer

# Analyze an audio file
analyzer = AudioAnalyzer()
analysis = analyzer.analyze_song("input.wav")

# Synthesize using sitar, tabla, and tanpura
synthesizer = InstrumentSynthesizer()
melody = synthesizer.synthesize_melody(analysis["melody"], instrument_name="sitar")
tabla = synthesizer.generate_tabla_rhythm(analysis["rhythm"])
tanpura = synthesizer.generate_tanpura(analysis["raga"], duration=2.0)
mixed = synthesizer.mix_outputs(melody, tabla, tanpura, output_path="output.wav")
print("Synthesis completed! Output saved to output.wav")
```

##### Parameters:
- `instrument_name`: Choose from "sitar", "veena", or "flute" for melody synthesis.
- `output_path`: Path to save the synthesized audio file (optional).
- `duration`: Duration of the tanpura drone in seconds (default: 2.0).

#### Command-Line Example
An example script (`main.py`) is provided in the repository for quick testing:
```bash
git clone https://github.com/yourusername/bhargava_swara.git
cd bhargava_swara
python main.py input.wav output.wav --instrument sitar --debug
```
- `--instrument`: Specify the instrument (default: "sitar").
- `--debug`: Save intermediate files for debugging (e.g., melody, tabla, tanpura).
- 
  **Note**: Ensure `ffmpeg` is installed for audio processing. On macOS/Linux:
  - `brew install ffmpeg` (macOS)
  - `sudo apt-get install ffmpeg` (Ubuntu)
  On Windows, download from [ffmpeg.org](https://ffmpeg.org/download.html) and add to PATH.

## Future Development

We plan to enhance Bhargava Swara with the following features:

### Spectrogram Enhancements
- **Chroma Spectrogram**: Add support for visualizing pitch class distributions over time.
- **CQT Spectrogram**: Implement constant-Q transform spectrograms for better frequency resolution.
- **Cent Filterbank Spectrogram**: Generate spectrograms using cent-scaled filterbanks for microtonal analysis.

### Analysis Features
- **Automatic Raga Recognition**: Develop algorithms to identify ragas automatically from audio input.
- **Tonic Identification**: Detect the tonic (base pitch) of a performance for accurate analysis.
- **Pitch Extraction**: Extract pitch contours from audio to analyze melodic structure.
- **Rhythm Analysis**: Analyze meter, rhythmic patterns, and structure in talas.

### Synthesis Tools
- **Tala Generation**: Create rhythmic cycles (talas) programmatically for practice or composition.
- **Generate Music Based on Raga**: Synthesize music adhering to a specific raga's rules.
- **Music Imitation**: Generate music imitating the style of a given audio input.

Contributions and suggestions for these features are welcome! Please submit ideas or pull requests to the [GitHub repository](https://github.com/Shouryaanga-Tribe/BhargavaSwaraLibrary).

## 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

0.0.8 (06/04/2025)
-------------------
- 3 more spectogram generations added. README.md file updated

0.0.9 (06/04/2025)
-------------------
- fixed bugs

0.0.10 to 0.0.12 (18/04/2025)
-------------------
- added tanpura droid generation

0.0.13 (18/04/2025)
-------------------
- removed spectograms

0.0.15 (28/04/2025)
-------------------
- Added music synthesis
