https://github.com/fabriziosalmi/audiogen
AI-Powered Sound Generation & Classification.
https://github.com/fabriziosalmi/audiogen
automation classification generation machine-learning sounds
Last synced: 6 months ago
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AI-Powered Sound Generation & Classification.
- Host: GitHub
- URL: https://github.com/fabriziosalmi/audiogen
- Owner: fabriziosalmi
- Created: 2023-11-28T17:12:35.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-02-26T20:11:14.000Z (8 months ago)
- Last Synced: 2025-02-26T21:22:26.260Z (8 months ago)
- Topics: automation, classification, generation, machine-learning, sounds
- Language: Python
- Homepage:
- Size: 48.8 KB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🎵 AudioGen: AI-Powered Sound Generation & Classification 🎶
[](https://www.python.org/)
[](https://opensource.org/licenses/MIT)
[](CONTRIBUTING.md)
## 🌟 Overview
**AudioGen** is a powerful and versatile tool that leverages AI and machine learning to generate, classify, and analyze audio. Whether you're a musician looking for unique soundscapes, a sound engineer needing efficient audio classification, or a developer wanting to integrate audio intelligence into your applications, AudioGen provides an intuitive and customizable platform to bring your sonic visions to life. Imagine creating custom sound effects for a video game, automatically tagging audio files in a large library, or even generating entirely new musical compositions – AudioGen makes it possible.
## ✨ Key Features
* 🎤 **Sound Generation:** Generate diverse and original sounds using pre-trained models or your own custom-trained models. Create everything from realistic ambient noises to synthesized instrument sounds, all from Python or a command-line interface.
* 🎚️ **Audio Classification:** Automatically categorize audio files into predefined sound types (e.g., "speech," "music," "nature sounds"). Leverage pre-trained models for immediate use or train your own classifiers for specialized sound recognition tasks.
* 🧠 **Custom Model Training:** Train your own sound classification models using extracted audio features and your own datasets. Fine-tune models to achieve optimal accuracy for your specific audio needs.
* 📊 **Advanced Feature Extraction:** Extract a wide range of audio features (e.g., Chroma STFT, Root Mean Square Energy (RMSE), Spectral Centroid, MFCCs) to gain deeper insights into your audio data.
* 📂 **Data Preparation Tools:** Organize, tag, and prepare audio datasets for efficient model training and evaluation. Utilities for splitting audio files, converting formats, and creating balanced datasets.
* 🔧 **Customizable Pipelines:** Tailor every step of the audio processing workflow, from feature extraction parameters to model architectures. Experiment with different configurations to optimize performance and create truly unique audio experiences.
* 🔌 **Seamless Integration:** Easily integrate AudioGen's capabilities into your existing Python applications, web services, or audio processing pipelines.
## 🛠️ Installation
1. **Clone the Repository:**
```bash
git clone https://github.com/fabriziosalmi/audiogen.git
cd audiogen
```
2. **Create a Virtual Environment (Recommended):**
```bash
python3 -m venv venv
source venv/bin/activate # or venv\Scripts\activate on Windows
```
3. **Install Dependencies:**
```bash
pip install -r requirements.txt
```
## 🚀 Usage
### 🔊 Generate Sounds
#### Command Line Interface (CLI)
Generate a "synth" sound using the CLI:
```bash
python generate_sound_cli.py --sound_type synth --output synth_sound.wav
```
This will generate a sound and save it as `synth_sound.wav`. Check the documentation for supported sound types and other options.
#### Python Script
Integrate sound generation directly into your Python code:
```python
from audiogen.sound_generation import generate_sound # Adjust import based on your project structure
# Example usage (replace with your actual model and features)
model = load_your_trained_model() # Replace with your model loading code
input_features = {'frequency': 440, 'duration': 2} # Sample features (adjust as needed)
sound = generate_sound(model, input_features)
# Save the generated sound (example using a library like soundfile)
import soundfile as sf
sf.write('generated_sound.wav', sound, samplerate=44100)
print("Sound generated and saved as generated_sound.wav")
```
**Important:** The `sound_generation` module and its related functions are placeholders. You'll need to adapt the code based on your specific implementation.
### 🏋️♂️ Train a Model
1. **Prepare Your Audio Data:** Organize your audio files into folders representing different classes (e.g., `dogs`, `cats`, `birds`).
2. **Extract Features:**
```python
from audiogen.feature_extraction import extract_features # Adjust import based on your project structure
features = extract_features('path/to/audio/dog_bark.wav')
print(features) # Print the extracted features (e.g., as a dictionary)
```
This will output a dictionary containing the extracted audio features for the provided audio file.
3. **Train the Model:**
```python
from audiogen.model_training import train_model, load_features # Adjust import based on your project structure
import pandas as pd
# Assuming you have extracted features and saved them in a CSV file
features_df = pd.read_csv('extracted_features.csv') # Use pandas for loading
# Assuming your CSV has a 'label' column for audio class
model = train_model(features_df, label_column='label')
# Save the trained model
import joblib
joblib.dump(model, 'trained_sound_classifier.pkl')
print("Model trained and saved as trained_sound_classifier.pkl")
```
**Important:** The `model_training` module and its related functions are placeholders. Implement your training logic using libraries like scikit-learn, TensorFlow, or PyTorch. The example uses Pandas to load a CSV. Adjust this based on how you manage your extracted features.
### 📊 Extract Features
```python
from audiogen.feature_extraction import extract_features # Adjust import based on your project structure
features = extract_features('path/to/audio/sample.wav')
print(features)
```
This will print a dictionary or other data structure containing the extracted features. Refer to the documentation for a list of available features and their meanings.
### 📂 Data Preparation
```python
from audiogen.data_preparation import tag_sounds # Adjust import based on your project structure
tag_sounds('input/audio_folder', 'output/labeled_audio', 'audio_tags.json')
```
This function (and the module itself) needs to be implemented based on your desired data preparation strategy. Consider using libraries like `pydub` for audio manipulation.
## 📚 Documentation
Comprehensive documentation is available in the `docs/` directory. This includes:
* API reference
* Tutorials
* Examples
* Troubleshooting guide
## 🤝 Contributing
We welcome contributions of all kinds! Please see our [contributing guidelines](CONTRIBUTING.md) for details on how to get involved. Areas for contribution include:
* Adding new sound generation models
* Improving existing audio classifiers
* Developing new audio features
* Writing documentation
* Creating example scripts
* Fixing bugs
## 📜 License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.