PaddleSpeech/paddlespeech/audiotools

Audiotools is a comprehensive toolkit designed for audio processing and analysis, providing robust solutions for audio signal processing, data management, model training, and evaluation.

Directory Structure

.
├── audiotools
│   ├── README.md
│   ├── __init__.py
│   ├── core
│   │   ├── __init__.py
│   │   ├── _julius.py
│   │   ├── audio_signal.py
│   │   ├── display.py
│   │   ├── dsp.py
│   │   ├── effects.py
│   │   ├── ffmpeg.py
│   │   ├── loudness.py
│   │   └── util.py
│   ├── data
│   │   ├── __init__.py
│   │   ├── datasets.py
│   │   ├── preprocess.py
│   │   └── transforms.py
│   ├── metrics
│   │   ├── __init__.py
│   │   └── quality.py
│   ├── ml
│   │   ├── __init__.py
│   │   ├── accelerator.py
│   │   ├── basemodel.py
│   │   └── decorators.py
│   ├── requirements.txt
│   └── post.py
├── tests
│   └── audiotools
│       ├── core
│       │   ├── test_audio_signal.py
│       │   ├── test_bands.py
│       │   ├── test_display.py
│       │   ├── test_dsp.py
│       │   ├── test_effects.py
│       │   ├── test_fftconv.py
│       │   ├── test_grad.py
│       │   ├── test_highpass.py
│       │   ├── test_loudness.py
│       │   ├── test_lowpass.py
│       │   └── test_util.py
│       ├── data
│       │   ├── test_datasets.py
│       │   ├── test_preprocess.py
│       │   └── test_transforms.py
│       ├── ml
│       │   ├── test_decorators.py
│       │   └── test_model.py
│       └── test_post.py

• core: Contains the core class AudioSignal, which is responsible for the fundamental representation and manipulation of audio signals.

• data: Primarily dedicated to storing and processing datasets, including classes and functions for data preprocessing, ensuring efficient loading and transformation of audio data.

• metrics: Implements functions for various audio evaluation metrics, enabling precise assessment of the performance of audio models and processing algorithms.

• ml: Comprises classes and methods related to model training, supporting the construction, training, and optimization of machine learning models in the context of audio.

This project aims to provide developers and researchers with an efficient and flexible framework to foster innovation and exploration across various domains of audio technology.