wavx 0.1.5

音频分析和处理工具库

WavX

WavX is a Python library for audio analysis and processing, providing a simple yet powerful API for handling various audio-related tasks.

Features

• Audio file analysis, including amplitude, loudness, and acoustic parameter measurement
• Audio processing, including RMS level normalization
• Modular design, easy to extend
• Clean and intuitive API interface

Installation

pip install wavx

Quick Start

Analyze audio file amplitude information

import wavx

# Analyze audio file and get amplitude information
amplitude_info = wavx.analysis.amplitude.analyze_amplitude("your_audio_file.wav")

# Print all amplitude information
wavx.analysis.amplitude.print_amplitude_info(amplitude_info)

# Or get specific information
print(f"Peak amplitude: {amplitude_info['peak_amplitude']} dB")
print(f"Total RMS amplitude: {amplitude_info['total_rms_amplitude']} dB")

Generate and display audio waveform

import wavx

# Simple way: Display waveform with one function call
wavx.analysis.waveform.display_waveform("your_audio_file.wav")

# Advanced way: Step by step for more control
# 1. Analyze waveform data
waveform_data = wavx.analysis.waveform.analyze_waveform(
    audio_file="your_audio_file.wav",
    channel=0  # 0=left channel, 1=right channel
)

# 2. Print waveform information
wavx.analysis.waveform.print_waveform_info(waveform_data)

# 3. Plot waveform with custom settings
import matplotlib.pyplot as plt
fig = wavx.analysis.waveform.plot_waveform(
    waveform_data=waveform_data,
    figsize=(12, 4),     # figure size
    save_path="waveform.png"  # save to file
)
plt.show()

Generate and display audio spectrogram

import wavx

# Simple way: Display spectrogram with one function call
wavx.analysis.spectrogram.display_spectrogram("your_audio_file.wav")

# Advanced way: Step by step for more control
# 1. Analyze spectrogram data
spec_data = wavx.analysis.spectrogram.analyze_spectrogram(
    audio_file="your_audio_file.wav",
    channel=0,  # 0=left channel, 1=right channel
    window_size=None,  # auto window size
    overlap=0.75  # 75% window overlap
)

# 2. Print spectrogram information
wavx.analysis.spectrogram.print_spectrogram_info(spec_data)

# 3. Plot spectrogram with custom settings
import matplotlib.pyplot as plt
fig = wavx.analysis.spectrogram.plot_spectrogram(
    spec_data=spec_data,
    use_log_scale=True,  # use dB scale
    freq_limit=8000,     # limit to 8kHz
    figsize=(12, 4),     # figure size
    save_path="spectrogram.png"  # save to file
)
plt.show()

Normalize audio file to target RMS level

import wavx

# Normalize audio file to -20 dB FS
result = wavx.processing.normalization.normalize_to_target(
    input_file="input.wav",
    output_file="output.wav",
    target_rms_db=-20.0,
    reference_type="square"  # or "sine"
)

# Print normalization information
wavx.processing.normalization.print_normalization_info(result)

Project Structure

wavx/
├── docs/
│   ├── amplitude/
│   │   └── amplitude_analysis.md  # Amplitude analysis documentation
│   ├── analysis/
│   │   └── spectrogram.md         # Spectrogram analysis documentation
│   └── processing/
│       └── normalization.md       # RMS normalization documentation
├── examples/
│   ├── analyze_audio.py           # Example of audio analysis
│   ├── display_spectrogram.py     # Example of spectrogram visualization
│   └── normalize_audio.py         # Example of audio normalization
├── wavx/
│   ├── __init__.py                # Package initialization
│   ├── cli.py                     # Command line interface
│   ├── analysis/
│   │   ├── __init__.py            # Analysis module initialization
│   │   ├── amplitude.py           # Amplitude analysis functionality
│   │   └── spectrogram.py         # Spectrogram analysis functionality
│   ├── processing/
│   │   ├── __init__.py            # Processing module initialization
│   │   └── normalization.py       # RMS normalization functionality
│   ├── tests/
│   │   ├── __init__.py            # Tests initialization
│   │   ├── test_amplitude.py      # Amplitude analysis tests
│   │   └── test_normalization.py  # Normalization tests
│   └── utils/
│       └── __init__.py            # Utilities module initialization
├── README.md                      # English documentation
├── README_zh.md                   # Chinese documentation
├── requirements.txt               # Project dependencies
└── setup.py                       # Package installation config

Command Line Usage

After installation, you can use WavX from the command line:

# Basic amplitude analysis
wavx amplitude path/to/audio.wav

# Generate and display waveform
wavx waveform path/to/audio.wav

# Waveform with custom parameters
wavx waveform path/to/audio.wav --channel 1 --save output.png

# Generate and display spectrogram
wavx spectrogram path/to/audio.wav

# Spectrogram with custom parameters
wavx spectrogram path/to/audio.wav --channel 1 --freq-limit 5000 --save output.png

# RMS normalization
wavx normalize input.wav output.wav --target -18.0

# With custom reference signal
wavx normalize input.wav output.wav --reference sine --freq 500

Future Extensions

The modular design allows easy extensions:

1. More Analysis Functions:

   • Spectrum analysis
   • Harmonic analysis
   • Reverb and spatial analysis

2. More Audio Processing:

   • Equalization
   • Noise reduction
   • Dynamic range compression
   • Resampling and format conversion

3. Visualization:

   • Waveform display
   • Spectrogram
   • Loudness/RMS history

Release Notes

• v0.1.0 (2025-03-20): Initial release with amplitude analysis functionality
• v0.1.1 (2025-03-20): Added docs directory and bilingual README files
• v0.1.2 (2025-03-20): Added RMS normalization functionality
• v0.1.3 (2025-03-20): Added WAVX LOGO display after pip install
• v0.1.4 (2025-03-21): Added spectrogram analysis and visualization
• v0.1.5 (2025-03-22): Added waveform visualization functionality

Contributing

Contributions to the code, questions, or suggestions are welcome!

License

MIT License