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audio-normalizer by dkyazzentwatwa/chatgpt-skills
npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill audio-normalizer使用峰值或 RMS 标准化来统一音频音量电平,确保不同文件间的响度一致。
音量标准化适用于:
from audio_normalizer import AudioNormalizer
# 峰值标准化至 -1 dBFS
normalizer = AudioNormalizer()
normalizer.load('input.mp3')
normalizer.normalize_peak(target_dbfs=-1.0)
normalizer.save('normalized.mp3')
# RMS 标准化以获得一致的平均响度
normalizer.normalize_rms(target_dbfs=-20.0)
normalizer.save('normalized_rms.mp3')
# 批量标准化所有文件至相同电平
normalizer.batch_normalize(
input_files=['audio1.mp3', 'audio2.mp3'],
output_dir='normalized/',
method='rms',
target_dbfs=-20.0
)
# 峰值标准化
python audio_normalizer.py input.mp3 --output normalized.mp3 --method peak --target -1.0
# RMS 标准化
python audio_normalizer.py input.mp3 --output normalized.mp3 --method rms --target -20.0
# 批量标准化目录
python audio_normalizer.py *.mp3 --output-dir normalized/ --method rms --target -20.0
# 仅分析当前电平,不进行标准化
python audio_normalizer.py input.mp3 --analyze-only
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class AudioNormalizer:
def load(self, filepath: str) -> 'AudioNormalizer'
def normalize_peak(self, target_dbfs: float = -1.0, headroom: float = 0.1) -> 'AudioNormalizer'
def normalize_rms(self, target_dbfs: float = -20.0) -> 'AudioNormalizer'
def analyze_levels(self) -> Dict[str, float]
def save(self, output: str, format: str = None, bitrate: str = '192k') -> str
def batch_normalize(self, input_files: List[str], output_dir: str,
method: str = 'rms', target_dbfs: float = -20.0) -> List[str]
对于播客:
normalizer.normalize_rms(target_dbfs=-19.0) # 语音清晰度
对于音乐:
normalizer.normalize_peak(target_dbfs=-1.0) # 保留动态
对于广播:
normalizer.normalize_rms(target_dbfs=-23.0) # 符合 EBU R128 标准
每周安装量
39
代码仓库
GitHub 星标数
23
首次出现
2026年1月24日
安全审计
安装于
opencode31
gemini-cli31
codex29
claude-code27
cursor27
github-copilot27
Normalize audio volume levels using peak or RMS normalization to ensure consistent loudness across files.
Volume normalization for:
from audio_normalizer import AudioNormalizer
# Peak normalization to -1 dBFS
normalizer = AudioNormalizer()
normalizer.load('input.mp3')
normalizer.normalize_peak(target_dbfs=-1.0)
normalizer.save('normalized.mp3')
# RMS normalization for consistent average loudness
normalizer.normalize_rms(target_dbfs=-20.0)
normalizer.save('normalized_rms.mp3')
# Batch normalize all files to same level
normalizer.batch_normalize(
input_files=['audio1.mp3', 'audio2.mp3'],
output_dir='normalized/',
method='rms',
target_dbfs=-20.0
)
# Peak normalization
python audio_normalizer.py input.mp3 --output normalized.mp3 --method peak --target -1.0
# RMS normalization
python audio_normalizer.py input.mp3 --output normalized.mp3 --method rms --target -20.0
# Batch normalize directory
python audio_normalizer.py *.mp3 --output-dir normalized/ --method rms --target -20.0
# Show current levels without normalizing
python audio_normalizer.py input.mp3 --analyze-only
class AudioNormalizer:
def load(self, filepath: str) -> 'AudioNormalizer'
def normalize_peak(self, target_dbfs: float = -1.0, headroom: float = 0.1) -> 'AudioNormalizer'
def normalize_rms(self, target_dbfs: float = -20.0) -> 'AudioNormalizer'
def analyze_levels(self) -> Dict[str, float]
def save(self, output: str, format: str = None, bitrate: str = '192k') -> str
def batch_normalize(self, input_files: List[str], output_dir: str,
method: str = 'rms', target_dbfs: float = -20.0) -> List[str]
For Podcasts:
normalizer.normalize_rms(target_dbfs=-19.0) # Speech clarity
For Music:
normalizer.normalize_peak(target_dbfs=-1.0) # Preserve dynamics
For Broadcast:
normalizer.normalize_rms(target_dbfs=-23.0) # EBU R128 compliance
Weekly Installs
39
Repository
GitHub Stars
23
First Seen
Jan 24, 2026
Security Audits
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Installed on
opencode31
gemini-cli31
codex29
claude-code27
cursor27
github-copilot27
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