“Torch speaker”版本间的差异

git clone 
cd torch_speaker
pip install -r requirements.txt
python setup.py develop

• 预加重(Pre-emphasis，弥补了高频部分的损耗，保护声道信息）
• 加窗（hamming窗，降低吉布斯现象）并做短时傅里叶变换(stft)
• 对stft后的复数结果取模
• 取对数（加1e-9防止出现0）
• Instance Norm(可以等价于做了cmvn倒谱均值方差归一化)

• 预加重(Pre-emphasis，弥补了高频部分的损耗，保护声道信息）
• 加窗（hamming窗，降低吉布斯现象）并做短时傅里叶变换(stft)
• 对stft后的复数结果取模
• 取对数（加1e-9防止出现0）
• Mel滤波
• Instance Norm(可以等价于做了cmvn倒谱均值方差归一化)

目前各类开源的工具中，语音数据的读取的方法实现主要有两种：

• 一种是以matlab，soundfile为代表的
• 一种是以kaldi，scipy为代表的

在本项目中，根据training和evaluation阶段的不同，对语音的读取策略也有所区别。

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