python 如何提取视频中的音频再保存到 内存

Question

我想提取视频中的音频再保存到内存[python]

目前最相似的方案是moviepy 库但是这只能提取音频并保存到本地
我确实能够先保存再用其他的音频库读取，但由于这会让运行效率低下python雪上加霜，再加上本人精神洁癖的缘故坚决不使用这种方法

所以，有没有什么方法能解决我的问题呢

这里是我期望的输入和输出

input = videopath.mp4
# 然后output记录音频的波形数据。。。。
output = nparray #最好是这个
output = list # 这个也行
output = tensor # 这个也行

是保存到内存，而非本地。也不是保存，然后加载

Answer 1

https://github.com/JyzjYzjyZ/Format-conversion-of-in-memory-audio

如何使用和具体说明去看链接，有用的话给原作者star

import soundfile,librosa,torch, copy
import numpy as np
import scipy.interpolate as interpolate
import torchaudio,wave,torchvision #build时祈福求平安

'''
author:goodjin5
date:2022.3.16 --> 2022.3.19
-v 1.0.2 --> 1.0.3
=====================================

'''

def resample(sound,orig_sr,target_sr=16000,resample_type:str=None):
    '''
    :param sound: numpy-float64 maybe can run in float 32
    :param orig_sr: 原始的采样率
    :param target_sr: 目标采样率，与原相同或为0时直接返回
    :param resample_type: 采用类型，默认自动分配fast或者best,可以强制指定 使用 'best',  'kaiser_best','fast','kaiser_fast'
    :return: 采样后的nparray
    '''
    if orig_sr == target_sr or target_sr==0:
        return sound,orig_sr


    if resample_type in ['best',  'kaiser_best','fast','kaiser_fast']:
        restype = ['kaiser_best','kaiser_best','kaiser_fast','kaiser_fast'][['best', 'kaiser_best','fast', 'kaiser_fast'].index(resample_type)]
    else:
        restype = ('kaiser_fast' if target_sr <= orig_sr else 'kaiser_best')
    if __name__=='__main__':
        print(resample_type,restype)

    sound = librosa.resample(y=sound,orig_sr=orig_sr,target_sr=target_sr,res_type=restype)
    return sound,target_sr


def read(path:str,sr:int=16000,mono:bool=True):
    '''
    支持音视频格式，所以ffmpeg支持的格式都可以
    :param path: 音频路径
    :param sr: 采样率 为0时采用原始的采样率
    :param mono: 启用单声道
    :return: numpy-float64
    '''
    try:
        sound,orig_sr = soundfile.read(path,dtype='float64')
        sound=sound.T
    except:
        sound, orig_sr = librosa.load(path=path,dtype=np.float64,res_type='kaiser_fast')
    # to mono,and resample sr
    if mono:
        sound = librosa.to_mono(sound)
    # if sr == 0 or target_sr == orig_sr:
    #     # 无需采样
    #     return sound,orig_sr
    # sound = librosa.resample(y=sound,orig_sr=orig_sr,target_sr=target_sr,
    #                          res_type=('kaiser_fast' if target_sr <= orig_sr else 'kaiser_best'))
    sound,s = resample(sound,orig_sr,sr)
    return sound,s




def toWave(sound):
    '''
    :param sound: numpy-float64
    :return: waveFile -bytes
    '''
    # 32767
    return (sound * 32767).astype('int16').tobytes()


def toTorch(sound):
    '''
    :param sound: numpy-float64
    :return: tensor-float32
    '''
    sound = sound.astype('float32')
    t = torch.from_numpy(copy.deepcopy(sound))
    # 扩展维度，维度转换
    return t.unsqueeze(1).permute(1, 0)


def toNumpy_int16(sound):
    return (sound * 32767).astype('int16')


def torchToNumpy_float(sound):
    '''
    :param sound: tensor
    :return: numpy_float float是16还是32由输入的tensor决定
    '''
    # 将所有shape为1的维度降维
    return sound.squeeze().numpy()


def resamplrBylen(orig_list:list,target_len:int):
    '''
    同于标准重采样，此函数将len(list1)=x 从采样为len(list2)=y；y为指定的值，list2为输出
    :param orig_list: 是list,重采样的源列表：list1
    :param target_len: 重采样的帧数：y
    :return: 重采样后的数组:list2
    '''
    orig_list_len =len(orig_list)
    k = target_len/orig_list_len
    x = [x*k for x in range(orig_list_len)]
    x[-1] = 3572740
    if x[-1]!=target_len:
        # 线性更改越界结尾
        x1=x[-2];y1=orig_list[-2];x2=x[-1];y2=orig_list[-1]
        y_resa = (y2 - y1) * (target_len - x1) / (x2 - x1) + y1
        x[-1] = target_len;orig_list[-1]=y_resa
    # 使用了线性的插值方法，也可以根据需要改成其他的。推荐是线性
    f = interpolate.interp1d(x,orig_list,'linear')
    del x
    resample_list = f([x for x in range(target_len)])
    return resample_list


if __name__=='__main__':
    # exaxmple
    # You can test performance and usability here
    # change 'AUDIO_PATH.wav' or 'Video_path.*' all format[ffmpeg]
    path = 'AUDIO_PATH.wav'
    path = r'D:\setup\wav\130.wav'
    import matplotlib.pyplot as plt
    numpy_float64,sr = read(path,0,True)
    # 把target设置的过低会出现不能除以0的错误
    res_low,sr = resample(numpy_float64,sr,target_sr=100,resample_type='kaiser_best')
    # resample(numpy_float64,sr,50000,'kaiser_best')
    wave = toWave(numpy_float64)
    tensor = toTorch(numpy_float64)
    numpy_int16 = toNumpy_int16(numpy_float64)
    resamplr_bylen = resamplrBylen(numpy_float64.tolist(),5000)
    plt.figure(figsize=(1000, 50), dpi=80)
    plt.subplot(111)
    plt.plot(resamplr_bylen, color='red',linestyle="-",alpha=1)
    # plt.plot(numpy_float64.tolist(), color='green',linestyle="-",alpha=1)
    plt.plot(res_low.tolist(), color='blue',linestyle="-",alpha=1)
    plt.text(5, 0.4, 'InAudio,max{0},min{1}\nred:By len\nblue:By sr'
             .format(max(numpy_float64.tolist()),min(numpy_float64.tolist())))
    plt.show()
    print('Passing the test')