如何使用时间戳注释标记和提取音频

如何解决如何使用时间戳注释标记和提取音频

我想标记然后提取音频文件(audio.wav)的某些片段。段的开始和结束时间由DateTimeStamp(第一列)和动作持续时间(以毫秒为单位)(第三列)在另一个文件(注释文件(annot.csv))中给出:

DateTimeStamp           Action  Duration of action in milliseconds
04/16/20 21:25:36:241   A       502
04/16/20 21:25:36:317   B       2253
04/16/20 21:25:36:734   X       118
04/16/20 21:25:36:837   C       10
04/16/20 21:25:37:537   D       797
04/16/20 21:25:37:606   X       70
04/16/20 21:25:37:874   A       1506
.                       .       .

audio.wav文件在文件annot.csv的第一个DateTimeStamp时间开始。我该如何使用annot.csv文件中的信息来标记和提取audio.wav文件中的某个片段(例如,对应于Action X)?

我尝试使用librosa和pyAudioAnalysis软件包解决它,但找不到所需的信息。 任何帮助表示赞赏。

解决方法

这里的关键是计算每个指定段的开始和结束(在音频样本索引中)。

这可以通过先将毫秒转换为秒,然后再乘以音频的采样率来采样索引来完成。

但是通常,我建议在处理诸如此类的时间序列时使用Pandas datetime和timedelta功能。下面是实现此目的的一些示例代码:

import io

import pandas
import numpy
import librosa


def read_data(f,date_format):
    df = pandas.read_csv(f,sep=',')

    # Use proper pandas datatypes
    df['Time'] = pandas.to_datetime(df['DateTimeStamp'],format=date_format)
    df['Duration'] = pandas.to_timedelta(df['Duration ms'],unit='ms')
    df = df.drop(columns=['DateTimeStamp','Duration ms'])

    # Compute start and end time of each segment
    # audio starts at time of first segment
    first = df['Time'].iloc[0]
    df['Start'] = df['Time'] - first
    df['End'] = df['Start'] + df['Duration']

    return df

def extract_segments(y,sr,segments):
    # compute segment regions in number of samples
    starts = numpy.floor(segments.Start.dt.total_seconds() * sr).astype(int)
    ends = numpy.ceil(segments.End.dt.total_seconds() * sr).astype(int)

    # slice the audio into segments
    for start,end in zip(starts,ends):
        audio_seg = y[start:end]
        print('extracting audio segment:',len(audio_seg),'samples')

## Reproducible example
data = io.StringIO("""DateTimeStamp,Action,Duration ms
04/16/20 21:25:36:241,A,502
04/16/20 21:25:36:317,B,2253
04/16/20 21:25:36:734,X,118
04/16/20 21:25:36:837,C,10
04/16/20 21:25:37:537,D,797
04/16/20 21:25:37:606,70
04/16/20 21:25:37:874,1506
""")

segments = read_data(data,date_format="%m/%d/%y %H:%M:%S:%f")
print(segments)

path = librosa.util.example_audio_file()
y,sr = librosa.load(path,sr=16000,duration=10)
extract_segments(y,segments)

应该输出类似

 Action                    Time        Duration           Start             End
0      A 2020-04-16 21:25:36.241 00:00:00.502000        00:00:00 00:00:00.502000
1      B 2020-04-16 21:25:36.317 00:00:02.253000 00:00:00.076000 00:00:02.329000
2      X 2020-04-16 21:25:36.734 00:00:00.118000 00:00:00.493000 00:00:00.611000
3      C 2020-04-16 21:25:36.837 00:00:00.010000 00:00:00.596000 00:00:00.606000
4      D 2020-04-16 21:25:37.537 00:00:00.797000 00:00:01.296000 00:00:02.093000
5      X 2020-04-16 21:25:37.606 00:00:00.070000 00:00:01.365000 00:00:01.435000
6      A 2020-04-16 21:25:37.874 00:00:01.506000 00:00:01.633000 00:00:03.139000
extracting audio segment: 8032 samples
extracting audio segment: 36048 samples
extracting audio segment: 1888 samples
extracting audio segment: 160 samples
extracting audio segment: 12752 samples
extracting audio segment: 1120 samples
extracting audio segment: 24097 samples
,
import io
import pandas
import numpy as np
import librosa
import soundfile as sf
def read_data(annot,date_format):
  df = pandas.read_csv(annot,')

  # Use proper pandas datatypes
  df['Time'] = pandas.to_datetime(df['DateTime'],format=date_format)
  df['Duration'] = pandas.to_timedelta(df['Duration ms'],unit='ms')
  df = df.drop(columns=['DateTime','Duration ms'])

  # Compute start and end time of each segment
  # audio starts at time of first segment
  first = df['Time'].iloc[0]
  df['Start'] = df['Time'] - first
  df['End'] = df['Start'] + df['Duration']

  return df

def extract_segments(y,segments):
  # compute segment regions in number of samples
  starts = np.floor(segments.Start.dt.total_seconds() * sr).astype(int)
  ends = np.ceil(segments.End.dt.total_seconds() * sr).astype(int)

  # slice the audio into segments
  i = 0
  for start,ends):
    audio_seg = y[start:end]
    print('extracting audio segment:','samples')
    
    # file name string
    # it takes 5 first character of Action
    # and converts start and end time 
    file_name = str(segments.Activity[i][:5]) + \
    '__' + \
    str(segments.Start[i]).split('s ')[1].replace(':','_') + \
    '__' + \
    str(segments.End[i]).split('s ')[1].replace(':','_') + ".wav"
    
    sf.write(file_name,audio_seg,sr)
    i += 1
segments = read_data("annot.csv",date_format="%m/%d/%y %H:%M:%S:%f")
segments

y,sr = librosa.load("audio.wav",duration=2027)
extract_segments(y,segments)

版权声明:本文内容由互联网用户自发贡献,该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容, 请发送邮件至 dio@foxmail.com 举报,一经查实,本站将立刻删除。

相关推荐


依赖报错 idea导入项目后依赖报错,解决方案:https://blog.csdn.net/weixin_42420249/article/details/81191861 依赖版本报错:更换其他版本 无法下载依赖可参考:https://blog.csdn.net/weixin_42628809/a
错误1:代码生成器依赖和mybatis依赖冲突 启动项目时报错如下 2021-12-03 13:33:33.927 ERROR 7228 [ main] o.s.b.d.LoggingFailureAnalysisReporter : *************************** APPL
错误1:gradle项目控制台输出为乱码 # 解决方案:https://blog.csdn.net/weixin_43501566/article/details/112482302 # 在gradle-wrapper.properties 添加以下内容 org.gradle.jvmargs=-Df
错误还原:在查询的过程中,传入的workType为0时,该条件不起作用 <select id="xxx"> SELECT di.id, di.name, di.work_type, di.updated... <where> <if test=&qu
报错如下,gcc版本太低 ^ server.c:5346:31: 错误:‘struct redisServer’没有名为‘server_cpulist’的成员 redisSetCpuAffinity(server.server_cpulist); ^ server.c: 在函数‘hasActiveC
解决方案1 1、改项目中.idea/workspace.xml配置文件,增加dynamic.classpath参数 2、搜索PropertiesComponent,添加如下 <property name="dynamic.classpath" value="tru
删除根组件app.vue中的默认代码后报错:Module Error (from ./node_modules/eslint-loader/index.js): 解决方案:关闭ESlint代码检测,在项目根目录创建vue.config.js,在文件中添加 module.exports = { lin
查看spark默认的python版本 [root@master day27]# pyspark /home/software/spark-2.3.4-bin-hadoop2.7/conf/spark-env.sh: line 2: /usr/local/hadoop/bin/hadoop: No s
使用本地python环境可以成功执行 import pandas as pd import matplotlib.pyplot as plt # 设置字体 plt.rcParams['font.sans-serif'] = ['SimHei'] # 能正确显示负号 p
错误1:Request method ‘DELETE‘ not supported 错误还原:controller层有一个接口,访问该接口时报错:Request method ‘DELETE‘ not supported 错误原因:没有接收到前端传入的参数,修改为如下 参考 错误2:cannot r
错误1:启动docker镜像时报错:Error response from daemon: driver failed programming external connectivity on endpoint quirky_allen 解决方法:重启docker -> systemctl r
错误1:private field ‘xxx‘ is never assigned 按Altʾnter快捷键,选择第2项 参考:https://blog.csdn.net/shi_hong_fei_hei/article/details/88814070 错误2:启动时报错,不能找到主启动类 #
报错如下,通过源不能下载,最后警告pip需升级版本 Requirement already satisfied: pip in c:\users\ychen\appdata\local\programs\python\python310\lib\site-packages (22.0.4) Coll
错误1:maven打包报错 错误还原:使用maven打包项目时报错如下 [ERROR] Failed to execute goal org.apache.maven.plugins:maven-resources-plugin:3.2.0:resources (default-resources)
错误1:服务调用时报错 服务消费者模块assess通过openFeign调用服务提供者模块hires 如下为服务提供者模块hires的控制层接口 @RestController @RequestMapping("/hires") public class FeignControl
错误1:运行项目后报如下错误 解决方案 报错2:Failed to execute goal org.apache.maven.plugins:maven-compiler-plugin:3.8.1:compile (default-compile) on project sb 解决方案:在pom.
参考 错误原因 过滤器或拦截器在生效时,redisTemplate还没有注入 解决方案:在注入容器时就生效 @Component //项目运行时就注入Spring容器 public class RedisBean { @Resource private RedisTemplate<String
使用vite构建项目报错 C:\Users\ychen\work>npm init @vitejs/app @vitejs/create-app is deprecated, use npm init vite instead C:\Users\ychen\AppData\Local\npm-