如何在python3中将.wav文件转换为频谱图
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How to convert a .wav file to a spectrogram in python3
提问by Sreehari R
I am trying to create a spectrogram from a .wav file in python3.
我正在尝试从 python3 中的 .wav 文件创建频谱图。
I want the final saved image to look similar to this image:
我希望最终保存的图像看起来类似于此图像：
I have tried the following:
我尝试了以下方法：
This stack overflow post: Spectrogram of a wave file
此堆栈溢出帖子： 波形文件的频谱图
This post worked, somewhat. After running it, I got
这篇文章有点奏效。运行后，我得到了
However, This graph does not contain the colors that I need. I need a spectrogram that has colors. I tried to tinker with this code to try and add the colors however after spending significant time and effort on this, I couldn't figure it out!
但是，此图不包含我需要的颜色。我需要一个有颜色的光谱图。我尝试修改此代码以尝试添加颜色，但是在为此花费了大量时间和精力之后，我无法弄清楚！
I then tried thistutorial.
然后我尝试了这个教程。
This code crashed(on line 17) when I tried to run it with the error TypeError: 'numpy.float64' object cannot be interpreted as an integer.
当我尝试运行该代码时出现错误 TypeError: 'numpy.float64' object cannot be interpret as an integer 时，此代码崩溃（在第 17 行）。
line 17:
第 17 行：
samples = np.append(np.zeros(np.floor(frameSize/2.0)), sig)
I tried to fix it by casting
我试图通过铸造来修复它
samples = int(np.append(np.zeros(np.floor(frameSize/2.0)), sig))
and I also tried
我也试过
samples = np.append(np.zeros(int(np.floor(frameSize/2.0)), sig))
However neither of these worked in the end.
然而，这些最终都没有奏效。
I would really like to know how to convert my .wav files to spectrograms with color so that I can analyze them! Any help would be appreciated!!!!!
我真的很想知道如何将我的 .wav 文件转换为带有颜色的频谱图，以便我可以分析它们！任何帮助，将不胜感激！！！！！
Please tell me if you want me to provide any more information about my version of python, what I tried, or what I want to achieve.
如果您希望我提供有关我的 Python 版本、我尝试过的内容或我想要实现的内容的更多信息，请告诉我。
回答by Tom Wyllie
Use scipy.signal.spectrogram.
使用scipy.signal.spectrogram.
import matplotlib.pyplot as plt
from scipy import signal
from scipy.io import wavfile
sample_rate, samples = wavfile.read('path-to-mono-audio-file.wav')
frequencies, times, spectrogram = signal.spectrogram(samples, sample_rate)
plt.pcolormesh(times, frequencies, spectrogram)
plt.imshow(spectrogram)
plt.ylabel('Frequency [Hz]')
plt.xlabel('Time [sec]')
plt.show()
Be sure that your wav file is mono (single channel) and not stereo (dual channel) before trying to do this. I highly recommend reading the scipy documentation at https://docs.scipy.org/doc/scipy- 0.19.0/reference/generated/scipy.signal.spectrogram.html.
在尝试执行此操作之前，请确保您的 wav 文件是单声道（单声道）而不是立体声（双声道）。我强烈建议阅读https://docs.scipy.org/doc/scipy-0.19.0/reference/generated/scipy.signal.spectrogram.html 上的 scipy 文档。
Putting plt.pcolormeshbefore plt.imshowseems to fix some issues, as pointed out by @Davidjb, and if unpacking error occurs, follow the steps by @cgnorthcutt below.
正如@Davidjb 所指出的，放在plt.pcolormesh之前plt.imshow似乎解决了一些问题，如果发生解包错误，请按照下面@cgnorthcutt 的步骤操作。
回答by Mudit Verma
import os
import wave
import pylab
def graph_spectrogram(wav_file):
sound_info, frame_rate = get_wav_info(wav_file)
pylab.figure(num=None, figsize=(19, 12))
pylab.subplot(111)
pylab.title('spectrogram of %r' % wav_file)
pylab.specgram(sound_info, Fs=frame_rate)
pylab.savefig('spectrogram.png')
def get_wav_info(wav_file):
wav = wave.open(wav_file, 'r')
frames = wav.readframes(-1)
sound_info = pylab.fromstring(frames, 'int16')
frame_rate = wav.getframerate()
wav.close()
return sound_info, frame_rate
for A Capella Science - Bohemian Gravity!this gives:
对于卡佩拉科学 - 波西米亚重力！这给出：
Use graph_spectrogram(path_to_your_wav_file).
I don't remember the blog from where I took this snippet. I will add the link whenever I see it again.
使用graph_spectrogram(path_to_your_wav_file). 我不记得我从哪里获取这个片段的博客。每当我再次看到它时，我都会添加链接。
回答by Beginner
I have fixed the errors you are facing for http://www.frank-zalkow.de/en/code-snippets/create-audio-spectrograms-with-python.html
This implementation is better because you can change the binsize(e.g. binsize=2**8)
我已经修复了您在http://www.frank-zalkow.de/en/code-snippets/create-audio-spectrograms-with-python.html 中遇到的错误
此实现更好，因为您可以更改binsize（例如binsize=2**8）
import numpy as np
from matplotlib import pyplot as plt
import scipy.io.wavfile as wav
from numpy.lib import stride_tricks
""" short time fourier transform of audio signal """
def stft(sig, frameSize, overlapFac=0.5, window=np.hanning):
win = window(frameSize)
hopSize = int(frameSize - np.floor(overlapFac * frameSize))
# zeros at beginning (thus center of 1st window should be for sample nr. 0)
samples = np.append(np.zeros(int(np.floor(frameSize/2.0))), sig)
# cols for windowing
cols = np.ceil( (len(samples) - frameSize) / float(hopSize)) + 1
# zeros at end (thus samples can be fully covered by frames)
samples = np.append(samples, np.zeros(frameSize))
frames = stride_tricks.as_strided(samples, shape=(int(cols), frameSize), strides=(samples.strides[0]*hopSize, samples.strides[0])).copy()
frames *= win
return np.fft.rfft(frames)
""" scale frequency axis logarithmically """
def logscale_spec(spec, sr=44100, factor=20.):
timebins, freqbins = np.shape(spec)
scale = np.linspace(0, 1, freqbins) ** factor
scale *= (freqbins-1)/max(scale)
scale = np.unique(np.round(scale))
# create spectrogram with new freq bins
newspec = np.complex128(np.zeros([timebins, len(scale)]))
for i in range(0, len(scale)):
if i == len(scale)-1:
newspec[:,i] = np.sum(spec[:,int(scale[i]):], axis=1)
else:
newspec[:,i] = np.sum(spec[:,int(scale[i]):int(scale[i+1])], axis=1)
# list center freq of bins
allfreqs = np.abs(np.fft.fftfreq(freqbins*2, 1./sr)[:freqbins+1])
freqs = []
for i in range(0, len(scale)):
if i == len(scale)-1:
freqs += [np.mean(allfreqs[int(scale[i]):])]
else:
freqs += [np.mean(allfreqs[int(scale[i]):int(scale[i+1])])]
return newspec, freqs
""" plot spectrogram"""
def plotstft(audiopath, binsize=2**10, plotpath=None, colormap="jet"):
samplerate, samples = wav.read(audiopath)
s = stft(samples, binsize)
sshow, freq = logscale_spec(s, factor=1.0, sr=samplerate)
ims = 20.*np.log10(np.abs(sshow)/10e-6) # amplitude to decibel
timebins, freqbins = np.shape(ims)
print("timebins: ", timebins)
print("freqbins: ", freqbins)
plt.figure(figsize=(15, 7.5))
plt.imshow(np.transpose(ims), origin="lower", aspect="auto", cmap=colormap, interpolation="none")
plt.colorbar()
plt.xlabel("time (s)")
plt.ylabel("frequency (hz)")
plt.xlim([0, timebins-1])
plt.ylim([0, freqbins])
xlocs = np.float32(np.linspace(0, timebins-1, 5))
plt.xticks(xlocs, ["%.02f" % l for l in ((xlocs*len(samples)/timebins)+(0.5*binsize))/samplerate])
ylocs = np.int16(np.round(np.linspace(0, freqbins-1, 10)))
plt.yticks(ylocs, ["%.02f" % freq[i] for i in ylocs])
if plotpath:
plt.savefig(plotpath, bbox_inches="tight")
else:
plt.show()
plt.clf()
return ims
ims = plotstft(filepath)
