How to convert a .wav file to a spectrogram in python3

How to convert a .wav file to a spectrogram in python3

To convert a .wav file to a spectrogram in Python 3, you can use libraries like scipy, numpy, and matplotlib. Here's a basic example using these libraries:

import numpy as np import matplotlib.pyplot as plt from scipy.io import wavfile from scipy.signal import spectrogram # Load the .wav file sample_rate, audio_data = wavfile.read('audio_file.wav') # Compute the spectrogram frequencies, times, spectrogram_data = spectrogram(audio_data, fs=sample_rate) # Plot the spectrogram plt.pcolormesh(times, frequencies, 10 * np.log10(spectrogram_data)) plt.ylabel('Frequency (Hz)') plt.xlabel('Time (s)') plt.title('Spectrogram') plt.colorbar(label='Power/Frequency (dB/Hz)') plt.show()

Replace 'audio_file.wav' with the path to your .wav audio file. This code uses the wavfile.read function from scipy.io to load the audio data and its sample rate. The scipy.signal.spectrogram function is used to compute the spectrogram. Finally, matplotlib.pyplot is used to visualize the spectrogram.

Keep in mind that this is a basic example, and you can further customize the visualization and processing of the spectrogram based on your needs. You might also want to experiment with other parameters of the spectrogram function to fine-tune the spectrogram generation.

Examples

  1. "Python3 convert .wav to spectrogram example"

    • Description: This query is commonly used to find examples or tutorials on how to convert a .wav audio file to a spectrogram using Python3.
    • Code Implementation:
      import numpy as np import matplotlib.pyplot as plt from scipy.io import wavfile from scipy.signal import spectrogram # Load the .wav file sample_rate, audio_data = wavfile.read('your_audio_file.wav') # Generate the spectrogram frequencies, times, spectrogram_data = spectrogram(audio_data, sample_rate) # Plot the spectrogram plt.pcolormesh(times, frequencies, 10 * np.log10(spectrogram_data)) plt.ylabel('Frequency [Hz]') plt.xlabel('Time [sec]') plt.colorbar(label='Intensity [dB]') plt.show()

  2. "Python3 .wav file to spectrogram conversion"

    • Description: This query seeks information on how to convert a .wav file to a spectrogram using Python3.
    • Code Implementation:
      import numpy as np import matplotlib.pyplot as plt from scipy.io import wavfile from scipy.signal import spectrogram # Load the .wav file sample_rate, audio_data = wavfile.read('your_audio_file.wav') # Generate the spectrogram frequencies, times, spectrogram_data = spectrogram(audio_data, sample_rate) # Plot the spectrogram plt.pcolormesh(times, frequencies, 10 * np.log10(spectrogram_data)) plt.ylabel('Frequency [Hz]') plt.xlabel('Time [sec]') plt.colorbar(label='Intensity [dB]') plt.show()

  3. "Python3 create spectrogram from .wav file"

    • Description: Users issuing this query are looking for methods to create a spectrogram from a .wav audio file using Python3.
    • Code Implementation:
      import numpy as np import matplotlib.pyplot as plt from scipy.io import wavfile from scipy.signal import spectrogram # Load the .wav file sample_rate, audio_data = wavfile.read('your_audio_file.wav') # Generate the spectrogram frequencies, times, spectrogram_data = spectrogram(audio_data, sample_rate) # Plot the spectrogram plt.pcolormesh(times, frequencies, 10 * np.log10(spectrogram_data)) plt.ylabel('Frequency [Hz]') plt.xlabel('Time [sec]') plt.colorbar(label='Intensity [dB]') plt.show()

  4. "Python3 code to convert .wav to spectrogram"

    • Description: This query aims to find Python3 code snippets that demonstrate how to convert a .wav file to a spectrogram.
    • Code Implementation:
      import numpy as np import matplotlib.pyplot as plt from scipy.io import wavfile from scipy.signal import spectrogram # Load the .wav file sample_rate, audio_data = wavfile.read('your_audio_file.wav') # Generate the spectrogram frequencies, times, spectrogram_data = spectrogram(audio_data, sample_rate) # Plot the spectrogram plt.pcolormesh(times, frequencies, 10 * np.log10(spectrogram_data)) plt.ylabel('Frequency [Hz]') plt.xlabel('Time [sec]') plt.colorbar(label='Intensity [dB]') plt.show()

  5. "Python3 .wav to spectrogram conversion tutorial"

    • Description: Users searching with this query are likely looking for a step-by-step tutorial on how to convert a .wav file to a spectrogram using Python3.
    • Code Implementation:
      import numpy as np import matplotlib.pyplot as plt from scipy.io import wavfile from scipy.signal import spectrogram # Load the .wav file sample_rate, audio_data = wavfile.read('your_audio_file.wav') # Generate the spectrogram frequencies, times, spectrogram_data = spectrogram(audio_data, sample_rate) # Plot the spectrogram plt.pcolormesh(times, frequencies, 10 * np.log10(spectrogram_data)) plt.ylabel('Frequency [Hz]') plt.xlabel('Time [sec]') plt.colorbar(label='Intensity [dB]') plt.show()

  6. "Python3 spectrogram generation from .wav file"

    • Description: This query is focused on finding methods to generate a spectrogram from a .wav audio file using Python3.
    • Code Implementation:
      import numpy as np import matplotlib.pyplot as plt from scipy.io import wavfile from scipy.signal import spectrogram # Load the .wav file sample_rate, audio_data = wavfile.read('your_audio_file.wav') # Generate the spectrogram frequencies, times, spectrogram_data = spectrogram(audio_data, sample_rate) # Plot the spectrogram plt.pcolormesh(times, frequencies, 10 * np.log10(spectrogram_data)) plt.ylabel('Frequency [Hz]') plt.xlabel('Time [sec]') plt.colorbar(label='Intensity [dB]') plt.show()

  7. "Python3 convert .wav to spectrogram using scipy"

    • Description: Users issuing this query are interested in converting a .wav file to a spectrogram using the scipy library in Python3.
    • Code Implementation:
      import numpy as np import matplotlib.pyplot as plt from scipy.io import wavfile from scipy.signal import spectrogram # Load the .wav file sample_rate, audio_data = wavfile.read('your_audio_file.wav') # Generate the spectrogram frequencies, times, spectrogram_data = spectrogram(audio_data, sample_rate) # Plot the spectrogram plt.pcolormesh(times, frequencies, 10 * np.log10(spectrogram_data)) plt.ylabel('Frequency [Hz]') plt.xlabel('Time [sec]') plt.colorbar(label='Intensity [dB]') plt.show()

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