First, you need to import the necessary libraries for handling audio and generating spectrograms. Two commonly used libraries for this purpose are "librosa" and "matplotlib".
You can install them using pip by running the following commands in your Python environment:
Once you have the libraries installed, you can start by reading the wav file using the `librosa` library. Here's an example:
```python
import librosa
# Load the wav file
audio_data, sampling_rate = librosa.load('your_file.wav')
```
In this example, `audio_data` will contain the audio waveform and `sampling_rate` will represent the sampling rate of the audio.
3. Next, you can generate the spectrogram using the `librosa` library. The spectrogram is a visual representation of the frequencies present in the audio over time. Here's an example:
```python
import matplotlib.pyplot as plt
# Generate the spectrogram
spectrogram = librosa.feature.melspectrogram(y=audio_data, sr=sampling_rate)
# Convert to decibel scale
spectrogram_db = librosa.power_to_db(spectrogram, ref=np.max)
# Display the spectrogram
plt.figure(figsize=(10, 4))
librosa.display.specshow(spectrogram_db, sr=sampling_rate, x_axis='time', y_axis='mel')
plt.colorbar(format='%+2.0f dB')
plt.title('Spectrogram')
plt.show()
```
This code will generate a spectrogram from the audio data and display it using matplotlib.
In summary, to read a wav file and create a spectrogram, you need to import the necessary libraries, load the wav file using `librosa`, generate the spectrogram, and display it using `matplotlib`.