Loading wav data
To work with any non-standard data using Dask bags, you will need to write a lot of functions yourself. For this task, you are analyzing audio data, and so you need a custom function to load it.
Some of the audio recordings failed, and the audio is silent in these. Regular audio data looks like a wave, where the amplitude goes to large positive and negative values. Therefore, to check if a recording is silent, you can check whether the audio clip has very small amplitudes overall.
The scipy.io.wavfile module has been imported into your environment as wavfile, and numpy has been imported as np.
Este exercício faz parte do curso
Parallel Programming with Dask in Python
Instruções do exercício
- Inside the
load_wav()function, usewavfile.read()to load the audio data and sampling frequency. - Inside
load_wav(), construct the returned dictionary. - Inside the
not_silent()function, return a boolean of whether the'audio'array inside the input dictionary has a mean absolute value of greater than 100, by usingnumpy'sabs()andmean()functions.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
def load_wav(filename):
# Load in the audio data
sampling_freq, audio = ____
# Add the filename, audio data, and sampling frequency to the dictionary
data_dict = {
'filename': ____,
'audio': ____,
'sample_frequency': ____,
}
return data_dict
def not_silent(data_dict):
# Check if the audio data is silent
return ____