multiaudio_danger_random_prediction.py

ind = int(np.random.uniform()*len(inputs_test))
spectrogram_out = inputs_test[ind]

ipd.Audio(spectrogram_out, rate=desired_sr)

y = labels_test[ind]
output = model.predict(np.expand_dims(np.array([inputs_test[ind]]), 0))

print("True label:", textLabel(np.argmax(y)))
print("Prediction:", textLabel(np.argmax(output)))

plt.imshow(spectrogram_out, cmap='hot', interpolation='nearest', aspect='auto')
plt.show()

# Playing the sample
'''
audio = librosa.core.istft(spectrogram_out, hop_length=hop_len, win_length=win_len)
playback_sr, audio_interpolated = interpolateAudio(audio)
ipd.Audio(audio_interpolated, rate=playback_sr, autoplay=True)
'''