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import os
import matplotlib.pyplot as plt
import numpy as np
import librosa
import panns_inference
from panns_inference import AudioTagging, SoundEventDetection, labels
def print_audio_tagging_result(clipwise_output):
"""Visualization of audio tagging result.
Args:
clipwise_output: (classes_num,)
"""
sorted_indexes = np.argsort(clipwise_output)[::-1]
# Print audio tagging top probabilities
for k in range(10):
print('{}: {:.3f}'.format(np.array(labels)[sorted_indexes[k]],
clipwise_output[sorted_indexes[k]]))
def plot_sound_event_detection_result(framewise_output):
"""Visualization of sound event detection result.
Args:
framewise_output: (time_steps, classes_num)
"""
out_fig_path = 'results/sed_result.png'
os.makedirs(os.path.dirname(out_fig_path), exist_ok=True)
classwise_output = np.max(framewise_output, axis=0) # (classes_num,)
idxes = np.argsort(classwise_output)[::-1]
idxes = idxes[0:5]
ix_to_lb = {i : label for i, label in enumerate(labels)}
lines = []
for idx in idxes:
line, = plt.plot(framewise_output[:, idx], label=ix_to_lb[idx])
lines.append(line)
plt.legend(handles=lines)
plt.xlabel('Frames')
plt.ylabel('Probability')
plt.ylim(0, 1.)
plt.savefig(out_fig_path)
print('Save fig to {}'.format(out_fig_path))
if __name__ == '__main__':
"""Example of using panns_inferece for audio tagging and sound evetn detection.
"""
device = 'cpu' # 'cuda' | 'cpu'
audio_path = 'resources/R9_ZSCveAHg_7s.wav'
(audio, _) = librosa.core.load(audio_path, sr=32000, mono=True)
#print(audio)
plt.plot(audio)
plt.savefig('sample.png')
audio = audio[None, :] # (batch_size, segment_samples)
#print(audio)
print('------ Audio tagging ------')
at = AudioTagging(checkpoint_path=None, device=device)
(clipwise_output, embedding) = at.inference(audio)
"""clipwise_output: (batch_size, classes_num), embedding: (batch_size, embedding_size)"""
print_audio_tagging_result(clipwise_output[0])
print('------ Sound event detection ------')
sed = SoundEventDetection(checkpoint_path=None, device=device)
framewise_output = sed.inference(audio)
"""(batch_size, time_steps, classes_num)"""
plot_sound_event_detection_result(framewise_output[0]) |