Delete example.py

example.py

@@ -1,74 +0,0 @@
-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])