def get_audio_embedding(path):
try:
audio, _ = librosa.core.load(path, sr=32000, mono=True)
audio = audio[None, :]
at = AudioTagging(checkpoint_path=None, device='cuda')
_, embedding = at.inference(audio)
embedding = embedding / np.linalg.norm(embedding)
embedding = embedding.tolist()[0]
return embedding
except Exception as e:
print("error with embedding:", path)
return None
def pann(filepath):
musical = False
(audio, _) = librosa.core.load(filepath, sr=32000, mono=True)
audio = audio[None, :] # (batch_size, segment_samples)
at = AudioTagging(checkpoint_path='/Users/jacksongoode/panns_data/MobileNetV2_mAP=0.383.pth',
device='cuda', model='MobileNet')
(clipwise_output, embedding) = at.inference(audio)
probs = list(clipwise_output[0])
if probs.index(max(probs)) in range(137, 283): # range of music classes in AudioNet
print("\nIt's music!")
musical = True
else:
print('\nNot music!')
return musical
def generate(data_dir, batch_size=256, device="cuda", sr=16000):
save_dir = _make_save_dir(data_dir=data_dir)
inferenced_labels = []
panns = AudioTagging(device=device)
data_loader = _build_data_loader(data_dir=data_dir,
batch_size=batch_size,
sr=sr)
for data_dict in tqdm(data_loader):
inferences, _ = panns.inference(data_dict["audio"])
inferenced_labels += inferences.argmax(axis=1).tolist()
for idx, inferenced_label in enumerate(inferenced_labels):
np.save(
os.path.join(save_dir, f"{idx}_audio_label.npy"),
np.array([inferenced_label]),
)
class Voice2vec(object):
def __init__(self, device='cuda'):
self.at = AudioTagging(checkpoint_path=None, device=device)
# self.sed = SoundEventDetection(checkpoint_path=None, device='cuda')
def get_embedding(self, audio_path):
audio = self.get_audio(audio_path)
(clipwise_output, embedding) = self.at.inference(audio)
return embedding
@staticmethod
def get_audio(audio_path):
audio, _ = librosa.core.load(audio_path, sr=32000, mono=True)
return audio[None, :] # (batch_size, segment_samples)
def __init__(self, device='cuda'):
self.at = AudioTagging(checkpoint_path=None, device=device)
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)
audio = audio[None, :] # (batch_size, segment_samples)
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])
'''
print("<--- Audio Tagging ---->")
model = Transfer_Cnn14(sample_rate,
window_size,
hop_size,
mel_bins,
fmin,
fmax,
classes_num,
freeze_base=0)
cp = "checkpoints/main/holdout_fold=1/Transfer_Cnn14/pretrain=True/loss_type=clip_nll/augmentation=none/batch_size=1/freeze_base=False/1000_iterations.pth"
at = AudioTagging(model=model, checkpoint_path=cp, device='cuda')
preds = []
bird_name = "black-faced_antbird"
count = 0
for ii, filename in enumerate(glob.glob(audio_path)):
print("Iteration ", ii)
audio, _ = librosa.load(filename, sr=32000, mono=True)
audio = audio[None, :]
(clipwise_output, embedding) = at.inference(audio)
nm = print_audio_tagging_result(clipwise_output[0])
if nm == bird_name:
count += 1
print("Accuracy: ", count / (ii + 1))
'''for audio in audio_arrs:
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 = 'cuda'
audio_path = 'examples/R9_ZSCveAHg_7s.wav'
(audio, _) = librosa.core.load(audio_path, sr=32000, mono=True)
audio = audio[None, :] # (batch_size, segment_samples)
print('------ Audio tagging ------')
at = AudioTagging(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(device=device)
framewise_output = sed.inference(audio)
"""(batch_size, time_steps, classes_num)"""
plot_sound_event_detection_result(framewise_output[0])