def main():
pairs = load_audio_path_label_pairs()
for index, (audio_path, _) in enumerate(pairs):
print('{} / {} ...'.format(index + 1, len(pairs)))
mg = compute_melgram(audio_path)
print('max: ', np.max(mg))
print('min: ', np.min(mg))
def compute_melgram(self, audio_path):
if audio_path in self.cache:
return self.cache[audio_path]
else:
mg = compute_melgram(audio_path)
# mg = (mg + 100) / 200 # scale the values
self.cache[audio_path] = mg
return mg
def main():
sys.path.append(patch_path('..'))
audio_file_path = patch_path('data/audio_samples/example.mp3')
from mxnet_audio.library.utility.audio_utils import compute_melgram
arr = compute_melgram(audio_file_path)
print('melgram: ', arr.shape)
def main():
sys.path.append('..')
pairs = load_audio_path_label_pairs()
from mxnet_audio.library.utility.audio_utils import compute_melgram
for index, (audio_path, _) in enumerate(pairs):
print('{} / {} ...'.format(index + 1, len(pairs)))
mg = compute_melgram(audio_path)
print('max: ', np.max(mg))
print('min: ', np.min(mg))
def generate_batch(self, audio_paths, labels, batch_size, shuffled):
num_batches = len(audio_paths) // batch_size
while True:
batch_index_list = list(range(0, num_batches))
if shuffled:
shuffle(batch_index_list)
for batchIdx in batch_index_list:
start = batchIdx * batch_size
end = (batchIdx + 1) * batch_size
X = np.zeros(shape=(batch_size, self.input_shape[0], self.input_shape[1], self.input_shape[2]),
dtype=np.float32)
for i in range(start, end):
audio_path = audio_paths[i]
mg = compute_melgram(audio_path)
X[i - start, :, :, :] = mg
yield nd.array(X, ctx=self.data_ctx), nd.array(labels[start:end], ctx=self.data_ctx)
def encode_audio(self, audio_path):
mg = compute_melgram(audio_path)
mg = nd.array(np.expand_dims(mg, axis=0), ctx=self.model_ctx)
return self.model(mg).asnumpy()[0]
def main():
audio_file_path = '../data/audio_samples/example.mp3'
# melgram_v1(audio_file_path, '../data/output/example_mp3.png')
# melgram_v2(audio_file_path)
arr = compute_melgram(audio_file_path)
print('melgram: ', arr.shape)