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import os
import numpy as np
import librosa
import tensorflow as tf
# disable eager mode for tf.v1 compatibility with tf.v2
from musicnn import models
from musicnn import configuration as config
def batch_data(audio_file, n_frames, overlap):
'''For an efficient computation, we split the full music spectrograms in patches of length n_frames with overlap.
- file_name: path to the music file to tag.
Data format: string.
Example: './audio/TRWJAZW128F42760DD_test.mp3'
- n_frames: length (in frames) of the input spectrogram patches.
Data format: integer.
Example: 187
- overlap: ammount of overlap (in frames) of the input spectrogram patches.
Note: Set it considering n_frames.
Data format: integer.
Example: 10
- batch: batched audio representation. It returns spectrograms split in patches of length n_frames with overlap.
Data format: 3D np.array (batch, time, frequency)
- audio_rep: raw audio representation (spectrogram).
Data format: 2D np.array (time, frequency)
# compute the log-mel spectrogram with librosa
audio, sr = librosa.load(audio_file, sr=config.SR)
audio_rep = librosa.feature.melspectrogram(y=audio,
audio_rep = audio_rep.astype(np.float16)
audio_rep = np.log10(10000 * audio_rep + 1)
# batch it for an efficient computing
first = True
last_frame = audio_rep.shape[0] - n_frames + 1
# +1 is to include the last frame that range would not include
for time_stamp in range(0, last_frame, overlap):
patch = np.expand_dims(audio_rep[time_stamp : time_stamp + n_frames, : ], axis=0)
if first:
batch = patch
first = False
batch = np.concatenate((batch, patch), axis=0)
return batch, audio_rep
def extractor(file_name, model='MTT_musicnn', input_length=3, input_overlap=False, extract_features=True):
'''Extract the taggram (the temporal evolution of tags) and features (intermediate representations of the model) of the music-clip in file_name with the selected model.
- file_name: path to the music file to tag.
Data format: string.
Example: './audio/TRWJAZW128F42760DD_test.mp3'
- model: select a music audio tagging model.
Data format: string.
Options: 'MTT_musicnn', 'MTT_vgg', 'MSD_musicnn', 'MSD_musicnn_big' or 'MSD_vgg'.
MTT models are trained with the MagnaTagATune dataset.
MSD models are trained with the Million Song Dataset.
To know more about these models, check our musicnn / vgg examples, and the FAQs.
Important! 'MSD_musicnn_big' is only available if you install from source: python install.
- input_length: length (in seconds) of the input spectrogram patches. Set it small for real-time applications.
Note: This is the length of the data that is going to be fed to the model. In other words, this parameter defines the temporal resolution of the taggram.
Recommended value: 3, because the models were trained with 3 second inputs.
Observation: the vgg models do not allow for different input lengths. For this reason, the vgg models' input_length needs to be set to 3. However, musicnn models allow for different input lengths: see this jupyter notebook.
Data format: floating point number.
Example: 3.1
- input_overlap: ammount of overlap (in seconds) of the input spectrogram patches.
Note: Set it considering the input_length.
Data format: floating point number.
Example: 1.0
- extract_features: set it True for extracting the intermediate representations of the model.
Data format: boolean.
Options: False (for NOT extracting the features), True (for extracting the features).
- taggram: expresses the temporal evolution of the tags likelihood.
Data format: 2D np.ndarray (time, tags).
Example: see our basic / advanced examples.
- tags: list of tags corresponding to the tag-indices of the taggram.
Data format: list.
Example: see our FAQs page for the complete tags list.
- features: if extract_features = True, it outputs a dictionary containing the activations of the different layers the selected model has.
Data format: dictionary.
Keys (musicnn models): ['timbral', 'temporal', 'cnn1', 'cnn2', 'cnn3', 'mean_pool', 'max_pool', 'penultimate']
Keys (vgg models): ['pool1', 'pool2', 'pool3', 'pool4', 'pool5']
Example: see our musicnn and vgg examples.
# select model
if 'MTT' in model:
labels = config.MTT_LABELS
elif 'MSD' in model:
labels = config.MSD_LABELS
num_classes = len(labels)
if 'vgg' in model and input_length != 3:
raise ValueError('Set input_length=3, the VGG models cannot handle different input lengths.')
# convert seconds to frames
n_frames = librosa.time_to_frames(input_length, sr=config.SR, n_fft=config.FFT_SIZE, hop_length=config.FFT_HOP) + 1
if not input_overlap:
overlap = n_frames
overlap = librosa.time_to_frames(input_overlap, sr=config.SR, n_fft=config.FFT_SIZE, hop_length=config.FFT_HOP)
# tensorflow: define the model
with tf.name_scope('model'):
x = tf.compat.v1.placeholder(tf.float32, [None, n_frames, config.N_MELS])
is_training = tf.compat.v1.placeholder(tf.bool)
if 'vgg' in model:
y, pool1, pool2, pool3, pool4, pool5 = models.define_model(x, is_training, model, num_classes)
y, timbral, temporal, cnn1, cnn2, cnn3, mean_pool, max_pool, penultimate = models.define_model(x, is_training, model, num_classes)
normalized_y = tf.nn.sigmoid(y)
# tensorflow: loading model
sess = tf.compat.v1.Session()
saver = tf.compat.v1.train.Saver()
saver.restore(sess, os.path.dirname(__file__)+'/'+model+'/')
if model == 'MSD_musicnn_big':
raise ValueError('MSD_musicnn_big model is only available if you install from source: python install')
elif model == 'MSD_vgg':
raise ValueError('MSD_vgg model is still training... will be available soon! :)')
# batching data
print('Computing spectrogram (w/ librosa) and tags (w/ tensorflow)..', end =" ")
batch, spectrogram = batch_data(file_name, n_frames, overlap)
# tensorflow: extract features and tags
# ..first batch!
if extract_features:
if 'vgg' in model:
extract_vector = [normalized_y, pool1, pool2, pool3, pool4, pool5]
extract_vector = [normalized_y, timbral, temporal, cnn1, cnn2, cnn3, mean_pool, max_pool, penultimate]
extract_vector = [normalized_y]
tf_out =,
feed_dict={x: batch[:config.BATCH_SIZE],
is_training: False})
if extract_features:
if 'vgg' in model:
predicted_tags, pool1_, pool2_, pool3_, pool4_, pool5_ = tf_out
features = dict()
features['pool1'] = np.squeeze(pool1_)
features['pool2'] = np.squeeze(pool2_)
features['pool3'] = np.squeeze(pool3_)
features['pool4'] = np.squeeze(pool4_)
features['pool5'] = np.squeeze(pool5_)
predicted_tags, timbral_, temporal_, cnn1_, cnn2_, cnn3_, mean_pool_, max_pool_, penultimate_ = tf_out
features = dict()
features['timbral'] = np.squeeze(timbral_)
features['temporal'] = np.squeeze(temporal_)
features['cnn1'] = np.squeeze(cnn1_)
features['cnn2'] = np.squeeze(cnn2_)
features['cnn3'] = np.squeeze(cnn3_)
features['mean_pool'] = mean_pool_
features['max_pool'] = max_pool_
features['penultimate'] = penultimate_
predicted_tags = tf_out[0]
taggram = np.array(predicted_tags)
# of the batches!
for id_pointer in range(config.BATCH_SIZE, batch.shape[0], config.BATCH_SIZE):
tf_out =,
feed_dict={x: batch[id_pointer:id_pointer+config.BATCH_SIZE],
is_training: False})
if extract_features:
if 'vgg' in model:
predicted_tags, pool1_, pool2_, pool3_, pool4_, pool5_ = tf_out
features['pool1'] = np.concatenate((features['pool1'], np.squeeze(pool1_)), axis=0)
features['pool2'] = np.concatenate((features['pool2'], np.squeeze(pool2_)), axis=0)
features['pool3'] = np.concatenate((features['pool3'], np.squeeze(pool3_)), axis=0)
features['pool4'] = np.concatenate((features['pool4'], np.squeeze(pool4_)), axis=0)
features['pool5'] = np.concatenate((features['pool5'], np.squeeze(pool5_)), axis=0)
predicted_tags, timbral_, temporal_, midend1_, midend2_, midend3_, mean_pool_, max_pool_, penultimate_ = tf_out
features['timbral'] = np.concatenate((features['timbral'], np.squeeze(timbral_)), axis=0)
features['temporal'] = np.concatenate((features['temporal'], np.squeeze(temporal_)), axis=0)
features['cnn1'] = np.concatenate((features['cnn1'], np.squeeze(cnn1_)), axis=0)
features['cnn2'] = np.concatenate((features['cnn2'], np.squeeze(cnn2_)), axis=0)
features['cnn3'] = np.concatenate((features['cnn3'], np.squeeze(cnn3_)), axis=0)
features['mean_pool'] = np.concatenate((features['mean_pool'], mean_pool_), axis=0)
features['max_pool'] = np.concatenate((features['max_pool'], max_pool_), axis=0)
features['penultimate'] = np.concatenate((features['penultimate'], penultimate_), axis=0)
predicted_tags = tf_out[0]
taggram = np.concatenate((taggram, np.array(predicted_tags)), axis=0)
if extract_features:
return taggram, labels, features
return taggram, labels
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