def extract_other_features(paths, path2gt, model_type):
"""Extracts MusiCNN or OpenL3 features and their corresponding ground_truth and identifiers (the path).
OpenL3 features are extracted from non-overlapping audio patches of 1 second,
where each audio patch covers 128 mel bands.
MusiCNN features are extracted from non-overlapping audio patches of 1 second,
where each audio patch covers 96 mel bands.
We repeat ground_truth and identifiers to fit the number of extracted OpenL3 features.
"""
if model_type == 'openl3':
model = openl3.models.load_embedding_model(input_repr="mel128",
content_type="music",
embedding_size=512)
first_audio = True
for p in paths:
if model_type == 'musicnn':
taggram, tags, extracted_features = extractor(
config['audio_folder'] + p,
model='MSD_musicnn',
extract_features=True,
input_overlap=1)
emb = extracted_features[
'max_pool'] # or choose any other layer, for example: emb = taggram
# Documentation: https://github.com/jordipons/musicnn/blob/master/DOCUMENTATION.md
elif model_type == 'openl3':
wave, sr = wavefile_to_waveform(config['audio_folder'] + p,
'openl3')
emb, _ = openl3.get_embedding(wave,
sr,
hop_size=1,
model=model,
verbose=False)
if first_audio:
features = emb
ground_truth = np.repeat(path2gt[p], features.shape[0], axis=0)
identifiers = np.repeat(p, features.shape[0], axis=0)
first_audio = False
else:
features = np.concatenate((features, emb), axis=0)
tmp_gt = np.repeat(path2gt[p], emb.shape[0], axis=0)
ground_truth = np.concatenate((ground_truth, tmp_gt), axis=0)
tmp_id = np.repeat(p, emb.shape[0], axis=0)
identifiers = np.concatenate((identifiers, tmp_id), axis=0)
return [features, ground_truth, identifiers]
def classify_audio(fname: str, model_path="../models/features_classifier.pkl"):
# Load classifier
clf = joblib.load(model_path)
# Extract the `max_pool' features using musicnn
_, _, features = extractor(fname,
model="MSD_musicnn",
input_overlap=1,
extract_features=True)
maxpool_features = features["max_pool"]
# Classify each feature with the trained classifier
y_pred = clf.predict(maxpool_features)
# Assign a string tag (e.g. 'classical' instead of 1) to each predicted class
y_pred_labels = [idx2tag[n] for n in y_pred]
# The assigned genre is the most common tag
tag = Counter(y_pred_labels).most_common()[0][0]
return tag
def preprocess(self, musiccnn=False):
debug_message = "preprocess" if musiccnn == False else "musiccnn preprocess"
print(f'{debug_message} start')
feature_name = '/spec/' if musiccnn == False else '/embed/'
#Make dir for mel spec
for genre in self.genres:
os.makedirs(self.file_list_path + feature_name + genre,
exist_ok=True)
path_out_list = []
for path_in in tqdm(self.train_path_list + self.test_path_list):
path_out = self.file_list_path + feature_name + path_in.replace(
'.wav', '.npy')
path_out_list.append(path_out)
if os.path.isfile(path_out):
print(f'{path_out} already exists')
continue
if musiccnn:
_, _, embeds = extractor(
f'{self.file_list_path}/wav/{path_in}',
model='MSD_musicnn',
extract_features=True)
embed = embeds['max_pool'].mean(axis=0)
np.save(path_out, embed)
else:
signal, _ = librosa.load(
f'{self.file_list_path}/wav/{path_in}',
sr=self.sampling_rate)
melspec = librosa.feature.melspectrogram(
signal,
sr=self.sampling_rate,
n_fft=self.number_fft,
hop_length=self.hop_length,
n_mels=self.number_mel)
melspec = librosa.power_to_db(melspec)
melspec = melspec.astype('float32')
np.save(path_out, melspec)
print(f'{debug_message} finish')
return path_out_list
def extract_embeddings(in_base, out_base, model='MSD_musicnn'):
# Make directories to save embeddings.
for split in splits:
out_dir = out_base + split
os.makedirs(out_dir, exist_ok=True)
for path_in in tqdm(glob(os.path.join(in_base, split, '*.wav'))):
filename = path_in.split('/')[-1]
path_out = os.path.join(out_dir, filename.replace('.wav', '.npy'))
# Skip if the embeddings file already exists
if os.path.isfile(path_out):
continue
# Extract the embedding using the pre-trained model.
_, _, embeds = extractor(path_in,
model=model,
extract_features=True)
# Average the embeddings over temporal dimension.
embed = embeds['max_pool'].mean(axis=0)
# Save the embedding.
np.save(path_out, embed)
# # This notebook explains how to use the vgg models in `musicnn` as music feature extractors. `musicnn` allows you to extract features at every layer of the model. For this reason, we first present it – so that you can understand what to expect out of each layer. To start, let's consider this music clip: # In[1]: file_name = './audio/TRWJAZW128F42760DD_test.mp3' # Run these two lines of code to extract music features with our vgg model trained with the [MagnaTagATune](https://github.com/keunwoochoi/magnatagatune-list) dataset – the `MTT_vgg` model: # In[2]: from musicnn.extractor import extractor taggram, tags, features = extractor(file_name, model='MTT_vgg', extract_features=True) # Out of the extractor, we get the **output** of the model (the `taggram` and its associated `tags`) and all the **intermediate representations** of it (we refer to those as `features`). The `features` are packed in a dictionary: # In[3]: list(features.keys()) # These different key-features correspond to the outputs of the different layers that our vgg model has. For this reason, it is important that you understand the basic bulding blocks of this model – that we briefly outline in the following diagram: # # <br> # <img src="./images/vgg.png"> # <br>
def plotOutDiagrams(songPath, modelUsed, subfolderName=False, show=False):
# print("The Path is " + songPath)
taggram, tags, somethingElse = extractor(songPath, model=modelUsed)
if "SV.wav" == songPath and subfolderName:
# songName = "SV_" + os.path.basename(os.path.dirname(songPath))
# print("SubfolderName: " + subfolderName)
# print("DirName: " + os.path.dirname(subfolderName))
# print("BaseName: " + os.path.basename(subfolderName))
songName = "SV_" + fixName(os.path.basename(subfolderName))
else:
songName = os.path.basename(songPath)[:-4]
# print("The song name is " + songName)
in_length = 3 # seconds by default, the model takes inputs of 3 seconds with no overlap
plt.rcParams["figure.figsize"] = (10, 8) # set size of the figures
fontsize = 12 # set figures font size
fig, ax = plt.subplots()
# title
ax.title.set_text('Taggram ' + songName + " " + modelUsed)
ax.title.set_fontsize(fontsize)
# x-axis title
ax.set_xlabel('(seconds)', fontsize=fontsize)
# y-axis
y_pos = np.arange(len(tags))
ax.set_yticks(y_pos)
ax.set_yticklabels(tags, fontsize=fontsize - 1)
# x-axis
x_pos = np.arange(taggram.shape[0])
x_label = np.arange(in_length / 2, in_length * taggram.shape[0], 3)
ax.set_xticks(x_pos)
ax.set_xticklabels(x_label, fontsize=fontsize)
# depict taggram
ax.imshow(taggram.T, interpolation=None, aspect="auto")
if show:
plt.show()
plt.savefig("Taggram " + songName + " _ " + modelUsed + ".png")
####
tags_likelihood_mean = np.mean(
taggram, axis=0) # averaging the Taggram through time
fig, ax = plt.subplots()
# title
ax.title.set_text('Tags likelihood (mean of the taggram) ' + songName)
ax.title.set_fontsize(fontsize)
# y-axis title
ax.set_ylabel('(likelihood)', fontsize=fontsize)
# y-axis
ax.set_ylim((0, 1))
ax.tick_params(axis="y", labelsize=fontsize)
# x-axis
ax.tick_params(axis="x", labelsize=fontsize - 1)
pos = np.arange(len(tags))
ax.set_xticks(pos)
ax.set_xticklabels(tags, rotation=90)
# depict song-level tags likelihood
ax.bar(pos, tags_likelihood_mean)
if show:
plt.show()
# plt.show()
plt.savefig("Tags likelihood " + songName + " " + modelUsed + ".png")
plt.close('all')
return taggram, tags
ax.title.set_text('Taggram')
ax.title.set_fontsize(fontsize)
# x-axis title
ax.set_xlabel('(seconds)', fontsize=fontsize)
# y-axis
y_pos = np.arange(len(tags))
ax.set_yticks(y_pos)
ax.set_yticklabels(tags, fontsize=fontsize-2)
# x-axis
x_pos = np.arange(taggram.shape[0])
x_label = np.arange(taggram.shape[0])
ax.set_xticks(x_pos)
ax.set_xticklabels(x_label, fontsize=4)
plt.show()
print("Generating MSD tags...")
MSD_taggram, MSD_tags, MSD_features = extractor(input_path, model='MSD_musicnn', input_overlap=1, extract_features=True)
print("Generating MTT tags...")
MTT_taggram, MTT_tags, MTT_features = extractor(input_path, model='MTT_musicnn', input_overlap=1, extract_features=True)
taggram = np.concatenate((MSD_taggram, MTT_taggram), 1)
tags = np.concatenate((MSD_tags, MTT_tags))
showTags(taggram, tags)
exportJSON(taggram, tags, output_path)
"Wrong usage. Correct example: `python3 recommend.py path_to_file.mp3`"
)
sys.exit(1)
# get the argument/path provided and check if it's .mp3
input_path = sys.argv[1]
if '.mp3' not in input_path:
print("Wrong path format. The script has only been tested on .mp3 files")
sys.exit(2)
# get the length of the audio
input_length = math.floor(MP3(input_path).info.length)
# extract features using MusicNN
input_embed, _, _ = extractor(input_path,
model='MTT_musicnn',
input_length=input_length,
extract_features=True)
# load in the training set and select the embedding part
data = pd.read_csv('data/train_set.csv')
embeddings = data.iloc[:, 1:].values
# compute the similarities between input_embed and embeddings
cos_sim = []
for i in range(embeddings.shape[0]):
cos_sim.append(
cosine_similarity(input_embed.tolist()[0], embeddings[i, :].tolist()))
# sort cosine similarities, get top_n of them and their audio paths
top_n = 3
idx = np.array(cos_sim).argsort()[::-1][:top_n]
def top_tags(file_name,
model='MTT_musicnn',
topN=3,
input_length=3,
input_overlap=False,
print_tags=True,
save_tags=False):
''' Predict the topN tags of the music-clip in file_name with the selected model.
INPUT
- 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 setup.py install.
- topN: extract N most likely tags according to the selected model.
Data format: integer.
Example: 3
- 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
- print_tags: set it True for printing the tags.
Note: although you don't print the tags, these will be returned by the musicnn.tagger.top_tags() function.
Data format: boolean.
Options: False (for NOT printing the tags), True (for printing the tags).
- save_tags: Path where to store/save the tags.
Data format: string.
Example: 'file_name.tags'
OUTPUT
tags: topN most likely tags of the music-clip in file_name considering the selected model.
Data format: list.
Example: ['synth', 'techno']
'''
if 'vgg' in model and input_length != 3:
raise ValueError(
'Set input_length=3, the VGG models cannot handle different input lengths.'
)
taggram, tags = extractor(file_name,
model=model,
input_length=input_length,
input_overlap=input_overlap,
extract_features=False)
tags_likelihood_mean = np.mean(taggram, axis=0)
if print_tags:
print('[' + file_name + '] Top' + str(topN) + ' tags: ')
if save_tags:
to = open(save_tags, 'a')
to.write(file_name + ',' + model + ',input_length=' +
str(input_length) + ',input_overlap=' + str(input_overlap))
topN_tags = []
for tag_index in tags_likelihood_mean.argsort()[-topN:][::-1]:
topN_tags.append(tags[tag_index])
if print_tags:
print(' - ' + tags[tag_index])
if save_tags:
to.write(',' + tags[tag_index])
if save_tags:
to.write('\n')
to.close()
return topN_tags
def analyse_list(parsedlist):
track_tags_mtt = []
track_compos_mtt = []
track_tags_msd = []
track_compos_msd = []
for j, e in enumerate(parsedlist):
fn = e[4]
#top_tags_msd = top_tags(fn, model='MSD_musicnn', topN=10) ; top_tags_mtt = top_tags(fn, model='MTT_musicnn', topN=10)
taggram_msd, tags_msd, feats_msd = extractor(fn,
model="MSD_musicnn",
extract_features=True)
taggram_mtt, tags_mtt, feats_mtt = extractor(fn,
model="MTT_musicnn",
extract_features=True)
# get genre tags and associated likelyhood
means_mtt = taggram_mtt.mean(0)
top_indexes_mtt = sorted(range(len(means_mtt)),
key=lambda i: means_mtt[i],
reverse=True)[:9]
track_tags_mtt = [[tags_mtt[ix], '{:0.4f}'.format(means_mtt[ix])]
for ix in top_indexes_mtt]
means_msd = taggram_msd.mean(0)
top_indexes_msd = sorted(range(len(means_msd)),
key=lambda i: means_msd[i],
reverse=True)[:9]
track_tags_msd = [[tags_msd[ix], '{:0.4f}'.format(means_msd[ix])]
for ix in top_indexes_msd]
# get markovian temporal composition
track_compos_mtt = [[
tags_mtt[np.argmax(taggram_mtt[i])],
'{:0.4f}'.format(taggram_mtt[i][np.argmax(taggram_mtt[i])])
] for i in range(len(taggram_mtt))]
track_compos_msd = [[
tags_msd[np.argmax(taggram_msd[i])],
'{:0.4f}'.format(taggram_msd[i][np.argmax(taggram_msd[i])])
] for i in range(len(taggram_msd))]
# get other metadata
audio = AudioSegment.from_file(fn)
a_dur = audio.duration_seconds
a_fr = audio.frame_rate
a_fc = audio.frame_count()
a_ch = audio.channels
a_wid = audio.sample_width
a_max = audio.max
a_maxdb = audio.max_dBFS
a_rms = audio.rms
# pack the data
track_data = {
'duration': '{:0.4f}'.format(a_dur),
'sample_rate': str(a_fr),
'sample_count': '{:0.2f}'.format(a_fc),
'channels': str(a_ch),
'bit_resolution': str(8 * a_wid),
'max_volume': '{:0.4f}'.format(a_maxdb),
'mean_rms': '{:0.4f}'.format(a_rms),
'tags_mtt': track_tags_mtt,
'tags_msd': track_tags_msd,
'comp_mtt': track_compos_mtt,
'comp_msd': track_compos_msd
}
e.append(track_data)
print("\n\n\t\t\t -----", j)
return parsedlist
from musicnn.tagger import top_tags
tags = top_tags(file_name, model='MTT_musicnn', topN=3)
# -----------------------------
# ### Are you interested in the temporal evolution of these tags?
#
# Instead of predicting song-level tags, you can also plot the **Taggram**:
# In[3]:
from musicnn.extractor import extractor
taggram, tags = extractor(file_name,
model='MTT_musicnn',
extract_features=False)
# In[4]:
get_ipython().run_line_magic('matplotlib', 'inline')
import numpy as np
import matplotlib.pyplot as plt
# In[5]:
in_length = 3 # seconds by default, the model takes inputs of 3 seconds with no overlap
plt.rcParams["figure.figsize"] = (10, 8) # set size of the figures
fontsize = 12 # set figures font size
def make_frames(audio_file):
taggram, tags, feature_map = extractor(audio_file,
model=MUSICNN_MODEL,
input_length=MUSICNN_INPUT_LENGTH)
print(f'Musicnn features: {feature_map.keys()}')
feature_map['taggram'] = taggram
song_features = feature_map[FEATURE_NAME]
graph = tf.Graph()
sess = tf.InteractiveSession(graph=graph)
with tf.gfile.FastGFile(DEEP_DREAM_MODEL, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
# define input
X = tf.placeholder(tf.float32, name="input")
X2 = tf.expand_dims(X - IMAGENET_MEAN, 0)
tf.import_graph_def(graph_def, {"input": X2})
losses = []
targets = []
layers = []
num_features = 0
for layer_name in LAYER_NAMES:
layer = graph.get_tensor_by_name("import/%s:0" % layer_name)
layers.append(layer)
num_features += int(layer.shape[-1])
print(f'Layer {layer_name}, shape {layer.shape}')
target = tf.placeholder(tf.float32, name="target")
targets.append(target)
# loss = tf.reduce_mean(tf.sqrt(tf.square(layer - target)))
loss = tf.reduce_mean(layer * target)
losses.append(loss)
loss = losses[0] * LAYER_WEIGHTS[0]
for i in range(1, len(losses)):
loss = loss + losses[i] * LAYER_WEIGHTS[i]
gradient = tf.gradients(loss, X)[0]
def make_frame(image):
frame = cv2.resize(image, (OUTPUT_IMAGE_SIZE, OUTPUT_IMAGE_SIZE),
interpolation=cv2.INTER_CUBIC) / 255
frame = np.clip(frame, 0, 1)
frame = np.power(frame, GAMMA) * 255
return frame
image = np.full((OCTAVE_PARAMS[0][0], OCTAVE_PARAMS[0][0], 3),
IMAGENET_MEAN,
dtype=np.float32)
frame_num = 0
for fi in range(len(song_features)):
target_values = []
features = song_features[fi]
scale = int(num_features / len(features) * 4)
scale = scale if scale % 2 else scale + 1
features = cv2.resize(np.tile(features, (scale, 1)),
(num_features, scale),
interpolation=cv2.INTER_LINEAR)[scale // 2]
features = (features > FEATURE_THRESHOLD).astype(np.float32)
print(
f'Non-zero features {features.sum() / len(features) * 100:0.1f}%')
mix_rng.shuffle(features)
start = 0
for l in range(len(layers)):
layer = layers[l]
target_size = int(layer.shape[3])
t = features[start:start + target_size]
start += target_size
target_values.append(t)
for oi in range(len(OCTAVE_PARAMS)):
# l = sess.run(layer, {X: image})
# print(f'size {image.shape} l shape {l.shape} l range {l.min()} {l.max()}')
for batch in range(OCTAVE_PARAMS[oi][1]):
args = {X: image}
for t in range(len(targets)):
args[targets[t]] = target_values[t]
g = sess.run(gradient, args)
lr = OCTAVE_PARAMS[oi][2]
image += lr * g / (np.abs(g).mean() + 1e-7)
frame = make_frame(image)
cv2.imwrite(os.path.join(TEMP_DIR, f'f-{frame_num:05d}.png'),
frame)
frame_num += 1
cv2.imshow(f'image', frame / 255)
cv2.waitKey(1)
if oi < len(OCTAVE_PARAMS) - 1:
image = cv2.resize(
image,
(OCTAVE_PARAMS[oi + 1][0], OCTAVE_PARAMS[oi + 1][0]),
interpolation=cv2.INTER_CUBIC)
downscaled = image
for oi in range(len(OCTAVE_PARAMS) - 2, -1, -1):
s = OCTAVE_PARAMS[oi][0]
downscaled = cv2.resize(downscaled, (s, s),
interpolation=cv2.INTER_CUBIC)
frame = make_frame(downscaled)
cv2.imwrite(os.path.join(TEMP_DIR, f'f-{frame_num:05d}.png'),
frame)
frame_num += 1
cv2.imshow(f'image', frame / 255)
cv2.waitKey(100)
global fps
if fps is None:
fps = int(np.round(frame_num / MUSICNN_INPUT_LENGTH))
image = cv2.resize(image, (OCTAVE_PARAMS[0][0], OCTAVE_PARAMS[0][0]),
interpolation=cv2.INTER_CUBIC)
image = (image - image.min()) / (image.max() - image.min()) * 255
from musicnn.extractor import extractor
from musicnn.tagger import top_tags
from mutagen.mp3 import MP3
audio_names = os.listdir('music/')
audio_paths = ['music/' + i for i in audio_names]
# creating a data set with MusicNN features
features = []
for audio_path in audio_paths:
print('Extracting features for ' + audio_path)
# check the length of the audio
audio_length = math.floor(MP3(audio_path).info.length)
# extract features using MusicNN
taggram, tags, _ = extractor(audio_path,
model='MTT_musicnn',
input_length=audio_length,
extract_features=True)
print(taggram.shape)
features.append(taggram)
features = np.vstack(features)
# create and save a table
df = pd.DataFrame(features, columns=tags)
df['audio_path'] = audio_paths
df = df[['audio_path'] + tags] # rearrange columns
df.to_csv('data/train_set.csv', index=False)