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## real-time_auto-tagging_with_essentia_tensorflow_pyaudio_musicnn.ipynb

      
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                palonso
                / real-time_auto-tagging_with_essentia_tensorflow_pyaudio_musicnn.ipynb
            
            
              Last active
              October 28, 2019 16:09
            
              
                Real-time auto-tagging example using Essentia with Tensorflow and PyAudio to process the soundcard input using the MusiCNN model.
              
          
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## nsgcq_encoded.py
import numpy as np
import matplotlib.pyplot as plt

import essentia.pytools.spectral as sp
from essentia.standard import MonoLoader, MonoWriter
from essentia import lin2db

PARAMS = {
    'frameSize': 2 ** 14,
    'sampleRate': 44100,

## ismir_lbd_deep-embeddings-with-essentia-models.ipynb

      
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                palonso
                / ismir_lbd_deep-embeddings-with-essentia-models.ipynb
            
            
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              September 29, 2020 10:04
            
              
                Embedding extraction demo for the ISMIR 2020 LBD "Deep embeddings with Essentia models"
              
          
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## extract-openl3-embeddings.py
from pathlib import Path
import essentia.standard as es
import numpy as np
from essentia import Pool


class MelSpectrogramOpenL3:
    def __init__(self, hop_time):
        self.hop_time = hop_time

## freezer.py
# tensorflow: loading model
sess = tf.Session()
sess.run(tf.global_variables_initializer())

saver = tf.train.Saver()
results_folder = model + '/'
saver.restore(sess, results_folder)

gd = sess.graph.as_graph_def()
	import numpy as np
	import matplotlib.pyplot as plt

	import essentia.pytools.spectral as sp
	from essentia.standard import MonoLoader, MonoWriter
	from essentia import lin2db

	PARAMS = {
	'frameSize': 2 ** 14,
	'sampleRate': 44100,
	from pathlib import Path
	import essentia.standard as es
	import numpy as np
	from essentia import Pool


	class MelSpectrogramOpenL3:
	def __init__(self, hop_time):
	self.hop_time = hop_time
	# tensorflow: loading model
	sess = tf.Session()
	sess.run(tf.global_variables_initializer())

	saver = tf.train.Saver()
	results_folder = model + '/'
	saver.restore(sess, results_folder)

	gd = sess.graph.as_graph_def()