keunwoochoi

# Keunwoo Choi
# This example crawl snoring sound by searching keyword 'snore'.

from __future__ import print_function
import freesound # $ git clone https://github.com/MTG/freesound-python
import os
import sys

api_key = 'YOUR_API_KEY'
folder = 'data_freesound/' # folder to save

keunwoochoi

def gen():
    print('generator initiated')
    idx = 0
    while True:
        yield x_train[:32], y_train[:32]
        print('generator yielded a batch %d' % idx)
        idx += 1
        
tr_gen = gen()
model.fit_generator(generator=tr_gen, steps_per_epoch=20, max_queue_size=10)

keunwoochoi

import time
def gen():
    print('generator initiated')
    idx = 0
    while True:
        yield x_train[:32], y_train[:32]
        print('generator yielded a batch %d' % idx)
        idx += 1
        time.sleep(3)

keunwoochoi

def gen():
    print('generator initiated')
    idx = 0
    while True:
        yield x_train[:32], y_train[:32]
        print('generator yielded a batch %d' % idx)
        idx += 1
        
tr_gen = gen()
model.fit_generator(generator=tr_gen, steps_per_epoch=20, max_queue_size=10,

keunwoochoi

import threading

class threadsafe_iter:
    """Takes an iterator/generator and makes it thread-safe by
    serializing call to the `next` method of given iterator/generator.
    """
    def __init__(self, it):
        self.it = it
        self.lock = threading.Lock()

keunwoochoi

links = '''https://ismir2017.smcnus.org/wp-content/uploads/2017/10/126_Paper.pdf
https://ismir2017.smcnus.org/wp-content/uploads/2017/10/79_Paper.pdf
https://ismir2017.smcnus.org/wp-content/uploads/2017/10/89_Paper.pdf
https://ismir2017.smcnus.org/wp-content/uploads/2017/10/119_Paper.pdf
https://ismir2017.smcnus.org/wp-content/uploads/2017/10/51_Paper.pdf
https://ismir2017.smcnus.org/wp-content/uploads/2017/10/85_Paper.pdf
https://ismir2017.smcnus.org/wp-content/uploads/2017/10/164_Paper.pdf
https://ismir2017.smcnus.org/wp-content/uploads/2017/10/220_Paper.pdf
https://ismir2017.smcnus.org/wp-content/uploads/2017/10/172_Paper.pdf
https://ismir2017.smcnus.org/wp-content/uploads/2017/10/180_Paper.pdf

keunwoochoi

import keras
from keras import backend as K
from keras.layers.convolutional import Conv2D, Conv2DTranspose
from keras.layers import Input, Dense, Activation
from keras.layers import concatenate  # functional interface
from keras.models import Model
from keras.layers.advanced_activations import LeakyReLU

N_INPUT = 512

keunwoochoi

import keras.backend as K

# Load the weights
# If there are more than 1 BN, you might wanna have a proper layer
moving_mean = f['batchnormalization_1_running_mean']
moving_std = f['batchnormalization_1_running_std']
beta = f['batchnormalization_1_beta']
gamma = f['batchnormalization_2_beta']

# BE CAREFUL! Keras 1 stores std

keunwoochoi

import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('ggplot')
import numpy as np
import librosa
import keras
from keras import backend as K
print(keras.__version__)
print('Keras backend: ', keras.backend._BACKEND, ' and image dim ordering: ', keras.backend.image_dim_ordering())

keunwoochoi

"""
Code converted by Keunwoo from https://ccrma.stanford.edu/~jos/fp/Formant_Filtering_Example.html (JOS)
"""

import numpy as np
import scipy.signal as signal
import librosa

def gen_speech(F, sig=None, filename='speech'):
    nsecs = len(F)