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In [1]:



    


import numpy as np
import matplotlib.pyplot as plt
import matplotlib.style as ms
ms.use('seaborn-muted')
%matplotlib inline

import librosa
import librosa.display

import IPython.display

import os
import glob
import sys

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.externals import joblib

import keras

GENRE_DIR = "/home/minato/deep_learning/buildMLSystem/data/songData/genres/"
GENRE_LIST = []
# 0
GENRE_LIST.append("blues")
# 1
GENRE_LIST.append("classical")
# 2
GENRE_LIST.append("country")
# 3
GENRE_LIST.append("disco")
# 4
GENRE_LIST.append("hiphop")
# 5
GENRE_LIST.append("jazz")
# 6
GENRE_LIST.append("metal")
# 7
GENRE_LIST.append("pop")
# 8
GENRE_LIST.append("reggae")
# 9
GENRE_LIST.append("rock")



    


















    






Using TensorFlow backend.

















In [2]:



    


def create_data_for_conv_lstm(genre_list=GENRE_LIST):
    os.chdir(GENRE_DIR)
    for genre in genre_list:
        glob_wav = os.path.join(genre, "*.wav")
        for fn in glob.glob(glob_wav):
            create_mfcc_for_conv_lstm(fn)



    











In [3]:



    


def create_mfcc_for_conv_lstm(fn):
    y, sr = librosa.load(fn)
    mfcc = librosa.feature.mfcc(y=y, sr=sr,n_mfcc=128)
    last_dim = mfcc.shape[1]
    if last_dim < 1290:
        add_dim = 1290 - last_dim
        add_list = np.zeros((30, add_dim))
        mfcc = np.append(mfcc, add_list, axis=1)
    elif  last_dim > 1290:
        mfcc = mfcc[:,:1290]

    mfcc = mfcc.T
    data = []
    start_num = 0
    end_num = 128
    for i in range(0, 10):
        data.append(mfcc[start_num:end_num, :])
        start_num = start_num + 129
        end_num = end_num + 129

    dim1 = []
    dim1.append(data)
    dim1_10frame_data = np.array(dim1)
    base_fn, ext = os.path.splitext(fn)
    data_fn = base_fn + ".mfcc_10frame"
    np.save(data_fn, dim1_10frame_data)
    print("Written", data_fn)



    











In [4]:



    


def create_mfcc_all_data():
    genre_list = GENRE_LIST
    base_dir = GENRE_DIR
    X = []
    y = []
    for label, genre in enumerate(genre_list):
        mfcc_files = os.path.join(base_dir, genre, "*.mfcc_10frame.npy")
        for fn in glob.glob(mfcc_files):
            mfcc = np.load(fn)
            X.append(mfcc)
            y.append(label)

    print("loaded all data")
    all_x_data = np.array(X)
    all_y_data = np.array(y)
    print("all data x shape is")
    print(all_x_data.shape)
    print("all data y shape is")
    print(all_y_data.shape)

    x_data_path = '/home/minato/deep_learning/buildMLSystem/data/songData/genres/x_mfcc_10frame_all_data'
    y_data_path = '/home/minato/deep_learning/buildMLSystem/data/songData/genres/y_mfcc_10frame_all_data'

    np.save(x_data_path, all_x_data)
    np.save(y_data_path, all_y_data)
    print("Written", x_data_path)
    print("Written", y_data_path)



    











In [5]:



    


def createStdScaler():
    file_path = "/home/minato/deep_learning/buildMLSystem/data/songData/genres/x_mfcc_10frame_all_data.npy"
    all_x_data = np.load(file_path)
    n = all_x_data.shape[0]
    reshaped_data = all_x_data.reshape(n, -1)

    ss = StandardScaler()
    ss.fit(reshaped_data)
    joblib.dump(ss, './savedStanderdScaler/mfcc_10frame_ss.pkl')



    











In [6]:



    


create_data_for_conv_lstm()
create_mfcc_all_data()
createStdScaler()



    


















    






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Written rock/rock.00036.mfcc_10frame
Written rock/rock.00037.mfcc_10frame
Written rock/rock.00038.mfcc_10frame
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Written rock/rock.00040.mfcc_10frame
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Written rock/rock.00071.mfcc_10frame
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Written rock/rock.00073.mfcc_10frame
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Written rock/rock.00099.mfcc_10frame
loaded all data
all data x shape is
(1000, 1, 10, 128, 128)
all data y shape is
(1000,)
Written /home/minato/deep_learning/buildMLSystem/data/songData/genres/x_mfcc_10frame_all_data
Written /home/minato/deep_learning/buildMLSystem/data/songData/genres/y_mfcc_10frame_all_data










    






---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-6-55291ebb0dc9> in <module>()
      1 create_data_for_conv_lstm()
      2 create_mfcc_all_data()
----> 3 createStdScaler()

<ipython-input-5-d0f7395a5681> in createStdScaler()
      7     ss = StandardScaler()
      8     ss.fit(reshaped_data)
----> 9     joblib.dump(ss, './savedStanderdScaler/mfcc_10frame_ss.pkl')

/home/minato/.pyenv/versions/anaconda3-4.2.0/lib/python3.5/site-packages/sklearn/externals/joblib/numpy_pickle.py in dump(value, filename, compress, protocol, cache_size)
    480             NumpyPickler(f, protocol=protocol).dump(value)
    481     elif is_filename:
--> 482         with open(filename, 'wb') as f:
    483             NumpyPickler(f, protocol=protocol).dump(value)
    484     else:

FileNotFoundError: [Errno 2] No such file or directory: './savedStanderdScaler/mfcc_10frame_ss.pkl'

Content source: Nelca/buildMLSystem

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