- In this recipe, we start by importing all libraries:
import globimport numpy as npimport randomimport librosafrom sklearn.model_selection import train_test_splitfrom sklearn.preprocessing import LabelBinarizerimport kerasfrom keras.layers import LSTM, Dense, Dropout, Flattenfrom keras.models import Sequentialfrom keras.optimizers import Adamfrom keras.callbacks import EarlyStopping, ModelCheckpoint
- Let's set SEED and the location of the .wav files:
SEED = 2017DATA_DIR = 'Data/spoken_numbers_pcm/'
- Let's split the .wav files in a training set and a validation set with scikit-learn's train_test_split function:
files = glob.glob(DATA_DIR + "*.wav")X_train, X_val = train_test_split(files, test_size=0.2, random_state=SEED) ...
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