У меня возникла странная проблема при построении модели преобразования речи в текст.
Модель обучена и создана, но когда я пытаюсь сохранить ее как файл hdf5, чтобы повторно использовать ее для в целях тестирования операция просто не работает, и в консоли не выскакивают ошибки…
Вот полный код:
import os
import librosa
import IPython.display as ipd
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import wavfile
import warnings
from sklearn.preprocessing import LabelEncoder
from keras.utils import np_utils
from sklearn.model_selection import train_test_split
from keras.layers import Dense, Dropout, Flatten, Conv1D, Input, MaxPooling1D
from keras.models import Model
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras import backend as K
from keras.models import load_model
import random
warnings.filterwarnings("ignore")
samples, sample_rate = librosa.load(r'C:\Users\makra\OneDrive\Desktop\Conda\Speech-to-text\train\audio\yes\0a7c2a8d_nohash_0.wav', sr = 16000)
ipd.Audio(samples, rate=sample_rate)
samples = librosa.resample(samples, sample_rate, 8000)
ipd.Audio(samples, rate=8000)
labels=os.listdir(r'C:\Users\makra\OneDrive\Desktop\Conda\Speech-to-text\train\audio')
labels=["yes", "no", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine", "zero"]
all_wave = []
all_label = []
for label in labels:
print(label)
waves = [f for f in os.listdir(r'C:\Users\makra\OneDrive\Desktop\Conda\Speech-to-text\train\audio' + '/'+ label) if f.endswith('.wav')]
for wav in waves:
samples, sample_rate = librosa.load(r'C:\Users\makra\OneDrive\Desktop\Conda\Speech-to-text\train\audio' + '/' + label + '/' + wav, sr = 16000)
samples = librosa.resample(samples, sample_rate, 8000)
if(len(samples)== 8000) :
all_wave.append(samples)
all_label.append(label)
le = LabelEncoder()
y=le.fit_transform(all_label)
classes= list(le.classes_)
y=np_utils.to_categorical(y, num_classes=len(labels))
all_wave = np.array(all_wave).reshape(-1,8000,1)
x_tr, x_val, y_tr, y_val = train_test_split(np.array(all_wave),np.array(y),stratify=y,test_size = 0.2,random_state=777,shuffle=True)
K.clear_session()
inputs = Input(shape=(8000,1))
#First Conv1D layer
conv = Conv1D(8,13, padding='valid', activation='relu', strides=1)(inputs)
conv = MaxPooling1D(3)(conv)
conv = Dropout(0.3)(conv)
#Second Conv1D layer
conv = Conv1D(16, 11, padding='valid', activation='relu', strides=1)(conv)
conv = MaxPooling1D(3)(conv)
conv = Dropout(0.3)(conv)
#Third Conv1D layer
conv = Conv1D(32, 9, padding='valid', activation='relu', strides=1)(conv)
conv = MaxPooling1D(3)(conv)
conv = Dropout(0.3)(conv)
#Fourth Conv1D layer
conv = Conv1D(64, 7, padding='valid', activation='relu', strides=1)(conv)
conv = MaxPooling1D(3)(conv)
conv = Dropout(0.3)(conv)
#Flatten layer
conv = Flatten()(conv)
#Dense Layer 1
conv = Dense(256, activation='relu')(conv)
conv = Dropout(0.3)(conv)
#Dense Layer 2
conv = Dense(128, activation='relu')(conv)
conv = Dropout(0.3)(conv)
outputs = Dense(len(labels), activation='softmax')(conv)
model = Model(inputs, outputs)
model.summary()
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10, min_delta=0.0001)
mc = ModelCheckpoint('best_model.hdf5', monitor='val_acc', verbose=1, save_best_only=True, mode='max')
history=model.fit(x_tr, y_tr ,epochs=100, callbacks=[es,mc], batch_size=32, validation_data=(x_val,y_val))
Большое спасибо за вашу помощь,
макрам