<gh_stars>0
import platform as plat
import os

import tensorflow as tf
from keras.backend.tensorflow_backend import set_session


from SpeechModel import ModelSpeech
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
#进行配置，使用90%的GPU
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
#config.gpu_options.allow_growth=True   #不全部占满显存, 按需分配
set_session(tf.Session(config=config))


datapath = ''
modelpath = 'model_speech'


if(not os.path.exists(modelpath)): # 判断保存模型的目录是否存在
	os.makedirs(modelpath) # 如果不存在，就新建一个，避免之后保存模型的时候炸掉

system_type = plat.system() # 由于不同的系统的文件路径表示不一样，需要进行判断
if(system_type == 'Windows'):
	datapath = 'E:\\语音数据集'
	modelpath = modelpath + '\\'
elif(system_type == 'Linux'):
	datapath = 'dataset'
	modelpath = modelpath + '/'
else:
	print('*[Message] Unknown System\n')
	datapath = 'dataset'
	modelpath = modelpath + '/'

ms = ModelSpeech(datapath)

ms.LoadModel(modelpath + 'm251/speech_model251_e_0_step_42500.model')

ms.TestModel(datapath, str_dataset='test', data_count = 128, out_report = True)

#r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00241I0053.wav')
#r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00020I0087.wav')
#r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\wav\\train\\A11\\A11_167.WAV')
#r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\wav\\test\\D4\\D4_750.wav')
#print('*[提示] 语音识别结果：\n',r)

import platform as plat
import os

import tensorflow as tf
from keras.backend.tensorflow_backend import set_session


from SpeechModel import ModelSpeech
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
#进行配置，使用90%的GPU
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
#config.gpu_options.allow_growth=True   #不全部占满显存, 按需分配
set_session(tf.Session(config=config))


datapath = ''
modelpath = 'model_speech'


if(not os.path.exists(modelpath)): # 判断保存模型的目录是否存在
	os.makedirs(modelpath) # 如果不存在，就新建一个，避免之后保存模型的时候炸掉

system_type = plat.system() # 由于不同的系统的文件路径表示不一样，需要进行判断
if(system_type == 'Windows'):
	datapath = 'E:\\语音数据集'
	modelpath = modelpath + '\\'
elif(system_type == 'Linux'):
	datapath = 'dataset'
	modelpath = modelpath + '/'
else:
	print('*[Message] Unknown System\n')
	datapath = 'dataset'
	modelpath = modelpath + '/'

ms = ModelSpeech(datapath)

ms.LoadModel(modelpath + 'm251/speech_model251_e_0_step_42500.model')

ms.TestModel(datapath, str_dataset='test', data_count = 128, out_report = True)

#r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00241I0053.wav')
#r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00020I0087.wav')
#r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\wav\\train\\A11\\A11_167.WAV')
#r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\wav\\test\\D4\\D4_750.wav')
#print('*[提示] 语音识别结果：\n',r)
#进行配置，使用90%的GPU #config.gpu_options.allow_growth=True   #不全部占满显存, 按需分配 # 判断保存模型的目录是否存在 # 如果不存在，就新建一个，避免之后保存模型的时候炸掉 # 由于不同的系统的文件路径表示不一样，需要进行判断 #r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00241I0053.wav') #r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00020I0087.wav') #r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\wav\\train\\A11\\A11_167.WAV') #r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\wav\\test\\D4\\D4_750.wav') #print('*[提示] 语音识别结果：\n',r)