是呀，不过感觉好像资料不太多呀

搞语音识别啦～～

1. def train():
       # x_data ,y_data = pr.read_all_data(config.file_name,config.train_list)
       
       with fluid.dygraph.guard(place = fluid.CUDAPlace(0)):  #GPU:    place = fluid.CUDAPlace(0)
           #定义数据层
           speech = fluid.layers.data(name='speech',shape=[78,500,500],dtype='float32')
           label = fluid.layers.data(name='label',shape=[config.y_max_num,1440],dtype='int64')
           data1 = fluid.layers.fill_constant(shape=[config.batch_size], value=60, dtype='int64')
           data2 = fluid.layers.fill_constant(shape=[config.batch_size], value=60, dtype='int64')
           train_reader = paddle.batch(pr.custom_reader(config.file_name,config.train_list),batch_size=config.batch_size,drop_last=True)
           
           vgg = V.VGG()
           try:
               model,_ = fluid.dygraph.load_dygraph("vgg")
               vgg.load_dict(model)
           except:
               print("未加载模型")
           #优化器
           # optimizer = fluid.optimizer.AdamOptimizer(learning_rate=config.learning_rate,parameter_list=vgg.parameters())
           optimizer = fluid.optimizer.SGD(learning_rate=config.learning_rate,parameter_list=vgg.parameters())
           
           for epoch_num in range(config.epoch_num):
               for batch_id,data in enumerate(train_reader()):
                   dy_x_data = np.array([x[0] for x in data]).astype('float32')
                   y_data = np.array([x[1] for x in data]).astype('int64')
                   # y_data = y_data.transpose(1,0)
                   y_data = y_data[:,:,np.newaxis]
                   # print(y_data.shape,"y_dataaaaaaaaaaaaa")
                   speech = fluid.dygraph.to_variable(dy_x_data)
                   label = fluid.dygraph.to_variable(y_data)
                   label.stop_gradient = True
                   
                   out = vgg(speech)
                   # print(out[0,0,:],"outttttttttttttttttttttttt")
                   # print(label.shape,"labellllllllll")
                   # print(out[0,0,:],"outttttttttttttttttt")
                   #损失
                   loss = fluid.layers.cross_entropy(input=out, label=label)
                   # loss= fluid.layers.warpctc(input=out, label=label,input_length=data1,label_length=data2)
                   # print(loss,"lossssssssssssssssssssssss")
                   avg_loss = fluid.layers.mean(loss)
                   #反馈传递
                   avg_loss.backward()
                   optimizer.minimize(avg_loss)
                   
                   vgg.clear_gradients()
                   
                   if batch_id % 1 == 0:
                      print("Loss at epoch {} step {}: {}".format(epoch_num, batch_id, avg_loss.numpy()))
               fluid.save_dygraph(vgg.state_dict(),"vgg")
                   
   if __name__ =='__main__' :
       
       train()
       
       

   已经解决啦

我想要解决，大佬看一眼吧

有哪位大佬可以解决一下