switch model to 251
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@ -8,13 +8,13 @@
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import http.server
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import http.server
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import urllib
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import urllib
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import keras
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import keras
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from SpeechModel25 import ModelSpeech
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from SpeechModel251 import ModelSpeech
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from LanguageModel import ModelLanguage
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from LanguageModel import ModelLanguage
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datapath = 'data/'
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datapath = 'data/'
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modelpath = 'model_speech/'
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modelpath = 'model_speech/'
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ms = ModelSpeech(datapath)
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ms = ModelSpeech(datapath)
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ms.LoadModel(modelpath + 'm25/speech_model25_e_0_step_545500.model')
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ms.LoadModel(modelpath + 'm251/speech_model251_e_0_step_12000.model')
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ml = ModelLanguage('model_language')
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ml = ModelLanguage('model_language')
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ml.LoadModel()
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ml.LoadModel()
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@ -12,7 +12,7 @@ import tensorflow as tf
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from keras.backend.tensorflow_backend import set_session
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from keras.backend.tensorflow_backend import set_session
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from SpeechModel25 import ModelSpeech
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from SpeechModel251 import ModelSpeech
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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@ -44,7 +44,7 @@ else:
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ms = ModelSpeech(datapath)
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ms = ModelSpeech(datapath)
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ms.LoadModel(modelpath + 'm25/speech_model25_e_0_step_42500.model')
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ms.LoadModel(modelpath + 'm251/speech_model251_e_0_step_42500.model')
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ms.TestModel(datapath, str_dataset='test', data_count = 128, out_report = True)
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ms.TestModel(datapath, str_dataset='test', data_count = 128, out_report = True)
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@ -12,7 +12,7 @@ import tensorflow as tf
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from keras.backend.tensorflow_backend import set_session
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from keras.backend.tensorflow_backend import set_session
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from SpeechModel25 import ModelSpeech
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from SpeechModel251 import ModelSpeech
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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#进行配置,使用95%的GPU
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#进行配置,使用95%的GPU
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@ -43,7 +43,7 @@ else:
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ms = ModelSpeech(datapath)
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ms = ModelSpeech(datapath)
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#ms.LoadModel(modelpath + 'speech_model24_e_0_step_327500.model')
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#ms.LoadModel(modelpath + 'speech_model251_e_0_step_327500.model')
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ms.TrainModel(datapath, epoch = 50, batch_size = 16, save_step = 500)
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ms.TrainModel(datapath, epoch = 50, batch_size = 16, save_step = 500)
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