兼容python3.9及tensorflow2.8等更高版本

README.md

@@ -38,9 +38,9 @@ ASRT是一个基于深度学习的中文语音识别系统，如果您觉得喜
 * 硬盘: 500 GB 机械硬盘(或固态硬盘)
 
 ### 软件
-* Linux: Ubuntu 18.04 + / CentOS 7 +
-* Python: 3.7 +
-* TensorFlow: 2.5 +
+* Linux: Ubuntu 18.04 + / CentOS 7 + 或 Windows 10/11
+* Python: 3.7 - 3.10 及后续版本
+* TensorFlow: 2.5 - 2.11 及后续版本
 
 ## 快速开始
 
@@ -146,18 +146,17 @@ Github本仓库下[Releases](https://github.com/nl8590687/ASRT_SpeechRecognition
 
 ## Python依赖库
 
-* tensorFlow (2.5+)
+* tensorFlow (2.5-2.11+)
 * numpy
 * wave
 * matplotlib
-* math
 * scipy
 * requests
 * flask
 * waitress
 * grpcio / grpcio-tools / protobuf
 
-不会安装环境的同学请直接运行以下命令(前提是有GPU且已经安装好 CUDA 11.2 和 cudnn 8.1)：
+不会安装环境的同学请直接运行以下命令(前提是有GPU且已经安装好 Python3.9、CUDA 11.2 和 cudnn 8.1)：
 
 ```shell
 $ pip install -r requirements.txt

README_EN.md

@@ -34,9 +34,9 @@ This project uses tensorFlow.keras based on deep convolutional neural network an
 * 硬盘: 500 GB HDD(or SSD)
 
 ### Software
-* Linux: Ubuntu 18.04 + / CentOS 7 +
-* Python: 3.7 +
-* TensorFlow: 2.5 +
+* Linux: Ubuntu 18.04 + / CentOS 7 + or Windows 10/11
+* Python: 3.7 - 3.10 and later
+* TensorFlow: 2.5 - 2.11 and later
 
 ## Quick Start
 Take the operation under the Linux system as an example:
@@ -144,7 +144,7 @@ At present, the best model can basically reach 85% of Pinyin correct rate on the
 
 ## Python Dependency Library
 
-* tensorFlow (2.5+)
+* tensorFlow (2.5-2.11+)
 * numpy
 * wave
 * matplotlib
@@ -155,7 +155,7 @@ At present, the best model can basically reach 85% of Pinyin correct rate on the
 * waitress
 * grpcio / grpcio-tools / protobuf
 
-If you have trouble when install those packages, please run the following script to do it as long as you have a GPU and CUDA 11.2 and cudnn 8.1 have been installed：
+If you have trouble when install those packages, please run the following script to do it as long as you have a GPU and python 3.9, CUDA 11.2 and cudnn 8.1 have been installed：
 
 ```shell
 $ pip install -r requirements.txt

data_loader.py

@@ -79,7 +79,7 @@ def get_data_count(self) -> int:
         """
         return len(self.data_list)
 
-    def get_data(self, index:int) -> tuple:
+    def get_data(self, index: int) -> tuple:
         """
         按下标获取一条数据
         """

model_zoo/speech_model/keras_backend.py

@@ -192,7 +192,7 @@ def forward(self, data_input):
 
         in_len[0] = self.output_shape[0]
 
-        x_in = np.zeros((batch_size,) + self.input_shape, dtype=np.float)
+        x_in = np.zeros((batch_size,) + self.input_shape, dtype=np.float64)
 
         for i in range(batch_size):
             x_in[i, 0:len(data_input)] = data_input
@@ -298,10 +298,10 @@ def forward(self, data_input):
 
         in_len[0] = self.output_shape[0]
 
-        x_in = np.zeros((batch_size,) + self.input_shape, dtype=np.float)
+        x_in = np.zeros((batch_size,) + self.input_shape, dtype=np.float64)
 
         for i in range(batch_size):
-            x_in[i,0:len(data_input)] = data_input
+            x_in[i, 0:len(data_input)] = data_input
 
         base_pred = self.model_base.predict(x = x_in)
         r = K.ctc_decode(base_pred, in_len, greedy=True, beam_width=100, top_paths=1)
@@ -398,13 +398,13 @@ def forward(self, data_input):
 
         in_len[0] = self.output_shape[0]
 
-        x_in = np.zeros((batch_size,) + self.input_shape, dtype=np.float)
+        x_in = np.zeros((batch_size,) + self.input_shape, dtype=np.float64)
 
         for i in range(batch_size):
-            x_in[i,0:len(data_input)] = data_input
+            x_in[i, 0:len(data_input)] = data_input
 
-        base_pred = self.model_base.predict(x = x_in)
-        r = K.ctc_decode(base_pred, in_len, greedy = True, beam_width=100, top_paths=1)
+        base_pred = self.model_base.predict(x=x_in)
+        r = K.ctc_decode(base_pred, in_len, greedy=True, beam_width=100, top_paths=1)
 
         if tf.__version__[0:2] == '1.':
             r1 = r[0][0].eval(session=tf.compat.v1.Session())
@@ -469,7 +469,7 @@ def _define_model(self, input_shape, output_size) -> tuple:
         layer_h12 = Dense(output_size, use_bias=True, kernel_initializer='he_normal')(layer_h11) # 全连接层
         y_pred = Activation('softmax', name='Activation0')(layer_h12)
 
-        model_base = Model(inputs = input_data, outputs = y_pred)
+        model_base = Model(inputs=input_data, outputs=y_pred)
         # model_data.summary()
 
         labels = Input(name='the_labels', shape=[label_max_string_length], dtype='float32')
@@ -492,7 +492,7 @@ def forward(self, data_input):
 
         in_len[0] = self.output_shape[0]
 
-        x_in = np.zeros((batch_size,) + self.input_shape, dtype=np.float)
+        x_in = np.zeros((batch_size,) + self.input_shape, dtype=np.float64)
 
         for i in range(batch_size):
             x_in[i, 0:len(data_input)] = data_input

requirements.txt

@@ -1,47 +1,60 @@
-absl-py==0.15.0
-astor==0.8.0
+absl-py==1.4.0
 astunparse==1.6.3
-cached-property==1.5.2
-cachetools==4.2.4
-certifi==2019.9.11
-charset-normalizer==2.0.7
-cycler==0.10.0
-flatbuffers==1.12
-gast==0.4.0
-google-auth==2.3.3
+cachetools==5.3.0
+certifi==2022.12.7
+charset-normalizer==3.0.1
+click==8.1.3
+colorama==0.4.6
+contourpy==1.0.7
+cycler==0.11.0
+Flask==2.2.2
+flatbuffers==23.1.21
+fonttools==4.38.0
+gast==0.5.3
+google-auth==2.16.0
 google-auth-oauthlib==0.4.6
 google-pasta==0.2.0
-grpcio==1.34.1
-h5py==3.1.0
-idna==3.3
-Keras-Applications==1.0.8
-keras-nightly==2.5.0.dev2021032900
+grpcio==1.51.1
+grpcio-tools==1.51.1
+h5py==3.8.0
+idna==3.4
+importlib-metadata==6.0.0
+itsdangerous==2.1.2
+Jinja2==3.1.2
+keras==2.8.0
 Keras-Preprocessing==1.1.2
-kiwisolver==1.3.1
-Markdown==3.1.1
-matplotlib==3.4.2
-numpy==1.19.5
-oauthlib==3.1.1
+kiwisolver==1.4.4
+libclang==15.0.6.1
+Markdown==3.4.1
+MarkupSafe==2.1.2
+matplotlib==3.6.3
+numpy==1.24.1
+oauthlib==3.2.2
 opt-einsum==3.3.0
-Pillow==8.3.2
-protobuf==3.15.0
+packaging==23.0
+Pillow==9.4.0
+protobuf==3.19.6
 pyasn1==0.4.8
 pyasn1-modules==0.2.8
-pyparsing==2.4.7
-python-dateutil==2.8.1
-python-speech-features==0.6
-requests==2.26.0
-requests-oauthlib==1.3.0
-rsa==4.7.2
-scipy==1.6.3
-six==1.15.0
-tensorboard==2.7.0
+pyparsing==3.0.9
+python-dateutil==2.8.2
+requests==2.28.2
+requests-oauthlib==1.3.1
+rsa==4.9
+scipy==1.10.0
+six==1.16.0
+tensorboard==2.8.0
 tensorboard-data-server==0.6.1
-tensorboard-plugin-wit==1.8.0
-tensorflow-estimator==2.5.0
-tensorflow-gpu==2.5.3
-termcolor==1.1.0
-typing-extensions==3.7.4.3
-urllib3==1.26.7
-Werkzeug==0.16.0
-wrapt==1.12.1
+tensorboard-plugin-wit==1.8.1
+tensorflow-estimator==2.8.0
+tensorflow-gpu==2.8.4
+tensorflow-io-gcs-filesystem==0.30.0
+termcolor==2.2.0
+typing_extensions==4.4.0
+urllib3==1.26.14
+waitress==2.1.2
+Wave==0.0.2
+Werkzeug==2.2.2
+wincertstore==0.2
+wrapt==1.14.1
+zipp==3.12.0

speech_features/speech_features.py

@@ -78,7 +78,7 @@ def run(self, wavsignal, fs=16000):
 
         :returns: A numpy array of size (NUMFRAMES by numcep * 3) containing features. Each row holds 1 feature vector.
         """
-        wavsignal = np.array(wavsignal, dtype=np.float)
+        wavsignal = np.array(wavsignal, dtype=np.float64)
         # 获取输入特征
         feat_mfcc = mfcc(wavsignal[0], samplerate=self.framesamplerate, winlen=self.winlen,
                          winstep=self.winstep, numcep=self.numcep, nfilt=self.nfilt, preemph=self.preemph)
@@ -99,7 +99,7 @@ def __init__(self, framesamplerate=16000, nfilt=26):
         super().__init__(framesamplerate)
 
     def run(self, wavsignal, fs=16000):
-        wavsignal = np.array(wavsignal, dtype=np.float)
+        wavsignal = np.array(wavsignal, dtype=np.float64)
         # 获取输入特征
         wav_feature = logfbank(wavsignal, fs, nfilt=self.nfilt)
         return wav_feature
@@ -140,8 +140,8 @@ def run(self, wavsignal, fs=16000):
         # wav_length = wav_arr.shape[1]
 
         range0_end = int(len(wavsignal[0]) / fs * 1000 - time_window) // 10 + 1  # 计算循环终止的位置，也就是最终生成的窗数
-        data_input = np.zeros((range0_end, window_length // 2), dtype=np.float)  # 用于存放最终的频率特征数据
-        data_line = np.zeros((1, window_length), dtype=np.float)
+        data_input = np.zeros((range0_end, window_length // 2), dtype=np.float64)  # 用于存放最终的频率特征数据
+        data_line = np.zeros((1, window_length), dtype=np.float64)
 
         for i in range(0, range0_end):
             p_start = i * 160
@@ -192,8 +192,8 @@ def run(self, wavsignal, fs=16000):
         # wav_length = wav_arr.shape[1]
 
         range0_end = int(len(wavsignal[0]) / fs * 1000 - time_window) // 10 + 1  # 计算循环终止的位置，也就是最终生成的窗数
-        data_input = np.zeros((range0_end, window_length // 2), dtype=np.float)  # 用于存放最终的频率特征数据
-        data_line = np.zeros((1, window_length), dtype=np.float)
+        data_input = np.zeros((range0_end, window_length // 2), dtype=np.float64)  # 用于存放最终的频率特征数据
+        data_line = np.zeros((1, window_length), dtype=np.float64)
 
         for i in range(0, range0_end):
             p_start = i * 160

speech_model.py

@@ -54,12 +54,12 @@ def _data_generator(self, batch_size, data_loader):
         数据生成器函数，用于Keras的generator_fit训练
         batch_size: 一次产生的数据量
         """
-        labels = np.zeros((batch_size, 1), dtype=np.float)
+        labels = np.zeros((batch_size, 1), dtype=np.float64)
         data_count = data_loader.get_data_count()
         index = 0
 
         while True:
-            X = np.zeros((batch_size,) + self.speech_model.input_shape, dtype=np.float)
+            X = np.zeros((batch_size,) + self.speech_model.input_shape, dtype=np.float64)
             y = np.zeros((batch_size, self.max_label_length), dtype=np.int16)
             input_length = []
             label_length = []
@@ -233,7 +233,7 @@ def recognize_speech(self, wavsignal, fs):
         """
         # 获取输入特征
         data_input = self.speech_features.run(wavsignal, fs)
-        data_input = np.array(data_input, dtype=np.float)
+        data_input = np.array(data_input, dtype=np.float64)
         # print(data_input,data_input.shape)
         data_input = data_input.reshape(data_input.shape[0], data_input.shape[1], 1)
         r1 = self.predict(data_input)