def asr_mandarin(req_file: "UploadedFile"):
load_data(req_file)
keras.backend.clear_session()
ms = ModelSpeech(asrPath)
ms.LoadModel(modelpath + 'speech_model251_e_0_step_625000.model')
r = ms.RecognizeSpeech_FromFile(filePath)
ml = ModelLanguage('model_language')
ml.LoadModel()
str_pinyin = r
r = ml.SpeechToText(str_pinyin)
return r
def __init__(self):
super(App, self).__init__()
self.record_name = 'saved_record.wav'
self.ms = ModelSpeech('dataset')
self.ms.LoadModel('model_speech/speech_model251_e_0_step_12000.model')
self.ml = ModelLanguage('model_language')
self.ml.LoadModel()
self.title = 'ASR demo'
self.left = 10
self.top = 10
self.width = 420
self.height = 400
self.rec = Recorder()
#self.rec.start_thread()
self.initUI()
self.rec.start()
def speech_recognition(f):
datapath = '.'
modelpath = 'model_speech' + '\\'
ms = ModelSpeech(datapath)
ms.LoadModel(modelpath + 'speech_model251_e_0_step_625000.model')
r = ms.RecognizeSpeech_FromFile(f)
K.clear_session()
print('*[提示] 语音识别结果：\n', r)
ml = ModelLanguage('model_language')
ml.LoadModel()
str_pinyin = r
r = ml.SpeechToText(str_pinyin)
print('语音转文字结果：\n', r)
return r
def predict(video_file):
from SpeechModel251 import ModelSpeech
from LanguageModel2 import ModelLanguage
from keras import backend as K
datapath = 'dataset'
modelpath = 'model_speech'
ms = ModelSpeech(datapath)
ms.LoadModel(modelpath + '/m251/speech_model251_e_0_step_60500.model')
pinyin = ms.RecognizeSpeech_FromFile(video_file)
K.clear_session()
ml = ModelLanguage('model_language')
ml.LoadModel()
str_pinyin = pinyin
text = ml.SpeechToText(str_pinyin)
return pinyin, text
def CTC_tf(current_path):
datapath = ''
modelpath = 'model_speech'
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 = current_path
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 + 'speech_model251_e_0_step_12000.model')
#ms.TestModel(datapath, str_dataset='test', data_count = 64, out_report = True)
rr = ms.RecognizeSpeech_FromFile(current_path + '\\chunk-00.wav')
print('*[提示] 语音识别结果：\n', rr)
ml = ModelLanguage('model_language')
ml.LoadModel()
#str_pinyin = ['zhe4','zhen1','shi4','ji2', 'hao3','de5']
#str_pinyin = ['jin1', 'tian1', 'shi4', 'xing1', 'qi1', 'san1']
#str_pinyin = ['ni3', 'hao3','a1']
str_pinyin = rr
#str_pinyin = ['su1', 'bei3', 'jun1', 'de5', 'yi4','xie1', 'ai4', 'guo2', 'jiang4', 'shi4', 'ma3', 'zhan4', 'shan1', 'ming2', 'yi1', 'dong4', 'ta1', 'ju4', 'su1', 'bi3', 'ai4', 'dan4', 'tian2','mei2', 'bai3', 'ye3', 'fei1', 'qi3', 'kan4', 'zhan4']
r = ml.SpeechToText(str_pinyin)
print('语音转文字结果：\n', r)
ctc_result = hanzi_pinyin(r)
return ctc_result
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: nl8590687
用于测试整个一套语音识别系统的程序
语音模型 + 语言模型
"""
import platform as plat
from SpeechModel251 import ModelSpeech
from keras import backend as K
import librosa
import soundfile as sf
modelpath = 'model_speech/'
datapath = '.'
ms = ModelSpeech(datapath)
ms.LoadModel(modelpath + 'speech_model251_e_0_step_625000.model')
utt_dir = '/home/bicbrv/project/GREAT_audio_demo_server_new/formal_data/enrollment/#0008_智勇/智勇_0.wav'
wavsignal, sr = librosa.load(utt_dir, sr=16000)
sf.write('testing_asr.wav', wavsignal, sr)
r = ms.RecognizeSpeech_FromFile('testing_asr.wav')
#K.clear_session()
print('*[提示] 语音识别结果：\n', r)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: nl8590687
语音识别API的HTTP服务器程序
"""
import http.server
import urllib
import keras
from SpeechModel251 import ModelSpeech
datapath = 'I:\\python_speech_file'
modelpath = 'model_speech\\'
ms = ModelSpeech(datapath)
ms.LoadModel(modelpath + 'm251\\speech_model251_e_0_step_82500.model')
class TestHTTPHandle(http.server.BaseHTTPRequestHandler):
def setup(self):
self.request.settimeout(10)
http.server.BaseHTTPRequestHandler.setup(self)
def _set_response(self):
self.send_response(200)
self.send_header('Content-type', 'text/html')
self.end_headers()
def do_GET(self):
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 = 'C:\\Users\\农夫三拳\\PycharmProjects\\Test\\ASRT_SpeechRecognition-master\\dataset'
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_4000.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('/home/ASRT_SpeechRecognition-master/dataset/ST-CMDS-20170001_1-OS/20170001P00001A0002.wav')
print('*[提示] 语音识别结果：\n',r)
return name
if (modelType == "2"):
name = 'show/yq-speech_model251_e_0_step_4800.model'
return name
if (modelType == "3"):
name = 'show/hq1speech_model251_e_0_step_410.model'
return name
if (modelType == "4"):
name = 'show/hq2-speech_model251_e_0_step_500.model'
return name
else:
name = '类型输入不正确，请核查。'
return name
ms = ModelSpeech(datapath)
print(
'\n*成人语音模型：type=0 \n*直接训练模型：type=1 \n*基于成人模型训练的模型：type=2 \n*迁移学习1.0模型 type=3 \n*迁移学习2.0模型 type=4 \n'
)
print('-----请输入您想调用的模型类型。-----')
modelType = input("**type**：") # 0 1 2 3 4
name = pickModel(modelType)
if name == "类型输入不正确，请核查。":
print("模型的类型输入不符合要求，请核查。")
else:
ms.LoadModel(modelpath + name)
r = ms.RecognizeSpeech_FromFile("./dataset/data_child/test/A5_75.wav")
K.clear_session()
@app.route('/result/')
def show_result():
rs = func_show(r)
return render_template('show_result.html', result=rs)
@app.route('/test/')
def show_test():
with g.as_default():
r_test = ms.RecognizeSpeech_FromFile('F:\\语音数据集\\test.wav')
str_pinyin = r_test
r_test = ml.SpeechToText(str_pinyin)
print("testtesttest")
# rs = func_test(r_test)
return render_template('show_test.html', result=r_test)
# return 'ge'
if __name__ == '__main__':
g = tf.Graph()
with g.as_default():
ms = ModelSpeech(datapath)
ms.LoadModel(modelpath + 'speech_model251_e_0_step_12000.model')
# r = ms.RecognizeSpeech_FromFile('F:\\语音数据集\\data_thchs30\\test\\D4_750.wav')
r = ms.RecognizeSpeech_FromFile(
'F:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00136I0088.wav')
r_test = ms.RecognizeSpeech_FromFile('F:\\语音数据集\\test.wav')
ml = ModelLanguage('model_language')
ml.LoadModel()
app.run(debug=True)
#!/usr/bin/env python3
# encoding: utf-8
import logging
import os
from flask import request, Blueprint, abort, jsonify
from werkzeug import secure_filename
from LanguageModel import ModelLanguage
from SpeechModel251 import ModelSpeech
data_path = 'data/train_data/'
ms = ModelSpeech(data_path)
ms.LoadModel('data/speech_model/speech_model251_e_0_step_12000.model')
ml = ModelLanguage('data/model_language/')
ml.LoadModel()
detect_speech_api = Blueprint('detect_language_api',
__name__,
template_folder='templates')
ALLOWED_EXTENSIONS = set(['txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'])
def allowed_file(filename):
return '.' in filename and \
filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS
@detect_speech_api.route('/language/recognize/chinese/offline',
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 + 'speech_model251_e_0_step_12000.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)
datapath = ''
modelpath = 'model_speech'
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 = 'dataset'
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 + 'm22_2\\0\\speech_model22_e_0_step_257000.model')
ms.LoadModel(modelpath + 'speech_model251_e_0_step_625000.model')
ml = ModelLanguage('model_language')
#ms.TestModel(datapath, str_dataset='test', data_count = 64, out_report = True)
#r = ms.RecognizeSpeech_FromFile('E:\\github\\ASRT_SpeechRecognition\\dataset\\ST-CMDS-20170001_1-OS\\20170001P00241I0052.wav')
#r = ms.RecognizeSpeech_FromFile('D:\语音数据集\ST-CMDS-20170001_1-OS\\20170001P00241I0053.wav')
#r = ms.RecognizeSpeech_FromFile('D:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00020I0087.wav')
#r = ms.RecognizeSpeech_FromFile('D:\\语音数据集\\data_thchs30\\data\\A11_167.WAV')
#r = ms.RecognizeSpeech_FromFile('D:\\语音数据集\\data_thchs30\\data\\D4_750.wav')
for speech_file in sorted(speech_files):
print(speech_file)
ml.LoadModel()
r = ms.RecognizeSpeech_FromFile(speech_file)
from LanguageModel2 import ModelLanguage
from keras import backend as K
datapath = ''
modelpath = 'model_speech'
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 = 'D:\\语音数据集'
elif (system_type == 'Linux'):
datapath = 'dataset'
else:
print('*[Message] Unknown System\n')
datapath = 'dataset'
ms = ModelSpeech(datapath)
#ms.LoadModel(os.path.join(modelpath, 'm22_2', '0', 'speech_model22_e_0_step_257000.model'))
ms.LoadModel(
os.path.join(modelpath, 'm251', 'speech_model251_e_0_step_12000.model'))
#ms.TestModel(datapath, str_dataset='test', data_count = 64, out_report = True)
r = ms.RecognizeSpeech_FromFile(
'D:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00241I0052.wav')
#r = ms.RecognizeSpeech_FromFile('D:\语音数据集\ST-CMDS-20170001_1-OS\\20170001P00241I0053.wav')
#r = ms.RecognizeSpeech_FromFile('D:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00020I0087.wav')
#r = ms.RecognizeSpeech_FromFile('D:\\语音数据集\\data_thchs30\\data\\A11_167.WAV')
#r = ms.RecognizeSpeech_FromFile('D:\\语音数据集\\data_thchs30\\data\\D4_750.wav')
K.clear_session()
datapath = ''
modelpath = 'model_speech'
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 = 'Z:\\SpeechData'
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 + 'm22_2\\0\\speech_model22_e_0_step_257000.h5')
ms.LoadModel(modelpath + 'm251\\speech_model251_e_0_step_625000.h5')
#ms.TestModel(datapath, str_dataset='test', data_count = 64, out_report = True)
r = ms.RecognizeSpeech_FromFile(
'D:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00241I0052.wav')
#r = ms.RecognizeSpeech_FromFile('D:\语音数据集\ST-CMDS-20170001_1-OS\\20170001P00241I0053.wav')
#r = ms.RecognizeSpeech_FromFile('D:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00020I0087.wav')
#r = ms.RecognizeSpeech_FromFile('D:\\语音数据集\\data_thchs30\\data\\A11_167.WAV')
#r = ms.RecognizeSpeech_FromFile('D:\\语音数据集\\data_thchs30\\data\\D4_750.wav')
K.clear_session()
print('*[提示] 语音识别结果：\n', r)
# -*- coding: utf-8 -*-
"""
@author: nl8590687
语音识别API的HTTP服务器程序
"""
import http.server
import urllib
import keras
import os
from SpeechModel251 import ModelSpeech
from LanguageModel import ModelLanguage
datapath = './'
modelpath = 'model_speech/'
ms = ModelSpeech(datapath)
ms.LoadModel(
os.path.join(modelpath, 'm251', 'speech_model251_e_0_step_12000.model'))
ml = ModelLanguage('model_language')
ml.LoadModel()
class TestHTTPHandle(http.server.BaseHTTPRequestHandler):
def setup(self):
self.request.settimeout(10)
http.server.BaseHTTPRequestHandler.setup(self)
def _set_response(self):
self.send_response(200)
self.send_header('Content-type', 'text/html')
datapath = ''
modelpath = 'model_speech'
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 + 'm22_2\\0\\speech_model22_e_0_step_257000.model')
ms.LoadModel(modelpath + 'speech_model26_e_0_step_22500.model')
# ms.TestModel(datapath, str_dataset='test', data_count = 64, out_report = True)
# r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00241I0052.wav')
# 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(
'/media/yean/BE6423686423231F/record/通用语料（不好意思给您带来不便，我们查看一下或到时重新给您补卡）.wav')
print('*[提示] 语音识别结果：\n', r)
ml = ModelLanguage('model_language')
datapath = ''
modelpath = 'model_speech'
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 + 'speech_model251_e_0_step_12000.model')
#ms.TestModel(datapath, str_dataset='test', data_count = 64, out_report = True)
#r = ms.RecognizeSpeech_FromFile('E:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00241I0052.wav')
#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('output.wav')#'E:\\语音数据集\\wav\\test\\D4\\D4_750.wav')
print('*[提示] 语音识别结果：\n',r)
ml = ModelLanguage('model_language')
ml.LoadModel()
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: nl8590687
语音识别API的HTTP服务器程序
"""
import http.server
import urllib
import tensorflow.keras
from SpeechModel251 import ModelSpeech
from LanguageModel import ModelLanguage
datapath = './'
modelpath = 'model_speech/'
ms = ModelSpeech(datapath)
ms.LoadModel(modelpath + 'm251/speech_model251_e_0_step_12000.model')
ml = ModelLanguage('model_language')
ml.LoadModel()
class TestHTTPHandle(http.server.BaseHTTPRequestHandler):
def setup(self):
self.request.settimeout(10)
http.server.BaseHTTPRequestHandler.setup(self)
def _set_response(self):
self.send_response(200)
self.send_header('Content-type', 'text/html')
self.end_headers()
#进行配置，使用95%的GPU
config = tf.compat.v1.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.95
#config.gpu_options.allow_growth=True #不全部占满显存, 按需分配
sess = tf.compat.v1.Session(config=config)
tf.compat.v1.keras.backend.set_session(sess)
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
os.makedirs(modelpath + '/m' + ModelName)
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 = 'D:\\SpeechData'
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 + 'speech_model251_e_0_step_327500.h5')
ms.TrainModel(datapath, epoch=50, batch_size=16, save_step=500)
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
#进行配置，使用95%的GPU
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.95
#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_27000.model')
print("Model loaded")
ms.TrainModel(datapath, epoch=50, batch_size=16, save_step=500)
datapath = ''
modelpath = 'model_speech'
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 = '.'
modelpath = modelpath + '\\'
elif (system_type == 'Linux'):
datapath = '.'
modelpath = modelpath + '/'
else:
print('*[Message] Unknown System\n')
datapath = 'dataset'
modelpath = modelpath + '/'
ms = ModelSpeech(datapath)
ms.LoadModel(modelpath + 'speech_model251_e_0_step_625000.model')
#ms.TestModel(datapath, str_dataset='test', data_count = 64, out_report = True)
r = ms.RecognizeSpeech_FromFile(
'F:\\文件\\Tencent Files\\904292841\\FileRecv\\904292841\\FileRecv\\真实语音数据\\21-30\\22\\22_03.wav'
)
K.clear_session()
print('*[提示] 语音识别结果：\n', r)
ml = ModelLanguage('model_language')
ml.LoadModel()
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 = 'I:\\python_speech_file'
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_79500.model')
ms.TestModel(datapath, str_dataset='dev', data_count=256, out_report=True)
#for index in range(10):
#ms.TestModel(datapath, str_dataset='test', data_count = 512, out_report = True)
#print(rate/10)
#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)
datapath = ''
modelpath = 'model_speech'
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 = '.'
modelpath = modelpath + '\\'
elif (system_type == 'Linux'):
datapath = '.'
modelpath = modelpath + '/'
else:
print('*[Message] Unknown System\n')
datapath = 'dataset'
modelpath = modelpath + '/'
ms = ModelSpeech(datapath)
ms.LoadModel(modelpath + 'speech_model251_e_0_step_625000.model')
#ms.TestModel(datapath, str_dataset='test', data_count = 64, out_report = True)
r = ms.RecognizeSpeech_FromFile('C:\\Users\\hezudao\\Desktop\\hello.wav')
K.clear_session()
print('*[提示] 语音识别结果：\n', r)
ml = ModelLanguage('model_language')
ml.LoadModel()
str_pinyin = r
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: nl8590687
语音识别API的HTTP服务器程序
"""
import http.server
import urllib
import keras
from SpeechModel251 import ModelSpeech
from LanguageModel2 import ModelLanguage
datapath = './'
modelpath = 'model_speech/'
ms = ModelSpeech(datapath)
ms.LoadModel(modelpath + 'speech_model251_e_0_step_625000.model')
ml = ModelLanguage('model_language')
ml.LoadModel()
class TestHTTPHandle(http.server.BaseHTTPRequestHandler):
def setup(self):
self.request.settimeout(10)
http.server.BaseHTTPRequestHandler.setup(self)
def _set_response(self):
self.send_response(200)
self.send_header('Content-type', 'text/html')
self.end_headers()
datapath = ''
modelpath = 'model_speech'
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 = 'Z:\\dataset'
modelpath = modelpath + '\\'
elif (system_type == 'Linux'):
datapath = '../dataset' #xuyao xiugai de lujing
modelpath = modelpath + '/'
else:
print('*[Message] Unknown System\n')
datapath = 'dataset'
modelpath = modelpath + '/'
ms = ModelSpeech(datapath)
#ms.LoadModel(modelpath + 'm22_2\\0\\speech_model22_e_0_step_257000.model')
ms.LoadModel(modelpath + 'm251/speech_model251_e_0_step_625000.model')
#ms.TestModel(datapath, str_dataset='test', data_count = 64, out_report = True)
r = ms.RecognizeSpeech_FromFile(
'../dataset/ST-CMDS-20170001_1-OS/20170001P00241I0052.wav')
#r = ms.RecognizeSpeech_FromFile('D:\dataset\ST-CMDS-20170001_1-OS\\20170001P00241I0053.wav')
#r = ms.RecognizeSpeech_FromFile('D:\\dataset\\ST-CMDS-20170001_1-OS\\20170001P00020I0087.wav')
#r = ms.RecognizeSpeech_FromFile('D:\\dataset\\data_thchs30\\data\\A11_167.WAV')
#r = ms.RecognizeSpeech_FromFile('D:\\dataset\\data_thchs30\\data\\D4_750.wav')
K.clear_session()
print('*[提示] 语音识别结果：\n', r)
#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'
elif (system_type == 'Linux'):
datapath = 'dataset'
else:
print('*[Message] Unknown System\n')
datapath = 'dataset'
ms = ModelSpeech(datapath)
ms.LoadModel(
os.path.join(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)
class App(QWidget):
def __init__(self):
super(App, self).__init__()
self.record_name = 'saved_record.wav'
self.ms = ModelSpeech('dataset')
self.ms.LoadModel('model_speech/speech_model251_e_0_step_12000.model')
self.ml = ModelLanguage('model_language')
self.ml.LoadModel()
self.title = 'ASR demo'
self.left = 10
self.top = 10
self.width = 420
self.height = 400
self.rec = Recorder()
#self.rec.start_thread()
self.initUI()
self.rec.start()
#self.setWindowTitle("ASR demo")
def initUI(self):
self.setWindowTitle(self.title)
self.setGeometry(self.left, self.top, self.width, self.height)
button = QPushButton('Record', self)
button.setToolTip('Press to start recording')
button.move(100, 70)
button.clicked.connect(self.start_record)
button = QPushButton('To Transcript', self)
button.setToolTip('Press to convert to transcript')
button.move(200, 70)
button.clicked.connect(self.stop_record)
button = QPushButton('Clear', self)
button.setToolTip('Press to clear transcripts')
button.move(100, 100)
button.clicked.connect(self.clear)
self.text_edit = QTextEdit("What you said: ", self)
self.text_edit.setReadOnly(True)
self.text_edit.move(100, 140)
#self.results=QLabel(self)
#self.results.move(100,140)
self.show()
@pyqtSlot()
def clear(self):
self.text_edit.clear()
self.text_edit.append("What you said: ")
@pyqtSlot()
def start_record(self):
self.rec.start()
#print('PyQt5 button click')
#self.rec.start()
@pyqtSlot()
def stop_record(self):
print(len(self.rec._frames))
#print('PyQt5 button click')
self.rec.stop()
self.rec.save(self.record_name)
r = self.ms.RecognizeSpeech_FromFile(self.record_name)
self.w = self.ml.SpeechToText(r)
print('语音转文字结果：\n', self.w)
self.text_edit.append(self.w)
datapath = ''
modelpath = 'model_speech'
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 = 'C:\\test'
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 + 'speech_model251_e_0_step_12000.model')
#ms.TestModel(datapath, str_dataset='test', data_count = 64, out_report = True)
r = ms.RecognizeSpeech_FromFile('C:\\test\\online_star.wav')
print('*[提示] 语音识别结果：\n', r)
ml = ModelLanguage('model_language')
ml.LoadModel()
#str_pinyin = ['zhe4','zhen1','shi4','ji2', 'hao3','de5']
#str_pinyin = ['jin1', 'tian1', 'shi4', 'xing1', 'qi1', 'san1']
#str_pinyin = ['ni3', 'hao3','a1']
datapath = ''
modelpath = 'model_speech'
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 = 'D:\\AISHELL-2-sample\\iOS\\data'
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 + 'm22_2\\0\\speech_model22_e_0_step_257000.model')
ms.LoadModel(modelpath + 'm251\\speech_model251_e_0_step_82500.model')
i = 0
#ms.TestModel(datapath, str_dataset='test', data_count = 64, out_report = True)
datapath = datapath + '\\wav\\C0936\\IC0936W0001.wav'
#r = ms.RecognizeSpeech_FromFile(datapath)
#r = ms.RecognizeSpeech_FromFile('D:\语音数据集\ST-CMDS-20170001_1-OS\\20170001P00241I0053.wav')
#r = ms.RecognizeSpeech_FromFile('D:\\语音数据集\\ST-CMDS-20170001_1-OS\\20170001P00020I0087.wav')
#r = ms.RecognizeSpeech_FromFile('D:\\语音数据集\\data_thchs30\\data\\A11_167.WAV')
r = ms.RecognizeSpeech_FromFile(
'D:\\AISHELL-2-sample\\iOS\\data\\wav\\C0936\\IC0936W0001.wav')
print('*[提示] 语音识别结果：\n', r)
K.clear_session()
