我的程序运行得很好，但我遇到了速度问题。响应和给出结果需要太长时间。运行 2,3 次后，需要比以前更多的时间。此外，启动还需要很多时间。提高程序运行速度的方法有哪些？我是这个领域的新手。
# Imported speech recognition as sr, pyttsx3, pywhatkit, datetime, pyjokes, os
listener = sr.Recognizer()
engine = pyttsx3.init()
def talk(text):
engine.say(text)
engine.runAndWait()
talk('Hello what can i do for you')
def take_command():
try:
with sr.Microphone() as source:
print("Listening...")
voice = listener.listen(source)
command = listener.recognize_google(voice)
command = command.lower()
if "jarvis" in command:
command = command.replace('jarvis', '')
return command
else:
talk("I cannot continue until you say jarvis")
take_command()
except:
pass
def run_jarvis():
command = take_command()
if not command:
take_command()
else:
print(command)
if "play" in command:
song = command.replace("play", "")
talk("playing" + song)
pywhatkit.playonyt(song)
elif 'time' in command:
time = datetime.datetime.now().strftime('%I:%M %p')
print(time)
talk(time)
elif 'joke' in command:
joke = pyjokes.get_joke()
print(joke)
talk(joke)
elif 'downloads' in command:
path = "C:/Users\Mayur\Downloads"
path = os.path.realpath(path)
os.startfile(path)
elif "whatsapp" in command:
msg = command.replace("whatsapp", "")
pywhatkit.sendwhatmsg_instantly("+91xxxxxxxxxx", msg)
print("Successfully Sent!")
talk("successfully sent")
elif 'who is' or 'what is' or 'where is' or 'when is' or 'information' or 'which is' or "how is" in command:
result = pywhatkit.info(command, 3, True)
talk(result)
print(result)
else:
talk('Sorry sir can you repeat again')
while True:
run_jarvis()
它对我来说工作得很好，我没有看到任何会使你的程序变慢的代码，并且
listener.recognize_google(voice)recognizerengine我自己有一个虚拟助手程序，你必须记住，使用tts sapi5文本转语音的虚拟助手是逐字阅读逻辑，不像ai，ai不会从文档、url地址中逐字阅读精确的文本和页面，，它围绕相关性，绕行导航，一般响应进行设计，如果要求人工智能模型阅读精确和准确的单词，它不能这样做，请自己询问聊天gpt，如果它可以逐字逐字地执行，它会说不，所以tts sapi5 epeak 或任何类似的东西，响应有点慢。 ？这并不完全意味着不可能加速。但对于人工智能生成模型，需要注意的一件重要的事情是，它们最有益的是编码程序，而不是揭示绝对真相，EG。书中实际写的内容，用准确的话来说，但是：因为编码程序是基于变体的，为了在程序中实现特定的结果，比如编码一个循环，输出一个列表。这可以通过多种方式完成，为了达到相同的结果，编码中有很多方法可以实现相同的结果，这就是人工智能在创建程序方面表现出色的地方，你不是在寻找精确和正确的功能，达到某种完美，而是对功能和数据条件进行合理的数据搜索，这将补充您的程序目的，这就是人工智能所做的，有趣的是，我使用人工智能聊天gpt创建了一个带有窗口等的tts spai 5程序，并创建了一个模型，这不是基于人工智能：但基于逐字记录，如果集成在一起，两者都可以在计算机程序中占有一席之地。，
下面是一个Python虚拟助手程序，你可以尝试一下：确保你的版本至少是3.11。在 python 中并安装必要的库，使用 cmd 上的 pip install 导入。 下图：
导入系统 导入剪贴板 将语音识别导入为 sr 从 googlesearch 导入搜索 导入请求 从 bs4 导入 BeautifulSoup 导入 pyttsx3 将 numpy 导入为 np 导入pyaudio 从 PyQt5.QtWidgets 导入 QMainWindow、QWidget、QVBoxLayout、QTextEdit、QLineEdit、QPushButton、QAction 从 PyQt5.QtCore 导入 Qt、pyqtSignal、QThread 从 PyQt5.QtWidgets 导入 QMenuBar、QMenu、QAction、QLineEdit 从 PyQt5 导入 QtWidgets、QtGui、QtCore 从 PyQt5.QtWidgets 导入 QVBoxLayout、QGraphicsView、QLabel、QApplication、QMainWindow、QWidget、QOpenGLWidget、QTextEdit 从 PyQt5.QtCore 导入 QTimer、pyqtSlot、pyqtSignal、QObject 从 OpenGL 导入 GL 从 OpenGL 导入 GLU 导入线程 从 PyQt5.QtCore 导入 Qt
类 AudioVisualizer(QtWidgets.QWidget): def init（自身）： 超级（）。init（）
self.resize(300, 50) # Size for the visualizer widget #400 / 100
# Set background color to black and add a white border
self.setStyleSheet("background-color: black; border: 8px solid white; border-radius: 10px;") # White border and radius
layout = QVBoxLayout()
self.label = QLabel(" Input Waveform:")
self.label.setStyleSheet("color: white;") # Set label color to white for contrast against the black background
layout.addWidget(self.label)
# Create a graphics view with a fixed height and width
self.graphics_view = QGraphicsView()
self.graphics_view.setFixedHeight(80) # Fixed height for the waveform display
self.graphics_view.setHorizontalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff) # Disable horizontal scroll bar
self.graphics_view.setVerticalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff) # Disable vertical scroll bar
layout.addWidget(self.graphics_view)
self.setLayout(layout)
# Initialize PyAudio
self.audio = pyaudio.PyAudio()
self.stream = self.audio.open(format=pyaudio.paInt16,
channels=1,
rate=44100,
input=True,
frames_per_buffer=1024)
self.timer = QtCore.QTimer()
self.timer.timeout.connect(self.update_waveform)
self.timer.start(50) # Update every 50 ms
def update_waveform(self):
try:
# Read data from the microphone
data = self.stream.read(1024, exception_on_overflow=False)
wav_data = np.frombuffer(data, dtype=np.int16)
# Create a scene for the waveform
scene = QtWidgets.QGraphicsScene()
pen = QtGui.QPen(QtGui.QColor(0, 255, 0)) # Green pen for the waveform
# Plot the waveform
for i in range(len(wav_data) - 1):
scene.addLine(i, wav_data[i] / 100, i + 1, wav_data[i + 1] / 100, pen) # Scale for visibility
self.graphics_view.setScene(scene)
except OSError as e:
# Handle the case where the stream is closed or there's an error
print(f"Error reading from the audio stream: {e}")
def closeEvent(self, event):
# Stop the audio stream when closing the application
if self.stream.is_active():
self.stream.stop_stream()
self.stream.close()
self.audio.terminate()
event.accept()
类 OpenGLWidget(QOpenGLWidget): def init（自身）： 超级（）。init（） self.rotation_angle = 0 # 初始化旋转角度 self.timer = QTimer() # 创建一个计时器 self.timer.timeout.connect(self.update_rotation) # 将超时信号连接到update方法 self.timer.start(16) # 设置计时器大约每 16 毫秒触发一次（约 60 FPS）
def initializeGL(self):
GL.glClearColor(0.0, 0.0, 0.0, 1.0) # Black background
GL.glEnable(GL.GL_DEPTH_TEST) # Enable depth testing
GL.glEnable(GL.GL_BLEND) # Enable blending for transparency
GL.glBlendFunc(GL.GL_SRC_ALPHA, GL.GL_ONE_MINUS_SRC_ALPHA) # Set blend function
GL.glMatrixMode(GL.GL_PROJECTION)
GL.glLoadIdentity()
GLU.gluPerspective(45, 200 / 150, 0.1, 50.0) # Updated aspect ratio for 200x150
GL.glTranslatef(0.0, 0.0, -5)
def paintGL(self):
GL.glClear(GL.GL_COLOR_BUFFER_BIT | GL.GL_DEPTH_BUFFER_BIT) # Clear the buffers
GL.glPushMatrix() # Save the current state
GL.glRotatef(self.rotation_angle, 0, 1, 0) # Rotate by the current angle
self.draw_cube() # Draw the cube
GL.glPopMatrix() # Restore the previous state
self.update() # Request an update
def update_rotation(self):
self.rotation_angle += 1 # Increment the rotation angle
self.rotation_angle %= 360 # Keep the angle within 0-359
def draw_cube(self):
# Define the vertices of the cube
vertices = [
(1, 1, -1), # Vertex 0
(1, -1, -1), # Vertex 1
(-1, -1, -1), # Vertex 2
(-1, 1, -1), # Vertex 3
(1, 1, 1), # Vertex 4
(1, -1, 1), # Vertex 5
(-1, -1, 1), # Vertex 6
(-1, 1, 1) # Vertex 7
]
edges = [
(0, 1), (1, 2), (2, 3), (3, 0), # Back face
(4, 5), (5, 6), (6, 7), (7, 4), # Front face
(0, 4), (1, 5), (2, 6), (3, 7) # Connecting edges
]
# Draw the cube edges
GL.glBegin(GL.GL_LINES) # Begin drawing lines
GL.glColor4f(1.0, 1.0, 1.0, 0.5) # Set color with transparency (white with 50% alpha)
for edge in edges:
for vertex in edge:
GL.glVertex3fv(vertices[vertex]) # Specify each vertex for the edges
GL.glEnd() # End drawing
类 TextAppender(QObject): 文本信号 = pyqtSignal(str)
类主窗口（QMainWindow）： def init（自身）： 超级（）。init（） self.setWindowTitle("带有嵌入式 OpenGL 和音频可视化工具的主窗口") self.setGeometry(100, 100, 800, 600) # 主窗口大小（宽度，高度）
# Create a central widget to hold the layout
central_widget = QWidget(self) # Ensure central widget is linked to MainWindow
self.setCentralWidget(central_widget)
# Create a vertical layout
layout = QVBoxLayout(central_widget)
# Set style for the central widget
central_widget.setStyleSheet("background-color: black; border: 8px solid gray; border-radius: 12px;") # Main window border
# Create an audio visualizer widget (replace AudioVisualizer() with your actual class)
self.audio_visualizer = AudioVisualizer() # Replace this with your actual audio visualizer class
self.audio_visualizer.setFixedSize(600, 100) # Set size of audio visualizer widget
self.audio_visualizer.setStyleSheet("background-color: black; border: 10px solid light gray; border-radius: 12px;") # Visualizer style
layout.addWidget(self.audio_visualizer, alignment=Qt.AlignHCenter) # Add audio visualizer at the top center
# Create an OpenGL widget
self.opengl_widget = OpenGLWidget()
self.opengl_widget.setFixedSize(500, 300) # Set size of OpenGL widget
self.opengl_widget.setStyleSheet("background-color: black; border: 8px white; border-radius: 12px;") # OpenGL widget border
layout.addWidget(self.opengl_widget) # Add OpenGL widget to layout
# Create a QTextEdit to display command output
self.output_area = QTextEdit()
self.output_area.setReadOnly(True) # Set as read-only
self.output_area.setStyleSheet("background-color: black; color: white; border: 8px solid red; border-radius: 12px; font-size: 18px;") # Text area style with white text
self.output_area.setFixedSize(800, 450) # Set the output area size
layout.addWidget(self.output_area) # Add text output area to layout
# Thread-safe text appender
self.text_appender = TextAppender()
self.text_appender.text_signal.connect(self.append_text)
# Start the assistant without interference
self.start_assistant()
def append_text(self, text):
"""Appends text to the QTextEdit output area."""
self.output_area.append(text) # Append the received text to the QTextEdit
def start_assistant(self):
"""Logic to start the AI assistant automatically (this shouldn't be modified)."""
pass
@pyqtSlot(str)
def append_text(self, text):
# Safely append text to the QTextEdit
self.output_area.append(text) # Add the text to the output area
def speak(self, text, speed=150, amplitude=200):
# Initialize the pyttsx3 engine
engine = pyttsx3.init()
engine.setProperty('rate', speed) # Set speech rate
engine.setProperty('volume', amplitude / 200) # Set speech volume
engine.say(text)
engine.runAndWait()
def google_search(self, query):
try:
search_results = list(search(query))
return search_results
except Exception as e:
self.text_appender.text_signal.emit(f"An error occurred during search: {e}")
return []
def recognize_speech(self):
recognizer = sr.Recognizer()
with sr.Microphone() as source:
self.text_appender.text_signal.emit("Detecting...") # Redirect to output area
recognizer.energy_threshold = 252
recognizer.adjust_for_ambient_noise(source, duration=1)
audio = recognizer.listen(source)
try:
self.text_appender.text_signal.emit("Recognizing...") # Redirect to output area
query = recognizer.recognize_google(audio)
self.text_appender.text_signal.emit("Output of query: " + query) # Redirect recognized speech to output area
return query.lower()
except sr.UnknownValueError:
self.text_appender.text_signal.emit("Error: Unable to recognize speech") # Redirect error to output area
return ""
except sr.RequestError as e:
self.text_appender.text_signal.emit(f"Could not request results: {e}") # Redirect error to output area
return ""
def read_from_url(self, url):
try:
response = requests.get(url)
if response.status_code == 200:
html_content = response.text
soup = BeautifulSoup(html_content, 'html.parser')
paragraphs = [p.text for p in soup.find_all('p')]
if not paragraphs:
self.text_appender.text_signal.emit("No content found in the URL.")
return
for paragraph in paragraphs:
self.speak(paragraph) # Read the paragraph aloud
self.text_appender.text_signal.emit(paragraph) # Append the paragraph to the output area
self.speak("Would you like me to read? If not, say 'stop reading', or say 'continue'.")
decision = self.recognize_speech() # Wait for the user's response
if decision and "stop reading" in decision.lower():
self.text_appender.text_signal.emit("Stopped reading.")
break # Exit the loop if the user chooses to stop
# After stopping reading, ask if the user wants to continue searching or exit
self.speak("Do you want to continue with a different data scan? If yes, say 'yes'. If not, say 'exit program'.")
post_decision = self.recognize_speech() # Capture user's decision
if post_decision and "exit program" in post_decision.lower():
self.speak("Exiting program.")
self.text_appender.text_signal.emit("Exiting program.")
sys.exit() # Exit the program immediately
elif post_decision and "yes" in post_decision.lower():
self.speak("Continuing with another search.")
self.text_appender.text_signal.emit("Continuing with another search.")
self.ai_assistant_main() # Restart the search process
else:
self.text_appender.text_signal.emit("Failed to retrieve content from the URL.")
except Exception as e:
self.text_appender.text_signal.emit(f"An error occurred while reading from the URL: {e}")
def extract_number(self, choice):
num_str = ''.join(filter(str.isdigit, choice))
if num_str:
return int(num_str)
else:
number_words = {
"one": 1, "two": 2, "three": 3, "four": 4, "five": 5,
"six": 6, "seven": 7, "eight": 8, "nine": 9, "ten": 10
}
choice_lower = choice.lower()
for word, number in number_words.items():
if word in choice_lower:
return number
return None
def start_assistant(self):
# Start the AI assistant in a separate thread
threading.Thread(target=self.assistant_main, daemon=True).start()
def assistant_main(self):
while True:
self.speak("Please say your search query.")
self.text_appender.text_signal.emit("Prompting user for input...")
query = self.recognize_speech()
if query == "":
self.text_appender.text_signal.emit("No query received, retrying...")
continue
if "exit program" in query:
self.speak("Exiting program.")
self.text_appender.text_signal.emit("Exiting program.")
sys.exit()
self.speak(f"Performing a search for {query}.")
search_results = self.google_search(query)
if not search_results:
self.speak("No results found.")
self.text_appender.text_signal.emit("No results found.")
continue
self.text_appender.text_signal.emit("Results:") # Output results
for i, result in enumerate(search_results[:5], 1): # Show first five results
self.text_appender.text_signal.emit(f"{i}. {result}")
self.speak("Please choose one of the search results by saying the number.")
# Get the user's choice
choice = self.recognize_speech()
number = self.extract_number(choice)
if number is not None and 1 <= number <= len(search_results[:5]):
url = search_results[number - 1]
self.speak(f"Reading from result number {number}.")
self.text_appender.text_signal.emit(f"Reading from: {url}")
clipboard.copy(url) # Copy URL to clipboard
self.read_from_url(url) # Read the content from the selected URL
else:
self.speak("Invalid choice. Please try again.")
self.text_appender.text_signal.emit("Invalid choice. Retrying...")
continue
if name == "main":
app = QApplication(sys.argv)
# Create the main window for the AI assistant
window = MainWindow()
window.show()
sys.exit(app.exec_())