做ps的素材哪个网站百度竞价排名正确解释
1、使用步骤
确保已安装以下库：
pip install vosk sounddevice requests pyttsx3
2、下载 Vosk 模型：
下载适合的中文模型，如 vosk-model-small-cn-0.22。
下载地址：
https://alphacephei.com/vosk/models
将模型解压后放置在代码中指定的路径 MODEL_PATH。
我选择放到项目的根目录下
如下图：
3、插麦克风
记得插个耳机当麦克风哦！！！
4、完整代码
import os
import sounddevice as sd
import queue
import vosk
import sys
import json
import pyttsx3
import requests
import threading# 初始化语音合成引擎
def init_tts():tts_engine = pyttsx3.init()tts_engine.setProperty('rate', 150) # 设置语速tts_engine.setProperty('volume', 1.0) # 设置音量return tts_engine# 语音输出函数
def speak(tts_engine, text):tts_engine.say(text)tts_engine.runAndWait()# 聊天机器人接口
class ChatBot:def __init__(self, api_url, key="free", appid="0"):self.api_url = api_urlself.key = keyself.appid = appiddef send_message(self, message):params = {"key": self.key,"appid": self.appid,"msg": message}try:response = requests.get(self.api_url, params=params)response.raise_for_status() # 检查请求是否成功data = response.json()if data.get("result") == 0:return data.get("content")else:return "对不起，我无法处理您的请求。"except requests.RequestException as e:return f"请求失败：{e}"# 初始化模型路径
MODEL_PATH = "vosk-model-small-cn-0.22" # 替换为模型的路径
if not os.path.exists(MODEL_PATH):print(f"模型路径不存在: {MODEL_PATH}")sys.exit(1)# 加载 Vosk 模型
try:model = vosk.Model(MODEL_PATH)print(f"模型加载成功: {MODEL_PATH}")
except Exception as e:print(f"加载模型时出错: {e}")sys.exit(1)q = queue.Queue()
stop_signal = threading.Event()# 音频流回调函数
def callback(indata, frames, time, status):if status:print(f"状态错误: {status}", file=sys.stderr)q.put(bytes(indata))# 实时语音识别函数
def recognize(tts_engine, chatbot):print("请开始说话...输入 'q' 并按回车退出。")try:with sd.RawInputStream(samplerate=16000, blocksize=8000, dtype="int16",channels=1, callback=callback):rec = vosk.KaldiRecognizer(model, 16000)while not stop_signal.is_set():try:# 设置超时，避免队列阻塞导致无法响应中断data = q.get(timeout=0.5)if rec.AcceptWaveform(data):result = json.loads(rec.Result())recognized_text = result.get('text', '')if recognized_text:print(f"\n你: {recognized_text}")# 调用聊天机器人bot_response = chatbot.send_message(recognized_text)print(f"机器人: {bot_response}")# 语音输出机器人回复speak(tts_engine, bot_response)else:partial_result = json.loads(rec.PartialResult()).get('partial', '')if partial_result:print(f"\r临时识别中: {partial_result}", end="")except queue.Empty:# 超时后检查停止信号continueexcept Exception as e:print(f"发生错误: {e}")finally:print("清理资源并退出。")# 主线程监听用户输入
def listen_for_exit():while True:user_input = input()if user_input.strip().lower() == 'q':stop_signal.set()print("退出指令收到，程序即将退出。")breakif __name__ == "__main__":# 初始化语音合成引擎tts_engine = init_tts()# 初始化聊天机器人chatbot = ChatBot(api_url="http://api.qingyunke.com/api.php")# 欢迎语音介绍welcome_message = "我是小菲菲，很高兴为你服务！"print(f"机器人: {welcome_message}")speak(tts_engine, welcome_message)# 创建监听输入的线程input_thread = threading.Thread(target=listen_for_exit)input_thread.daemon = Trueinput_thread.start()# 启动实时语音识别和对话recognize(tts_engine, chatbot)感谢您的支持和关注！！
也支持您的继续创新！！