语音识别、翻译及语音合成

Speech recognition, translation and speech synthesis.

python 复制代码
#语音识别、翻译及语音合成
#pip install SpeechRecognition gtts googletrans==3.1.0a0 pyaudio sounddevice soundfile keyboard

import speech_recognition as sr
from gtts import gTTS
from googletrans import Translator
import os
import time
import pyaudio
import logging
import argparse
import sounddevice as sd
import soundfile as sf
import numpy as np
import keyboard
import requests
from datetime import datetime

class SpeechTranslationApp:
    def __init__(self, source_language='en-US', target_language='zh-CN', input_type='microphone', save_transcript=False):
        """
        Initialize the Speech Translation Application
        
        :param source_language: Language of input speech
        :param target_language: Language to translate to
        :param input_type: Input method (microphone or system_sound)
        :param save_transcript: Whether to save speech recognition results
        """
        # Configure logging
        logging.basicConfig(
            level=logging.INFO, 
            format='%(asctime)s - %(levelname)s: %(message)s',
            datefmt='%Y-%m-%d %H:%M:%S'
        )
        self.logger = logging.getLogger(__name__)

        # Speech recognition setup
        self.recognizer = sr.Recognizer()
        self.translator = Translator()
        
        # Configuration
        self.source_language = source_language
        self.target_language = target_language
        self.input_type = input_type
        self.save_transcript = save_transcript

        # Audio parameters
        self.CHUNK = 1024
        self.FORMAT = pyaudio.paInt16
        self.CHANNELS = 1
        self.RATE = 44100
        self.RECORD_SECONDS = 5

        # API endpoint (replace with the actual API URL from the GitHub project)
        self.ASR_API_URL = "https://api.example.com/asr"  # Update with actual API endpoint

    def recognize_speech(self):
        """
        Recognize speech from either microphone or system sound
        
        :return: Dictionary with recognition results
        """
        result = {
            'success': False,
            'text': None,
            'error': None
        }

        try:
            if self.input_type == "microphone":
                with sr.Microphone() as source:
                    self.logger.info(f"Listening (Language: {self.source_language})...")
                    audio = self.recognizer.listen(source, timeout=5, phrase_time_limit=5)
            elif self.input_type == "system_sound":
                # Record system audio using sounddevice
                self.logger.info(f"Capturing system sound (Language: {self.source_language})...")
                recording = sd.rec(
                    int(self.RECORD_SECONDS * self.RATE), 
                    samplerate=self.RATE, 
                    channels=self.CHANNELS
                )
                sd.wait()
                
                # Save recording to temporary file
                temp_audio_file = 'temp_system_audio.wav'
                sf.write(temp_audio_file, recording, self.RATE)
                
                # Convert to speech_recognition format
                with sr.AudioFile(temp_audio_file) as source:
                    audio = self.recognizer.record(source)
                
                # Clean up temporary file
                os.remove(temp_audio_file)
            else:
                raise ValueError("Invalid input type. Choose 'microphone' or 'system_sound'")

            # Recognize speech
            text = self.recognizer.recognize_google(audio, language=self.source_language)
            
            # Send to ASR API
            self.send_to_asr_api(audio)
            
            result['success'] = True
            result['text'] = text
            self.logger.info(f"Speech Recognition Result: {text}")
            
            # Save transcript if enabled
            if self.save_transcript:
                self.save_speech_to_file(text)

        except sr.WaitTimeoutError:
            result['error'] = "Listening timed out. No speech detected."
            self.logger.warning(result['error'])
        except sr.UnknownValueError:
            result['error'] = "Could not understand the audio"
            self.logger.warning(result['error'])
        except sr.RequestError as e:
            result['error'] = f"Could not request results from Google Speech Recognition service; {e}"
            self.logger.error(result['error'])
        except Exception as e:
            result['error'] = f"An unexpected error occurred: {e}"
            self.logger.error(result['error'])
        
        return result

    def send_to_asr_api(self, audio):
        """
        Send audio to ASR API for processing
        
        :param audio: Recognized audio data
        """
        try:
            # Convert audio to a format suitable for API upload
            audio_data = audio.get_wav_data()
            
            # Prepare files for upload
            files = {'audio': ('speech.wav', audio_data, 'audio/wav')}
            
            # Send to ASR API (replace with actual API call)
            response = requests.post(self.ASR_API_URL, files=files)
            
            if response.status_code == 200:
                self.logger.info("Successfully sent audio to ASR API")
            else:
                self.logger.warning(f"ASR API request failed with status {response.status_code}")
        
        except Exception as e:
            self.logger.error(f"Error sending audio to ASR API: {e}")

    def save_speech_to_file(self, text):
        """
        Save recognized speech to a text file
        
        :param text: Recognized text
        """
        try:
            # Create transcripts directory if it doesn't exist
            os.makedirs('transcripts', exist_ok=True)
            
            # Generate filename with timestamp
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            filename = f"transcripts/speech_transcript_{timestamp}.txt"
            
            with open(filename, 'w', encoding='utf-8') as f:
                f.write(text)
            
            self.logger.info(f"Transcript saved to {filename}")
        
        except Exception as e:
            self.logger.error(f"Error saving transcript: {e}")

    def translate_text(self, text):
        """
        Translate text to target language
        
        :param text: Text to translate
        :return: Translated text
        """
        try:
            translation = self.translator.translate(text, dest=self.target_language)
            self.logger.info(f"Translation Result: {translation.text}")
            return translation.text
        except Exception as e:
            self.logger.error(f"Translation error: {e}")
            return None

    def speak_text(self, text):
        """
        Convert text to speech and play
        
        :param text: Text to convert to speech
        """
        try:
            tts = gTTS(text=text, lang=self.target_language)
            output_file = "translation_output.mp3"
            tts.save(output_file)
            
            # Cross-platform audio playback
            if os.name == 'nt':  # Windows
                os.system(f"start {output_file}")
            elif os.name == 'posix':  # macOS and Linux
                os.system(f"mpg123 {output_file}")
            
            # Remove temporary file after playback
            time.sleep(2)  # Give time for playback
            os.remove(output_file)
        except Exception as e:
            self.logger.error(f"Text-to-speech error: {e}")

    def run(self):
        """
        Main application loop
        """
        self.logger.info("Speech Translation App Started")
        
        print("Press 'Esc' to exit the application")
        
        try:
            while not keyboard.is_pressed('esc'):
                # Recognize speech
                recognition_result = self.recognize_speech()
                
                if recognition_result['success']:
                    # Translate recognized text
                    translated_text = self.translate_text(recognition_result['text'])
                    
                    if translated_text:
                        # Speak translated text
                        self.speak_text(translated_text)
                else:
                    # Log or handle unsuccessful recognition
                    if recognition_result['error']:
                        print(f"Recognition error: {recognition_result['error']}")
                
                # Pause to prevent excessive processing
                time.sleep(2)
        
        except KeyboardInterrupt:
            self.logger.info("Application stopped by user")
        finally:
            print("Speech Translation App Closed")

def main():
    """
    Parse command-line arguments and start the application
    """
    parser = argparse.ArgumentParser(description='Speech Translation Application')
    parser.add_argument('--source_lang', default='en-US', help='Source language code')
    parser.add_argument('--target_lang', default='zh-CN', help='Target language code')
    parser.add_argument('--input', choices=['microphone', 'system_sound'], default='microphone', help='Input method')
    parser.add_argument('--save_transcript', action='store_true', help='Save speech transcripts')
    
    args = parser.parse_args()
    
    app = SpeechTranslationApp(
        source_language=args.source_lang, 
        target_language=args.target_lang, 
        input_type=args.input,
        save_transcript=args.save_transcript
    )
    
    app.run()

if __name__ == "__main__":
    main()
相关推荐
luckys.one3 小时前
第9篇:Freqtrade量化交易之config.json 基础入门与初始化
javascript·数据库·python·mysql·算法·json·区块链
大翻哥哥4 小时前
Python 2025:量化金融与智能交易的新纪元
开发语言·python·金融
zhousenshan5 小时前
Python爬虫常用框架
开发语言·爬虫·python
IMER SIMPLE6 小时前
人工智能-python-深度学习-经典神经网络AlexNet
人工智能·python·深度学习
CodeCraft Studio6 小时前
国产化Word处理组件Spire.DOC教程:使用 Python 将 Markdown 转换为 HTML 的详细教程
python·html·word·markdown·国产化·spire.doc·文档格式转换
专注API从业者7 小时前
Python/Java 代码示例:手把手教程调用 1688 API 获取商品详情实时数据
java·linux·数据库·python
java1234_小锋7 小时前
[免费]基于Python的协同过滤电影推荐系统(Django+Vue+sqlite+爬虫)【论文+源码+SQL脚本】
python·django·电影推荐系统·协同过滤
看海天一色听风起雨落7 小时前
Python学习之装饰器
开发语言·python·学习
XiaoMu_0018 小时前
基于Python+Streamlit的旅游数据分析与预测系统:从数据可视化到机器学习预测的完整实现
python·信息可视化·旅游
THMAIL8 小时前
深度学习从入门到精通 - 生成对抗网络(GAN)实战:创造逼真图像的魔法艺术
人工智能·python·深度学习·神经网络·机器学习·生成对抗网络·cnn