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
程序员清风19 分钟前
LangGraph 入门:用状态机设计可靠的 Agent 工作流
人工智能·python
Data_Journal26 分钟前
使用 AutoScraper 进行网页抓取:分步教程
大数据·开发语言·数据库·python·scrapy
FanetheDivine28 分钟前
学习python 1.搭建python环境
python
虎虎(_ _)。゜zzZ1 小时前
SQLAlchemy入门教程
数据库·后端·python·sql·ai·sqlalchemy
BUG研究员_2 小时前
LangGraph持久化之失败后恢复运行
python·agent
gb42152872 小时前
数字人面试和RAG区别?
python
宁渡AI大模型2 小时前
AI 全栈面试新趋势:Vibe Coding、前端、Java 后端高频面试题深度解析|河南宁渡科技有限公司编程教程
java·javascript·人工智能·python·ai大模型
Groundwork Explorer2 小时前
ESP32-C3 SuperMini 排查WIFI收发故障
python·单片机·嵌入式硬件·mcu
智购科技自动售货机工厂2 小时前
2026自动售货机端侧AI降本逻辑:从云端API到本地推理的成本重构~YH
人工智能·python·ui·面试·交互
Data_Journal2 小时前
如何将网页抓取用于机器学习
大数据·开发语言·数据库·python·scrapy