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import gradio as gr
from transformers import MarianMTModel, MarianTokenizer
import speech_recognition as sr
from gtts import gTTS
import pyttsx3
import tempfile
import re
# import os
# os.environ["no_proxy"]="localhost,127.0.0.1,::1"
def clean_text(text):
"""Clean text to handle punctuations and special characters."""
return re.sub(r'[^\w\s,.!?;:]', '', text)
# Load translation models
tokenizer_en2zh = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-zh")
model_en2zh = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-zh")
tokenizer_zh2en = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
model_zh2en = MarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-zh-en")
def translate_text(input_text, target_language):
cleaned_text = clean_text(input_text)
if target_language == "en":
tokenizer, model = tokenizer_zh2en, model_zh2en
else:
tokenizer, model = tokenizer_en2zh, model_en2zh
inputs = tokenizer(cleaned_text, return_tensors="pt", padding=True, truncation=True, max_length=512)
translated_tokens = model.generate(**inputs)
translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
return translated_text
def speech_to_text(audio_file):
recognizer = sr.Recognizer()
with sr.AudioFile(audio_file) as source:
audio = recognizer.record(source)
return recognizer.recognize_google(audio)
def synthesize_speech(text, language, method='gtts'):
if method == 'gtts':
tts = gTTS(text=text, lang=language)
temp_audio = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
tts.save(temp_audio.name)
elif method == 'pyttsx3':
engine = pyttsx3.init()
engine.save_to_file(text, temp_audio.name)
engine.runAndWait()
return temp_audio.name
def process_input(input_text=None, audio_file=None, method='gtts'):
recognized_text = ""
if audio_file:
recognized_text = speech_to_text(audio_file)
elif input_text:
recognized_text = input_text
else:
return "No input provided", "", None
detected_lang = "zh" if re.search(r'[\u4e00-\u9fff]', recognized_text) else "en"
target_language = "zh" if detected_lang == "en" else "en"
translated_text = translate_text(recognized_text, target_language)
audio_path = synthesize_speech(translated_text, target_language, method=method)
return recognized_text, translated_text, audio_path
# Gradio interface
# Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("## 中英文互译及语音播报")
inputs = [
gr.Textbox(label="Enter Text"),
gr.Audio(sources=["upload","microphone"], type="filepath", label="Or speak into the microphone")
]
translate_button = gr.Button("翻译并播报")
outputs = [
gr.Textbox(label="Recognized Text"),
gr.Textbox(label="翻译结果"),
gr.Audio(label="语音播报")
]
translate_button.click(fn=process_input, inputs=inputs, outputs=outputs)
demo.launch(share=True)
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