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import streamlit as st
import speech_recognition as sr
from transformers import MarianMTModel, MarianTokenizer
from gtts import gTTS
from io import BytesIO
import queue
import threading
import pyaudio
def load_model(source_lang, target_lang):
model_name = f"Helsinki-NLP/opus-mt-{source_lang}-{target_lang}"
try:
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
return tokenizer, model
except Exception as e:
st.error(f"Failed to load model for {source_lang} to {target_lang}. Ensure the language pair is supported. Error: {e}")
return None, None
def translate_text(tokenizer, model, text):
if not text:
return ""
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
outputs = model.generate(**inputs)
translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return translated_text
def text_to_audio(text, lang):
tts = gTTS(text=text, lang=lang)
audio_file = BytesIO()
tts.write_to_fp(audio_file)
audio_file.seek(0)
return audio_file
def recognize_speech_live(q):
recognizer = sr.Recognizer()
mic = sr.Microphone()
with mic as source:
recognizer.adjust_for_ambient_noise(source)
st.info("Start speaking...")
while True:
try:
audio_data = recognizer.listen(source)
text = recognizer.recognize_google(audio_data)
q.put(text)
except sr.UnknownValueError:
q.put("[Unintelligible]")
except Exception as e:
st.error(f"Error during speech recognition: {e}")
break
def main():
st.title("Real-Time Audio Language Translation")
st.write("Translate spoken words in real time using open-source models.")
# Language selection
languages = {
"English": "en",
"Spanish": "es",
"French": "fr",
"German": "de",
"Italian": "it",
"Russian": "ru",
"Chinese": "zh",
"Japanese": "ja",
"Korean": "ko",
}
source_language = st.selectbox("Select source language:", options=list(languages.keys()))
target_language = st.selectbox("Select target language:", options=list(languages.keys()))
if source_language == target_language:
st.warning("Source and target languages must be different.")
return
source_lang_code = languages[source_language]
target_lang_code = languages[target_language]
# Load the model
tokenizer, model = load_model(source_lang_code, target_lang_code)
if not (tokenizer and model):
return
# Real-time speech recognition
q = queue.Queue()
transcription_placeholder = st.empty()
translation_placeholder = st.empty()
audio_placeholder = st.empty()
if st.button("Start Real-Time Translation"):
st.write("Processing...")
# Start speech recognition in a separate thread
threading.Thread(target=recognize_speech_live, args=(q,), daemon=True).start()
while True:
if not q.empty():
spoken_text = q.get()
transcription_placeholder.text_area("Transcribed Text:", spoken_text, height=100)
# Translate text
translated_text = translate_text(tokenizer, model, spoken_text)
translation_placeholder.text_area("Translated Text:", translated_text, height=100)
# Generate and play translated audio
translated_audio = text_to_audio(translated_text, target_lang_code)
audio_placeholder.audio(translated_audio, format="audio/mp3")
if __name__ == "__main__":
main()
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