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import streamlit as st
import os
import speech_recognition as sr
from transformers import MarianMTModel, MarianTokenizer
from gtts import gTTS
from io import BytesIO
import tempfile
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 audio_to_text(audio_file):
recognizer = sr.Recognizer()
with sr.AudioFile(audio_file) as source:
audio_data = recognizer.record(source)
try:
text = recognizer.recognize_google(audio_data)
return text
except sr.UnknownValueError:
st.error("Speech Recognition could not understand the audio.")
except sr.RequestError as e:
st.error(f"Could not request results from Speech Recognition service; {e}")
return ""
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 main():
st.title("Audio Language Translation App")
st.write("Translate audio between multiple languages 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 and tokenizer
tokenizer, model = load_model(source_lang_code, target_lang_code)
if tokenizer and model:
# Audio input
uploaded_audio = st.file_uploader("Upload an audio file (WAV format):", type=["wav"])
if uploaded_audio is not None:
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
temp_audio.write(uploaded_audio.read())
temp_audio_path = temp_audio.name
st.audio(uploaded_audio, format="audio/wav")
with st.spinner("Converting audio to text..."):
input_text = audio_to_text(temp_audio_path)
st.success("Audio converted to text!")
st.text_area("Transcribed text:", input_text, height=100)
if st.button("Translate and Generate Audio"):
with st.spinner("Translating text..."):
translated_text = translate_text(tokenizer, model, input_text)
st.success("Translation completed!")
st.text_area("Translated text:", translated_text, height=100)
with st.spinner("Generating audio..."):
output_audio = text_to_audio(translated_text, target_lang_code)
st.success("Audio generated!")
st.audio(output_audio, format="audio/mp3")
if __name__ == "__main__":
main()
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