import streamlit as st import pandas as pd import pysrt from transformers import MarianMTModel, MarianTokenizer import tempfile import os from io import BytesIO import requests def fetch_languages(url): response = requests.get(url) if response.status_code == 200: df = pd.read_csv(BytesIO(response.content), delimiter="|", skiprows=2, header=None).dropna(axis=1, how='all') df.columns = ['ISO 639-1', 'ISO 639-2', 'Language Name', 'Native Name'] df['ISO 639-1'] = df['ISO 639-1'].str.strip() language_options = [(row['ISO 639-1'], f"{row['ISO 639-1']} - {row['Language Name']}") for index, row in df.iterrows()] return language_options else: return [] def translate_text(text, source_language_code, target_language_code): model_name = f"Helsinki-NLP/opus-mt-{source_language_code}-{target_language_code}" if source_language_code == target_language_code: return "Translation between the same languages is not supported." try: tokenizer = MarianTokenizer.from_pretrained(model_name) model = MarianMTModel.from_pretrained(model_name) except Exception as e: return f"Failed to load model for {source_language_code} to {target_language_code}: {str(e)}" translated_texts = [] for sentence in text.split("\n"): translated = model.generate(**tokenizer(sentence, return_tensors="pt", padding=True, truncation=True, max_length=512)) translated_text = tokenizer.decode(translated[0], skip_special_tokens=True) translated_texts.append(translated_text) return "\n".join(translated_texts) def translate_srt(input_file, source_language_code, target_language_code): subs = pysrt.open(input_file) total_subs = len(subs) translated_subs = [] progress_text = "Operation in progress. For information, the progress bar start when the translation begin." progress_bar = st.progress(0, text=progress_text) # Initialize the progress bar for idx, sub in enumerate(subs): translated_text = translate_text(sub.text, source_language_code, target_language_code) translated_sub = pysrt.SubRipItem(index=idx+1, start=sub.start, end=sub.end, text=translated_text) translated_subs.append(translated_sub) progress_bar.progress((idx + 1) / total_subs) # Update progress bar translated_file = pysrt.SubRipFile(items=translated_subs) return translated_file st.title("SRT Translation") st.write("We use model from [Language Technology Research Group at the University of Helsinki](https://huggingface.co/Helsinki-NLP). For API use please visit [this space](https://huggingface.co/spaces/Lenylvt/SRT_Translation-API)") # Fetch language options url = "https://huggingface.co/Lenylvt/LanguageISO/resolve/main/iso.md" language_options = fetch_languages(url) source_language_code, target_language_code = None, None if language_options: source_language_code = st.selectbox("1️⃣ Select Source Language", options=language_options, format_func=lambda x: x[1])[0] target_language_code = st.selectbox("2️⃣ Select Target Language", options=language_options, format_func=lambda x: x[1])[0] file_input = st.file_uploader("📁 Upload SRT File", type=["srt"], accept_multiple_files=False) if file_input is not None and source_language_code and target_language_code: with tempfile.NamedTemporaryFile(delete=False, suffix=".srt") as temp_file: temp_file.write(file_input.getvalue()) temp_file.flush() translated_srt = translate_srt(temp_file.name, source_language_code, target_language_code) os.unlink(temp_file.name) # Delete the temp file # Save the translated subtitles to a temporary file and then read it into BytesIO with tempfile.NamedTemporaryFile(delete=False, suffix=".srt") as temp_file: translated_srt.save(temp_file.name, encoding='utf-8') temp_file.seek(0) translated_srt_bytes = open(temp_file.name, 'rb').read() os.unlink(temp_file.name) # Delete the temp file after reading st.download_button( label="⬇️ Download Translated SRT", data=translated_srt_bytes, file_name="translated_subtitles.srt", mime="text/plain", )