import streamlit as st import os import io from transformers import M2M100Tokenizer, M2M100ForConditionalGeneration from transformers import AutoTokenizer, AutoModelForSequenceClassification from languages import LANGUANGE_MAP import time import json from typing import List import torch import random import logging if torch.cuda.is_available(): device = torch.device("cuda:0") else: device = torch.device("cpu") logging.warning("GPU not found, using CPU, translation will be very slow.") st.cache(suppress_st_warning=True, allow_output_mutation=True) st.set_page_config(page_title="M2M100 Translator") lang_id = { "Afrikaans": "af", "Amharic": "am", "Arabic": "ar", "Asturian": "ast", "Azerbaijani": "az", "Bashkir": "ba", "Belarusian": "be", "Bulgarian": "bg", "Bengali": "bn", "Breton": "br", "Bosnian": "bs", "Catalan": "ca", "Cebuano": "ceb", "Czech": "cs", "Welsh": "cy", "Danish": "da", "German": "de", "Greeek": "el", "English": "en", "Spanish": "es", "Estonian": "et", "Persian": "fa", "Fulah": "ff", "Finnish": "fi", "French": "fr", "Western Frisian": "fy", "Irish": "ga", "Gaelic": "gd", "Galician": "gl", "Gujarati": "gu", "Hausa": "ha", "Hebrew": "he", "Hindi": "hi", "Croatian": "hr", "Haitian": "ht", "Hungarian": "hu", "Armenian": "hy", "Indonesian": "id", "Igbo": "ig", "Iloko": "ilo", "Icelandic": "is", "Italian": "it", "Japanese": "ja", "Javanese": "jv", "Georgian": "ka", "Kazakh": "kk", "Central Khmer": "km", "Kannada": "kn", "Korean": "ko", "Luxembourgish": "lb", "Ganda": "lg", "Lingala": "ln", "Lao": "lo", "Lithuanian": "lt", "Latvian": "lv", "Malagasy": "mg", "Macedonian": "mk", "Malayalam": "ml", "Mongolian": "mn", "Marathi": "mr", "Malay": "ms", "Burmese": "my", "Nepali": "ne", "Dutch": "nl", "Norwegian": "no", "Northern Sotho": "ns", "Occitan": "oc", "Oriya": "or", "Panjabi": "pa", "Polish": "pl", "Pushto": "ps", "Portuguese": "pt", "Romanian": "ro", "Russian": "ru", "Sindhi": "sd", "Sinhala": "si", "Slovak": "sk", "Slovenian": "sl", "Somali": "so", "Albanian": "sq", "Serbian": "sr", "Swati": "ss", "Sundanese": "su", "Swedish": "sv", "Swahili": "sw", "Tamil": "ta", "Thai": "th", "Tagalog": "tl", "Tswana": "tn", "Turkish": "tr", "Ukrainian": "uk", "Urdu": "ur", "Uzbek": "uz", "Vietnamese": "vi", "Wolof": "wo", "Xhosa": "xh", "Yiddish": "yi", "Yoruba": "yo", "Chinese": "zh", "Zulu": "zu", } @st.cache(suppress_st_warning=True, allow_output_mutation=True) def load_model( pretrained_model: str = "facebook/m2m100_418M", cache_dir: str = "models/", ): tokenizer = M2M100Tokenizer.from_pretrained(pretrained_model, cache_dir=cache_dir) model = M2M100ForConditionalGeneration.from_pretrained( pretrained_model, cache_dir=cache_dir ).to(device) model.eval() return tokenizer, model @st.cache(suppress_st_warning=True, allow_output_mutation=True) def load_detection_model( pretrained_model: str = "ivanlau/language-detection-fine-tuned-on-xlm-roberta-base", cache_dir: str = "models/", ): tokenizer = AutoTokenizer.from_pretrained(pretrained_model, cache_dir=cache_dir) model = AutoModelForSequenceClassification.from_pretrained(pretrained_model, cache_dir=cache_dir).to(device) model.eval() return tokenizer, model st.title("M2M100 Translator") st.write("M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this paper https://arxiv.org/abs/2010.11125 and first released in https://github.com/pytorch/fairseq/tree/master/examples/m2m_100 repository. The model that can directly translate between the 9,900 directions of 100 languages.\n") st.write(" This demo uses the facebook/m2m100_1.2B model. For local inference see https://github.com/ikergarcia1996/Easy-Translate") user_input: str = st.text_area( "Input text", height=200, max_chars=5120, ) target_lang = st.selectbox(label="Target language", options=list(lang_id.keys())) if st.button("Run"): time_start = time.time() tokenizer, model = load_model() de_tokenizer, de_model = load_detection_model() with torch.no_grad(): tokenized_sentence = de_tokenizer(user_input, return_tensors='pt') output = de_model(**tokenized_sentence) de_predictions = torch.nn.functional.softmax(output.logits, dim=-1) _, preds = torch.max(de_predictions, dim=-1) lang_type = LANGUANGE_MAP[preds.item()] if lang_type not in lang_id: time_end = time.time() st.success('Unsupported Language') st.write(f"Computation time: {round((time_end-time_start),3)} segs") else: src_lang = lang_id[lang_type] trg_lang = lang_id[target_lang] tokenizer.src_lang = src_lang encoded_input = tokenizer(user_input, return_tensors="pt").to(device) generated_tokens = model.generate( **encoded_input, forced_bos_token_id=tokenizer.get_lang_id(trg_lang) ) translated_text = tokenizer.batch_decode( generated_tokens, skip_special_tokens=True )[0] time_end = time.time() st.success(translated_text) st.write(f"Computation time: {round((time_end-time_start),3)} segs")