| import streamlit as st |
| import os |
| import io |
| from transformers import M2M100Tokenizer, M2M100ForConditionalGeneration |
| 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_1.2B", |
| 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.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, |
| ) |
|
|
| source_lang = st.selectbox(label="Source language", options=list(lang_id.keys())) |
| target_lang = st.selectbox(label="Target language", options=list(lang_id.keys())) |
|
|
| if st.button("Run"): |
| time_start = time.time() |
| tokenizer, model = load_model() |
|
|
| src_lang = lang_id[source_lang] |
| trg_lang = lang_id[target_lang] |
| tokenizer.src_lang = src_lang |
| with torch.no_grad(): |
| 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") |
|
|
