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import gradio as gr
import torch
import numpy as np
import fasttext
import os
import urllib
from transformers import MBartForConditionalGeneration, MBart50Tokenizer
MODEL_URL_MYV_MUL = 'slone/mbart-large-51-myv-mul-v1'
MODEL_URL_MUL_MYV = 'slone/mbart-large-51-mul-myv-v1'
MODEL_URL_LANGID = 'https://huggingface.co/slone/fastText-LID-323/resolve/main/lid.323.ftz'
MODEL_PATH_LANGID = 'lid.323.ftz'
lang_to_code = {
'Эрзянь | Erzya': 'myv_XX',
'Русский | Рузонь | Russian': 'ru_RU',
'Suomi | Суоминь | Finnish': 'fi_FI',
'Deutsch | Немецень | German': 'de_DE',
'Español | Испанонь | Spanish': 'es_XX',
'English | Англань ': 'en_XX',
'हिन्दी | Хинди | Hindi': 'hi_IN',
'漢語 | Китаень | Chinese': 'zh_CN',
'Türkçe | Турконь | Turkish': 'tr_TR',
'Українська | Украинань | Ukrainian': 'uk_UA',
'Français | Французонь | French': 'fr_XX',
'العربية | Арабонь | Arabic': 'ar_AR',
}
def fix_tokenizer(tokenizer, extra_lang='myv_XX'):
"""Add a new language id to a MBART 50 tokenizer (because it is not serialized) and shift the mask token id."""
old_len = len(tokenizer) - int(extra_lang in tokenizer.added_tokens_encoder)
tokenizer.lang_code_to_id[extra_lang] = old_len-1
tokenizer.id_to_lang_code[old_len-1] = extra_lang
tokenizer.fairseq_tokens_to_ids["<mask>"] = len(tokenizer.sp_model) + len(tokenizer.lang_code_to_id) + tokenizer.fairseq_offset
tokenizer.fairseq_tokens_to_ids.update(tokenizer.lang_code_to_id)
tokenizer.fairseq_ids_to_tokens = {v: k for k, v in tokenizer.fairseq_tokens_to_ids.items()}
if extra_lang not in tokenizer._additional_special_tokens:
tokenizer._additional_special_tokens.append(extra_lang)
tokenizer.added_tokens_encoder = {}
def translate(
text, model, tokenizer,
src='ru_RU',
trg='myv_XX',
max_length='auto',
num_beams=3,
repetition_penalty=5.0,
train_mode=False, n_out=None,
**kwargs
):
tokenizer.src_lang = src
encoded = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024)
if max_length == 'auto':
max_length = int(32 + 1.5 * encoded.input_ids.shape[1])
if train_mode:
model.train()
else:
model.eval()
generated_tokens = model.generate(
**encoded.to(model.device),
forced_bos_token_id=tokenizer.lang_code_to_id[trg],
max_length=max_length,
num_beams=num_beams,
repetition_penalty=repetition_penalty,
# early_stopping=True,
num_return_sequences=n_out or 1,
**kwargs
)
out = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
if isinstance(text, str) and n_out is None:
return out[0]
return out
def translate_rerank(
text, model, tokenizer,
src='ru_RU', trg='myv_XX', max_length='auto', num_beams=3, repetition_penalty=5.0, train_mode=False,
n=5, diversity_penalty=3.0, lang='myv', max_score=0.3, order_penalty=0.01,
verbose=False,
**kwargs
):
texts = translate(
text, model, tokenizer, src, trg,
max_length=max_length, train_mode=train_mode, repetition_penalty=repetition_penalty,
num_beams=n,
num_beam_groups=n,
diversity_penalty=diversity_penalty,
n_out=n,
**kwargs
)
scores = [get_mean_lang_score(t, lang=lang, max_score=max_score) for t in texts]
pen_scores = scores - order_penalty * np.arange(n)
if verbose:
print(texts)
print(scores)
print(pen_scores)
return texts[np.argmax(pen_scores)]
def get_mean_lang_score(text, lang='myv', k=300, max_score=0.3):
words = text.split() + [text]
res = []
for langs, scores in zip(*langid_model.predict(words, k=k)):
d = dict(zip([l[9:] for l in langs], scores))
score = min(d.get(lang, 0), max_score) / max_score
res.append(score)
# print(res)
return np.mean(res)
def translate_wrapper(text, src, trg):
src = lang_to_code.get(src)
trg = lang_to_code.get(trg)
if src == trg:
return 'Please choose two different languages'
if src == 'myv_XX':
model = model_myv_mul
elif trg == 'myv_XX':
model = model_mul_myv
else:
return 'Please translate to or from Erzya'
print(text, src, trg)
fn = translate_rerank if trg == 'myv_XX' else translate
result = fn(
text=text,
model=model,
tokenizer=tokenizer,
src=src,
trg=trg,
)
return result
interface = gr.Interface(
translate_wrapper,
[
gr.Textbox(label="Text / текстэнь", lines=2, placeholder='text to translate / текстэнь ютавтозь '),
gr.Dropdown(list(lang_to_code.keys()), type="value", label='source language / васень келесь', value=list(lang_to_code.keys())[0]),
gr.Dropdown(list(lang_to_code.keys()), type="value", label='target language / эряви келесь', value=list(lang_to_code.keys())[1]),
],
"text",
)
if __name__ == '__main__':
model_mul_myv = MBartForConditionalGeneration.from_pretrained(MODEL_URL_MUL_MYV)
model_myv_mul = MBartForConditionalGeneration.from_pretrained(MODEL_URL_MYV_MUL)
if torch.cuda.is_available():
model_mul_myv.cuda()
model_myv_mul.cuda()
tokenizer = MBart50Tokenizer.from_pretrained(MODEL_URL_MYV_MUL)
fix_tokenizer(tokenizer)
if not os.path.exists(MODEL_PATH_LANGID):
print('downloading LID model...')
urllib.request.urlretrieve(MODEL_URL_LANGID, MODEL_PATH_LANGID)
langid_model = fasttext.load_model(MODEL_PATH_LANGID)
interface.launch()
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