the first commit

.gitignore

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+.idea

README.md

@@ -1,10 +1,10 @@
 ---
-title: Nllb Rus Myv V2023 Demo
-emoji: 📊
-colorFrom: green
-colorTo: pink
+title: Nllb Rus Tyv V1 Demo
+emoji: 🚀
+colorFrom: blue
+colorTo: gray
 sdk: gradio
-sdk_version: 4.1.1
+sdk_version: 3.46.1
 app_file: app.py
 pinned: false
 ---

app.py

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+import gradio as gr
+
+
+from translation import Translator, LANGUAGES
+LANGUAGES_LIST = list(LANGUAGES.keys())
+
+
+def translate_wrapper(text, src, trg, by_sentence=True, preprocess=True, random=False, num_beams=4):
+    src_lang = LANGUAGES.get(src)
+    tgt_lang = LANGUAGES.get(trg)
+    # if src == trg:
+    #     return 'Please choose two different languages'
+    result = translator.translate(
+        text=text,
+        src_lang=src_lang,
+        tgt_lang=tgt_lang,
+        do_sample=random,
+        num_beams=int(num_beams),
+        by_sentence=by_sentence,
+        preprocess=preprocess,
+    )
+    return result
+
+
+article = """
+This is a NLLB-200-600M model fine-tuned for translation between Russian and Tyvan (Tuvan) languages,
+using the data from https://tyvan.ru/.
+
+This model is described in https://cointegrated.medium.com/a37fc706b865.
+
+If you want to host in on your own backend, consider running this dockerized app: https://github.com/slone-nlp/nllb-docker-demo.
+"""
+
+
+interface = gr.Interface(
+    translate_wrapper,
+    [
+        gr.Textbox(label="Text", lines=2, placeholder='text to translate '),
+        gr.Dropdown(LANGUAGES_LIST, type="value", label='source language', value=LANGUAGES_LIST[0]),
+        gr.Dropdown(LANGUAGES_LIST, type="value", label='target language', value=LANGUAGES_LIST[1]),
+        gr.Checkbox(label="by sentence", value=True),
+        gr.Checkbox(label="text preprocesing", value=True),
+        gr.Checkbox(label="randomize", value=False),
+        gr.Dropdown([1, 2, 3, 4, 5], label="number of beams", value=4),
+    ],
+    "text",
+    title='Tyvan-Russian translaton',
+    article=article,
+)
+
+
+if __name__ == '__main__':
+    translator = Translator()
+
+    interface.launch()

requirements.txt

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+transformers==4.33
+sentencepiece
+gradio>=3.18.0
+torch
+sentence-splitter==1.4
+sacremoses==0.0.45
+accelerate==0.23

translation.py

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+import re
+import sys
+import typing as tp
+import unicodedata
+
+import torch
+from sacremoses import MosesPunctNormalizer
+from sentence_splitter import SentenceSplitter
+from transformers import AutoModelForSeq2SeqLM, NllbTokenizer
+
+MODEL_URL = "slone/nllb-rus-myv-v1-extvoc"
+LANGUAGES = {
+    "Рузонь | Русский | Russian": "rus_Cyrl",
+    "Эрзянь | Эрзянский | Erzya": "myv_Cyrl",
+}
+L1 = "rus_Cyrl"
+L2 = "myv_Cyrl"
+
+
+def get_non_printing_char_replacer(replace_by: str = " ") -> tp.Callable[[str], str]:
+    non_printable_map = {
+        ord(c): replace_by
+        for c in (chr(i) for i in range(sys.maxunicode + 1))
+        # same as \p{C} in perl
+        # see https://www.unicode.org/reports/tr44/#General_Category_Values
+        if unicodedata.category(c) in {"C", "Cc", "Cf", "Cs", "Co", "Cn"}
+    }
+
+    def replace_non_printing_char(line) -> str:
+        return line.translate(non_printable_map)
+
+    return replace_non_printing_char
+
+
+class TextPreprocessor:
+    """
+    Mimic the text preprocessing made for the NLLB model.
+    This code is adapted from the Stopes repo of the NLLB team:
+    https://github.com/facebookresearch/stopes/blob/main/stopes/pipelines/monolingual/monolingual_line_processor.py#L214
+    """
+
+    def __init__(self, lang="en"):
+        self.mpn = MosesPunctNormalizer(lang=lang)
+        self.mpn.substitutions = [
+            (re.compile(r), sub) for r, sub in self.mpn.substitutions
+        ]
+        self.replace_nonprint = get_non_printing_char_replacer(" ")
+
+    def __call__(self, text: str) -> str:
+        clean = self.mpn.normalize(text)
+        clean = self.replace_nonprint(clean)
+        # replace 𝓕𝔯𝔞𝔫𝔠𝔢𝔰𝔠𝔞 by Francesca
+        clean = unicodedata.normalize("NFKC", clean)
+        return clean
+
+
+def fix_tokenizer(tokenizer, new_lang=L2):
+    """Add a new language token to the tokenizer vocabulary
+    (this should be done each time after its initialization)
+    """
+    old_len = len(tokenizer) - int(new_lang in tokenizer.added_tokens_encoder)
+    tokenizer.lang_code_to_id[new_lang] = old_len - 1
+    tokenizer.id_to_lang_code[old_len - 1] = new_lang
+    # always move "mask" to the last position
+    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 new_lang not in tokenizer._additional_special_tokens:
+        tokenizer._additional_special_tokens.append(new_lang)
+    # clear the added token encoder; otherwise a new token may end up there by mistake
+    tokenizer.added_tokens_encoder = {}
+    tokenizer.added_tokens_decoder = {}
+
+
+def sentenize_with_fillers(text, splitter, fix_double_space=True, ignore_errors=False):
+    """Apply a sentence splitter and return the sentences and all separators before and after them"""
+    if fix_double_space:
+        text = re.sub(" +", " ", text)
+    sentences = splitter.split(text)
+    fillers = []
+    i = 0
+    for sentence in sentences:
+        start_idx = text.find(sentence, i)
+        if ignore_errors and start_idx == -1:
+            # print(f"sent not found after {i}: `{sentence}`")
+            start_idx = i + 1
+        assert start_idx != -1, f"sent not found after {i}: `{sentence}`"
+        fillers.append(text[i:start_idx])
+        i = start_idx + len(sentence)
+    fillers.append(text[i:])
+    return sentences, fillers
+
+
+class Translator:
+    def __init__(self):
+        self.model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_URL, low_cpu_mem_usage=False, load_in_8bit=True)
+        if torch.cuda.is_available():
+            self.model.cuda()
+        self.tokenizer = NllbTokenizer.from_pretrained(MODEL_URL)
+        fix_tokenizer(self.tokenizer)
+
+        self.splitter = SentenceSplitter("ru")
+        self.preprocessor = TextPreprocessor()
+
+        self.languages = LANGUAGES
+
+    def translate(
+        self,
+        text,
+        src_lang=L1,
+        tgt_lang=L2,
+        max_length="auto",
+        num_beams=4,
+        by_sentence=True,
+        preprocess=True,
+        **kwargs,
+    ):
+        """Translate a text sentence by sentence, preserving the fillers around the sentences."""
+        if by_sentence:
+            sents, fillers = sentenize_with_fillers(
+                text, splitter=self.splitter, ignore_errors=True
+            )
+        else:
+            sents = [text]
+            fillers = ["", ""]
+        if preprocess:
+            sents = [self.preprocessor(sent) for sent in sents]
+        results = []
+        for sent, sep in zip(sents, fillers):
+            results.append(sep)
+            results.append(
+                self.translate_single(
+                    sent,
+                    src_lang=src_lang,
+                    tgt_lang=tgt_lang,
+                    max_length=max_length,
+                    num_beams=num_beams,
+                    **kwargs,
+                )
+            )
+        results.append(fillers[-1])
+        return "".join(results)
+
+    def translate_single(
+        self,
+        text,
+        src_lang=L1,
+        tgt_lang=L2,
+        max_length="auto",
+        num_beams=4,
+        n_out=None,
+        **kwargs,
+    ):
+        self.tokenizer.src_lang = src_lang
+        encoded = self.tokenizer(
+            text, return_tensors="pt", truncation=True, max_length=512
+        )
+        if max_length == "auto":
+            max_length = int(32 + 2.0 * encoded.input_ids.shape[1])
+        generated_tokens = self.model.generate(
+            **encoded.to(self.model.device),
+            forced_bos_token_id=self.tokenizer.lang_code_to_id[tgt_lang],
+            max_length=max_length,
+            num_beams=num_beams,
+            num_return_sequences=n_out or 1,
+            **kwargs,
+        )
+        out = self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+        if isinstance(text, str) and n_out is None:
+            return out[0]
+        return out
+
+
+if __name__ == "__main__":
+    print("Initializing a translator to pre-download models...")
+    translator = Translator()
+    print("Initialization successful!")