app.py

import os

import ctranslate2
import gradio as gr
from huggingface_hub import snapshot_download
from sentencepiece import SentencePieceProcessor

title = "MADLAD-400 Translation Demo"
description = """
<p>
Translator using <a href='https://arxiv.org/abs/2309.04662' target='_blank'>MADLAD-400</a>, a multilingual machine translation model on 250 billion tokens covering over 450 languages using publicly available data. This demo application uses  <a href="https://huggingface.co/santhosh/madlad400-3b-ct2">santhosh/madlad400-3b-ct2</a> model, which is a ctranslate2 optimized model of <a href="https://huggingface.co/google/madlad400-3b-mt">google/madlad400-3b-mt</a>
</p>
"""

# As per https://opennmt.net/CTranslate2/performance.html
# By default CTranslate2 is compiled with intel MKL.
# It is observed that this setting has a significant positive performance impact.
os.environ["CT2_USE_EXPERIMENTAL_PACKED_GEMM"] = "1"

model_name = "Heng666/madlad400-3b-mt-ct2-int8"
model_path = snapshot_download(model_name)

tokenizer = SentencePieceProcessor()
tokenizer.load(f"{model_path}/sentencepiece.model")
translator = ctranslate2.Translator(model_path)
tokens = [tokenizer.decode(i) for i in range(460)]
lang_codes = [token[2:-1] for token in tokens if token.startswith("<2")]


def translate(input_text, target_language):
    input_tokens = tokenizer.encode(f"<2{target_language}> {input_text}", out_type=str)
    results = translator.translate_batch(
        [input_tokens],
        batch_type="tokens",
        # max_batch_size=1024,
        beam_size=1,
        no_repeat_ngram_size=1,
        # repetition_penalty=2,
    )
    translated_sentence = tokenizer.decode(results[0].hypotheses[0])
    return translated_sentence


def translate_interface(input_text, target_language):
    translated_text = translate(input_text, target_language)
    return translated_text


input_text = gr.Textbox(
    label="Input Text",
    value="Imagine a world in which every single person on the planet is given free access to the sum of all human knowledge.",
)
target_language = gr.Dropdown(lang_codes, value="ml", label="Target Language")
output_text = gr.Textbox(label="Translated Text")

gr.Interface(
    title=title,
    description=description,
    fn=translate_interface,
    inputs=[input_text, target_language],
    outputs=output_text,
).launch()