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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./Heng666/madlad400-3b-mt-ct2-int8">Heng666/madlad400-3b-mt-ct2-int8</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/madlad400-7b-mt-ct2-int8"
model_path = snapshot_download(model_name)
tokenizer = SentencePieceProcessor()
tokenizer.load(f"{model_path}/spiece.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()
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