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import os |
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import torch |
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import gradio as gr |
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import time |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline |
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from flores200_codes import flores_codes |
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def load_models(): |
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model_name_dict = {'nllb-distilled-1.3B': 'facebook/nllb-200-distilled-1.3B'} |
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model_dict = {} |
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for call_name, real_name in model_name_dict.items(): |
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print('\tLoading model: %s' % call_name) |
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model = AutoModelForSeq2SeqLM.from_pretrained(real_name) |
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tokenizer = AutoTokenizer.from_pretrained(real_name) |
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model_dict[call_name+'_model'] = model |
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model_dict[call_name+'_tokenizer'] = tokenizer |
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return model_dict |
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def translation(source, target, text): |
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if len(model_dict) == 2: |
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model_name = 'nllb-distilled-1.3B' |
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start_time = time.time() |
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source = flores_codes[source] |
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target = flores_codes[target] |
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model = model_dict[model_name + '_model'] |
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tokenizer = model_dict[model_name + '_tokenizer'] |
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translator = pipeline('translation', model=model, tokenizer=tokenizer, src_lang=source, tgt_lang=target) |
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output = translator(text, max_length=400) |
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end_time = time.time() |
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output = output[0]['translation_text'] |
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result = {'inference_time': end_time - start_time, |
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'source': source, |
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'target': target, |
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'result': output} |
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return result |
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if __name__ == '__main__': |
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print('\tinit models') |
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global model_dict |
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model_dict = load_models() |
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lang_codes = list(flores_codes.keys()) |
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inputs = [gr.inputs.Dropdown(lang_codes, default='English', label='Source'), |
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gr.inputs.Dropdown(lang_codes, default='Korean', label='Target'), |
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gr.inputs.Textbox(lines=5, label="Input text"), |
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] |
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outputs = gr.outputs.JSON() |
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title = "NLLB distilled 1.3B demo" |
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demo_status = "Demo is running on CPU" |
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description = f"Details: https://github.com/facebookresearch/fairseq/tree/nllb. {demo_status}" |
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examples = [ |
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['English', 'Korean', 'Hi. nice to meet you'] |
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] |
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gr.Interface(translation, |
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inputs, |
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outputs, |
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title=title, |
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description=description, |
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examples=examples, |
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examples_per_page=50, |
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).launch() |
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