realtime translate

app.py

@@ -11,7 +11,6 @@ import torch
 import kenlm
 import torchaudio
 from pyctcdecode import Alphabet, BeamSearchDecoderCTC, LanguageModel
-device = torch.device(0 if torch.cuda.is_available() else "cpu")
 
 """Vietnamese speech2text"""
 cache_dir = './cache/'
@@ -125,16 +124,8 @@ def speech2text_en(input_file):
 """Machine translation"""
 vien_model_checkpoint = "datnth1709/finetuned_HelsinkiNLP-opus-mt-vi-en_PhoMT"
 envi_model_checkpoint = "datnth1709/finetuned_HelsinkiNLP-opus-mt-en-vi_PhoMT"
-# vien_translator = pipeline("translation", model=vien_model_checkpoint)
-# envi_translator = pipeline("translation", model=envi_model_checkpoint)
-
-vien_tokenizer = AutoTokenizer.from_pretrained(vien_model_checkpoint, return_tensors="pt")
-vien_model = ORTModelForSeq2SeqLM.from_pretrained(vien_model_checkpoint)
-vien_translator = TranslationPipeline(model=vien_model, tokenizer=vien_tokenizer,clean_up_tokenization_spaces=True, device=device)
-
-envi_tokenizer = AutoTokenizer.from_pretrained(envi_model_checkpoint, return_tensors="pt")
-envi_model = ORTModelForSeq2SeqLM.from_pretrained(envi_model_checkpoint)
-envi_translator = TranslationPipeline(model=envi_model, tokenizer=envi_tokenizer,clean_up_tokenization_spaces=True, device=device)
+vien_translator = pipeline("translation", model=vien_model_checkpoint)
+envi_translator = pipeline("translation", model=envi_model_checkpoint)
 
 
 def translate_vi2en(Vietnamese):