Update app.py

app.py

@@ -18,6 +18,9 @@ from typing import Union
 from diffusers.utils.torch_utils import randn_tensor
 from tqdm import tqdm
 
+from transformers import pipeline
+translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en")
+
 class Tango2Pipeline(DiffusionPipeline):
     def __init__(
         self,
@@ -243,9 +246,14 @@ pipe = Tango2Pipeline(vae=tango.vae,
     
 @spaces.GPU(duration=60)
 def gradio_generate(prompt, output_format, steps, guidance):
-    output_wave = pipe(prompt,steps,guidance) ## Using pipeliine automatically uses flash attention for torch2.0 above
-    #output_wave = tango.generate(prompt, steps, guidance)
-    # output_filename = f"{prompt.replace(' ', '_')}_{steps}_{guidance}"[:250] + ".wav"
+    # 한글이 포함되어 있는지 확인
+    if any(ord('가') <= ord(char) <= ord('힣') for char in prompt):
+        # 한글을 영어로 번역
+        translation = translator(prompt)[0]['translation_text']
+        prompt = translation
+        print(f"Translated prompt: {prompt}")
+    
+    output_wave = pipe(prompt,steps,guidance)
     output_wave = output_wave.audios[0]
     output_filename = "temp.wav"
     wavio.write(output_filename, output_wave, rate=16000, sampwidth=2)
@@ -256,7 +264,6 @@ def gradio_generate(prompt, output_format, steps, guidance):
 
     return output_filename
 
-
 input_text = gr.Textbox(lines=2, label="Prompt")
 output_format = gr.Radio(label = "Output format", info = "The file you can dowload", choices = ["mp3", "wav"], value = "wav")
 output_audio = gr.Audio(label="Generated Audio", type="filepath")