Update app.py

app.py

@@ -12,7 +12,7 @@ pipe = pipeline(
 )
 label = pipeline("audio-classification", model="facebook/mms-lid-126", device=device)
 processor = AutoProcessor.from_pretrained("suno/bark")
-model = BarkModel.from_pretrained("suno/bark-small")
+model = BarkModel.from_pretrained("suno/bark")
 model = model.to(device)
 synthesised_rate = model.generation_config.sample_rate
 
@@ -38,19 +38,15 @@ title = "外国话转中文话"
 description = """
 作为[Hugging Face Audio course](https://huggingface.co/learn/audio-course/chapter0/introduction) 的结课大作业，本演示调用了三个自然语言处理的大模型，一个用于将外国话翻译成中文，一个用于判断说的哪个国家的话，一个用于将中文转成语音输出。演示同时支持语音上传和麦克风输入，转换速度比较慢因为租不起GPU的服务器（支出增加20倍），建议您通过已经缓存Examples体验效果。欢迎添加我的微信号：ESGGTP 与我的平行人交流。
 
-![外国话转中文话](https://drive.google.com/file/d/1jTVYNAgXWRf0miiTKCQOcx54JeFBhzJJ/view?usp=drive_link "Diagram of my Wechat QR code")
+![Cascaded STST](https://huggingface.co/datasets/huggingface-course/audio-course-images/resolve/main/s2st_cascaded.png "Diagram of cascaded speech to speech translation")
+"""
 """
 examples = [
-    ["./cs.wav", None],
-    ["./de.wav", None],
-    ["./fr.wav", None],
-    ["./it.wav", None],
-    ["./nl.wav", None],
-    ["./pl.wav", None],
-    ["./ro.wav", None],
-    # ["./hr.wav", None],
-    # ["./fi.wav", None],
-    # ["./sl.wav", None],
+    ["./en.mp3", None],
+    ["./de.mp3", None],
+    ["./fr.mp3", None],
+    ["./it.mp3", None],
+    
 ]
 import gradio as gr