Update app.py

app.py

@@ -1,13 +1,5 @@
 import os
-os.system("pip install gradio==3.3")
 import gradio as gr
-import numpy as np
-
-title = "SpeechMatrix Speech-to-speech Translation"
-
-description = "Gradio Demo for SpeechMatrix. To use it, simply record your audio, or click the example to load. Read more at the links below. \nNote: These models are trained on SpeechMatrix data only, and meant to serve as a baseline for future research."
-
-article = "<p style='text-align: center'><a href='https://research.facebook.com/publications/speechmatrix' target='_blank'>SpeechMatrix</a> | <a href='https://github.com/facebookresearch/fairseq/tree/ust' target='_blank'>Github Repo</a></p>"
 
 SRC_LIST = ['cs', 'de', 'en', 'es', 'et', 'fi', 'fr', 'hr', 'hu', 'it', 'nl', 'pl', 'pt', 'ro', 'sk', 'sl']
 TGT_LIST = ['en', 'fr', 'es']
@@ -16,21 +8,17 @@ for src in SRC_LIST:
     for tgt in TGT_LIST:
         if src != tgt:
             MODEL_LIST.append(f"textless_sm_{src}_{tgt}")
-        
+
 examples = []
 
-io_dict = {model: gr.Interface.load(f"huggingface/facebook/{model}", api_key=os.environ['api_key']) for model in MODEL_LIST}
-   
+io_dict = {model: gr.load(f"huggingface/facebook/{model}") for model in MODEL_LIST}
+
 def inference(audio, model):
-    out_audio = io_dict[model](audio)   
-    return out_audio 
+    out_audio = io_dict[model](audio)
+    return out_audio
+
 gr.Interface(
     inference,
-    [gr.inputs.Audio(source="microphone", type="filepath", label="Input"),gr.inputs.Dropdown(choices=MODEL_LIST, default="xm_transformer_sm_all-en",type="value", label="Model")
+    [gr.Audio(source="microphone", type="filepath", label="Input"),gr.Dropdown(choices=MODEL_LIST, type="value", label="Model")
 ],
-    gr.outputs.Audio(label="Output"),
-    article=article,
-    title=title,
-    examples=examples,
-    cache_examples=False,
-    description=description).queue().launch()
+    gr.Audio(label="Output")).queue().launch()