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app.py

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+import os
+os.system("pip install gradio==3.3")
+import gradio as gr
+import numpy as np
+import streamlit as st
+
+title = "Fairseq Speech to Speech Translation"
+
+description = "Gradio Demo for fairseq S2S: speech-to-speech translation models. To use it, simply record your audio, or click the example to load. Read more at the links below."
+
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2107.05604' target='_blank'>Direct speech-to-speech translation with discrete units</a> | <a href='https://github.com/facebookresearch/fairseq/tree/main/examples/speech_to_speech' target='_blank'>Github Repo</a></p>"
+
+examples = [
+  ["enhanced_direct_s2st_units_audios_es-en_set2_source_12478_cv.flac","xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022"],
+]
+
+io1 = gr.Interface.load("huggingface/facebook/xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022", api_key=st.secrets["api_key"])
+   
+def inference(audio, model):
+#    if mic is not None and file is None:
+#        audio = mic
+#    elif file is not None and mic is None:
+#        audio = file
+#    else:
+#        return "ERROR: You must and may only select one method, it cannot be empty or select both methods at once."
+    out_audio = io1(audio)   
+    return out_audio 
+
+
+gr.Interface(
+    inference,
+    [gr.inputs.Audio(source="microphone", type="filepath", label="Input"),gr.inputs.Dropdown(choices=["xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022"], default="xm_transformer_s2ut_800m-es-en-st-asr-bt_h1_2022",type="value", label="Model")
+],
+    gr.outputs.Audio(label="Output"),
+    article=article,
+    title=title,
+    examples=examples,
+    description=description).queue().launch()