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. 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_en-hk", api_key=st.secrets["api_key"])
io2 = gr.Interface.load("huggingface/facebook/xm_transformer_s2ut_hk-en", api_key=st.secrets["api_key"])
io3 = gr.Interface.load("huggingface/facebook/xm_transformer_unity_en-hk", api_key=st.secrets["api_key"])
io4 = gr.Interface.load("huggingface/facebook/xm_transformer_unity_hk-en", api_key=st.secrets["api_key"])
   
def inference(audio, model):
    if model == "xm_transformer_s2ut_en-hk":
        out_audio = io1(text)
    elif model == "xm_transformer_s2ut_hk-en":
        out_audio = io2(text)
    elif model == "xm_transformer_unity_en-hk":
        out_audio = io3(text)
    else:
        out_audio = io4(text)
    return out_audio 


gr.Interface(
    inference,
    [gr.inputs.Audio(source="microphone", type="filepath", label="Input"),gr.inputs.Dropdown(choices=["xm_transformer_s2ut_en-hk", "xm_transformer_s2ut_hk-en", "xm_transformer_unity_en-hk", "xm_transformer_unity_en-hk"], default="xm_transformer_s2ut_en-hk",type="value", label="Model")
],
    gr.outputs.Audio(label="Output"),
    article=article,
    title=title,
    description=description).queue().launch()