import os
os.system("pip install gradio==2.4.6")
import gradio as gr

title = "fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit"

description = "Gradio Demo for fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."

article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2109.06912' target='_blank'>fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit</a> | <a href='https://github.com/pytorch/fairseq/tree/main/examples/speech_synthesis' target='_blank'>Github Repo</a></p>"

examples = [
    ["Hello this is a test run","fastspeech2-en-200_speaker-cv4"],
    ["Hello, this is a test run.","tts_transformer-en-200_speaker-cv4"],
]

io1 = gr.Interface.load("huggingface/facebook/fastspeech2-en-200_speaker-cv4")

io2 = gr.Interface.load("huggingface/facebook/tts_transformer-en-200_speaker-cv4")

io3 = gr.Interface.load("huggingface/facebook/tts_transformer-fr-cv7_css10")

io4 = gr.Interface.load("huggingface/facebook/tts_transformer-ru-cv7_css10")

io5 = gr.Interface.load("huggingface/facebook/tts_transformer-tr-cv7")





    
def inference(text,model):
   if model == "fastspeech2-en-200_speaker-cv4":
        outtext = io1(text)
   elif model == "tts_transformer-en-200_speaker-cv4":
        outtext = io2(text)
   elif model == "tts_transformer-fr-cv7_css10":
        outtext = io3(text)
   elif model == "tts_transformer-ru-cv7_css10":
        outtext = io4(text)
   else:
        outtext = io5(text)
   return outtext 


gr.Interface(
    inference, 
    [gr.inputs.Textbox(label="Input",lines=5),gr.inputs.Dropdown(choices=["fastspeech2-en-200_speaker-cv4","tts_transformer-en-200_speaker-cv4","tts_transformer-zh-cv7_css10","tts_transformer-fr-cv7_css10","tts_transformer-ru-cv7_css10"], type="value", default="fastspeech2-en-200_speaker-cv4", label="model")
],
    gr.outputs.Audio(label="Output"),
    examples=examples,
    article=article,
    title=title,
    description=description,
    enable_queue=True).launch()