import librosa
import gradio as gr
import pedalboard
import soundfile as sf

def inference(audio):
    y, sr = librosa.load(audio.name, sr=44100)

    reverb = pedalboard.Reverb()
    reverb
    reverb.room_size
    reverb.wet_level = 1.0
    effected = reverb(y, sample_rate=sr)
    with sf.SoundFile('./processed-output-stereo.wav', 'w', samplerate=sr, channels=len(effected.shape)) as f:
        f.write(effected)
    return './processed-output-stereo.wav'


inputs = gr.inputs.Audio(label="Input Audio", type="file")
outputs =  gr.outputs.Audio(label="Output Audio", type="file")


title = "VITS"
description = "demo for VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech. 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/2106.06103'>Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech</a> | <a href='https://github.com/jaywalnut310/vits'>Github Repo</a></p>"


gr.Interface(inference, inputs, outputs, title=title, description=description, article=article).launch(debug=True)