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import gradio as gr
import torch
import numpy as np
from torchaudio.functional import resample
from processAudio import upscaleAudio
class Object(object):
pass
with gr.Blocks(theme=gr.themes.Default().set(body_background_fill="#CCEEFF")) as layout:
with gr.Row():
gr.Markdown("<h2>Broadcast Audio Upscaler</h2>")
with gr.Row():
with open("html/directions.html", "r") as directionsHtml:
gr.Markdown(directionsHtml.read())
with gr.Row():
modelSelect = gr.Dropdown(
[
["FM Radio Super Resolution","FM_Radio_SR.th"],
["AM Radio Super Resolution (Beta v2)","AM_Radio_SR.th"],
["Telephone Super Resolution (Beta)","Telephone_SR.th"]
],
label="Select Model:",
value="FM_Radio_SR.th",
)
with gr.Row():
with gr.Column():
audioFileSelect = gr.Audio(label="Audio File (Mono or Stereo, Max 6 Minutes):",sources="upload", max_length=360)
with gr.Column():
audioOutput = gr.Audio(show_download_button=True, label="Restored Audio:", sources=[], max_length=360)
with gr.Row():
with gr.Column():
submit = gr.Button("Process Audio", variant="primary", interactive=False)
with gr.Row():
with gr.Accordion("More Information:", open=False):
with open("html/information.html", "r") as informationHtml:
gr.Markdown(informationHtml.read())
@audioFileSelect.input(inputs=audioFileSelect, outputs=[submit, audioFileSelect])
def audioFileSelectChanged(audioData: gr.Audio):
#Audio exists and is mono or stereo
if audioData is None:
return gr.update(interactive=False), None
if len(audioData[1].shape) == 1:
return gr.update(interactive=True), audioData
if audioData[1].shape[1] > 2:
gr.Warning("Audio with more than 2 channels is not supported.")
return gr.update(interactive=False), None
return gr.update(interactive=True), audioData
@submit.click(inputs=[modelSelect, audioFileSelect], outputs=audioOutput)
def processAudio(model: gr.Dropdown, audioData: gr.Audio):
if audioData is None:
raise gr.Error("Load an audio file.")
return None
elif len(audioData[1].shape) == 1: #Convert mono to stereo
lrAudio = torch.tensor(np.array([
audioData[1].copy().astype(np.float32)/32768,
audioData[1].copy().astype(np.float32)/32768
]))
elif audioData[1].shape[1] > 2:
raise gr.Error("Audio with more than 2 channels is not supported.")
return None
else: #re-order channel data from [samples, 2] to [2, samples]
lrAudio = torch.tensor(audioData[1].copy().astype(np.float32)/32768).transpose(0,1)
if audioData[0] != 44100:
lrAudio = resample(lrAudio, audioData[0], 44100)
model_name, experiment_file = getModelInfo(model)
hrAudio=upscaleAudio(lrAudio, model, model_name=model_name, experiment_file=experiment_file)
hrAudio=hrAudio / max(hrAudio.abs().max().item(), 1)
outAudio=(hrAudio*32767).numpy().astype(np.int16).transpose(1,0)
return tuple([44100, outAudio])
def getModelInfo(modelFilename: str):
if(modelFilename == "FM_Radio_SR.th"):
return "aero", "aero_441-441_512_256.yaml"
if(modelFilename == "AM_Radio_SR.th"):
return "aero", "aero_441-441_512_256.yaml"
if(modelFilename == "Telephone_SR.th"):
return "aero", "aero_441-441_512_256.yaml"
return "aero", "aero_441-441_512_256.yaml"
layout.launch() |