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