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# import spaces
import gradio as gr
from audiosr import super_resolution, build_model
import torch
import gc # free up memory
# @spaces.GPU(duration=180)
def inference(audio_file, model_name, guidance_scale, ddim_steps, seed):
audiosr = build_model(model_name=model_name)
if torch.cuda.is_available():
torch.cuda.empty_cache() # empty cuda cache
gc.collect()
# set random seed when seed input value is 0
if seed == 0:
import random
seed = random.randint(1, 2**32-1)
waveform = super_resolution(
audiosr,
audio_file,
seed,
guidance_scale=guidance_scale,
ddim_steps=ddim_steps
)
if torch.cuda.is_available():
torch.cuda.empty_cache()
gc.collect()
return (48000, waveform)
iface = gr.Interface(
fn=inference,
inputs=[
gr.Audio(type="filepath", label="Input Audio"),
gr.Dropdown(["basic", "speech"], value="basic", label="Model"),
gr.Slider(1, 10, value=3.5, step=0.1, label="Guidance Scale", info="Guidance scale (Large => better quality and relavancy to text; Small => better diversity)"),
gr.Slider(1, 100, value=50, step=1, label="DDIM Steps", info="The sampling step for DDIM"),
gr.Number(value=42, precision=0, label="Seed", info="Changing this value (any integer number) will lead to a different generation result, put 0 for a random one.")
],
outputs=gr.Audio(type="numpy", label="Output Audio"),
title="AudioSR",
description="Audio Super Resolution with AudioSR"
)
iface.launch(share=False) |