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import gradio as gr
import numpy as np
import resampy
import torch
import torchaudio
from huggingface_hub import hf_hub_download
from deepafx_st.system import System
from deepafx_st.utils import DSPMode
system_speech = System.load_from_checkpoint(
hf_hub_download("nateraw/deepafx-st-libritts-autodiff", "lit_model.ckpt"), batch_size=1
).eval()
system_music = System.load_from_checkpoint(
hf_hub_download("nateraw/deepafx-st-jamendo-autodiff", "lit_model.ckpt"), batch_size=1
).eval()
gpu = torch.cuda.is_available()
if gpu:
system_speech.to("cuda")
system_music.to("cuda")
def process(input_path, reference_path, model):
system = system_speech if model == "speech" else system_music
# load audio data
x, x_sr = torchaudio.load(input_path)
r, r_sr = torchaudio.load(reference_path)
# resample if needed
if x_sr != 24000:
print("Resampling to 24000 Hz...")
x_24000 = torch.tensor(resampy.resample(x.view(-1).numpy(), x_sr, 24000))
x_24000 = x_24000.view(1, -1)
else:
x_24000 = x
if r_sr != 24000:
print("Resampling to 24000 Hz...")
r_24000 = torch.tensor(resampy.resample(r.view(-1).numpy(), r_sr, 24000))
r_24000 = r_24000.view(1, -1)
else:
r_24000 = r
# peak normalize to -12 dBFS
x_24000 = x_24000[0:1, : 24000 * 5]
x_24000 /= x_24000.abs().max()
x_24000 *= 10 ** (-12 / 20.0)
x_24000 = x_24000.view(1, 1, -1)
# peak normalize to -12 dBFS
r_24000 = r_24000[0:1, : 24000 * 5]
r_24000 /= r_24000.abs().max()
r_24000 *= 10 ** (-12 / 20.0)
r_24000 = r_24000.view(1, 1, -1)
if gpu:
x_24000 = x_24000.to("cuda")
r_24000 = r_24000.to("cuda")
with torch.no_grad():
y_hat, p, e = system(x_24000, r_24000)
y_hat = y_hat.view(1, -1)
y_hat /= y_hat.abs().max()
x_24000 /= x_24000.abs().max()
# Sqeeze to (T,), convert to numpy, and convert to int16
out_audio = (32767 * y_hat).squeeze(0).detach().cpu().numpy().astype(np.int16)
return 24000, out_audio
gr.Interface(
fn=process,
inputs=[gr.Audio(type="filepath"), gr.Audio(type="filepath"), gr.Dropdown(["speech", "music"], value="speech")],
outputs="audio",
examples=[
[
hf_hub_download("nateraw/examples", "voice_raw.wav", repo_type="dataset", cache_dir="./data"),
hf_hub_download("nateraw/examples", "voice_produced.wav", repo_type="dataset", cache_dir="./data"),
"speech",
],
[
hf_hub_download("nateraw/examples", "nys_of_mind.wav", repo_type="dataset", cache_dir="./data"),
hf_hub_download("nateraw/examples", "world_is_yours_highpass.wav", repo_type="dataset", cache_dir="./data"),
"music",
],
],
title="DeepAFx-ST",
description=(
"Gradio demo for DeepAFx-ST for style transfer of audio effects with differentiable signal processing. To use it, simply"
" upload your audio files or choose from one of the examples. Read more at the links below."
),
article=(
"<div style='text-align: center;'><a href='https://github.com/adobe-research/DeepAFx-ST' target='_blank'>Github Repo</a>"
" <center><img src='https://visitor-badge.glitch.me/badge?page_id=nateraw_deepafx-st' alt='visitor"
" badge'></center></div>"
),
allow_flagging="never",
cache_examples=False
).launch()
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