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import pathlib
import yaml
import torch
import torchaudio
import numpy as np
from lightning_module import SSLDualLightningModule
import gradio as gr
import subprocess
def normalize_waveform(wav, sr, db=-3):
wav, _ = torchaudio.sox_effects.apply_effects_tensor(
wav.unsqueeze(0),
sr,
[["norm", "{}".format(db)]],
)
return wav.squeeze(0)
def calc_spectrogram(wav, config):
spec_module = torchaudio.transforms.MelSpectrogram(
sample_rate=config["preprocess"]["sampling_rate"],
n_fft=config["preprocess"]["fft_length"],
win_length=config["preprocess"]["frame_length"],
hop_length=config["preprocess"]["frame_shift"],
f_min=config["preprocess"]["fmin"],
f_max=config["preprocess"]["fmax"],
n_mels=config["preprocess"]["n_mels"],
power=1,
center=True,
norm="slaney",
mel_scale="slaney",
)
specs = spec_module(wav)
log_spec = torch.log(
torch.clamp_min(specs, config["preprocess"]["min_magnitude"])
* config["preprocess"]["comp_factor"]
).to(torch.float32)
return log_spec
def transfer(audio):
wp_src = pathlib.Path("aet_sample/src.wav")
wav_src, sr = torchaudio.load(wp_src)
sr_inp, wav_tar = audio
wav_tar = wav_tar / (np.max(np.abs(wav_tar)) * 1.1)
wav_tar = torch.from_numpy(wav_tar.astype(np.float32))
resampler = torchaudio.transforms.Resample(
orig_freq=sr_inp,
new_freq=sr,
)
wav_tar = resampler(wav_tar)
config_path = pathlib.Path("configs/test/melspec/ssl_tono.yaml")
config = yaml.load(open(config_path, "r"), Loader=yaml.FullLoader)
melspec_src = calc_spectrogram(
normalize_waveform(wav_src.squeeze(0), sr), config
)
wav_tar = normalize_waveform(wav_tar.squeeze(0), sr)
ckpt_path = pathlib.Path("aet_sample/tono_melspec_aet.ckpt")
src_model = SSLDualLightningModule(config).load_from_checkpoint(
checkpoint_path=ckpt_path,
config=config,
)
encoder_src = src_model.encoder
channelfeats_src = src_model.channelfeats
channel_src = src_model.channel
_, enc_hidden_src = encoder_src(
melspec_src.unsqueeze(0).unsqueeze(1).transpose(2, 3)
)
chfeats_src = channelfeats_src(enc_hidden_src)
wav_transfer = channel_src(wav_tar.unsqueeze(0), chfeats_src)
wav_transfer = wav_transfer.detach().numpy()[0, :]
return sr, wav_transfer
if __name__ == "__main__":
subprocess.run(["curl", "-OL", "https://sarulab.sakura.ne.jp/saeki/selfremaster/pretrained/tono_aet_melspec.ckpt"])
subprocess.run(["mv", "tono_aet_melspec.ckpt", "aet_sample"])
iface = gr.Interface(
transfer,
"audio",
gr.outputs.Audio(type="numpy"),
examples=[
["aet_sample/tar.wav"]
],
title='Audio effect transfer demo',
description='Add channel feature of Japanese old audio recording to any high-quality audio'
)
iface.launch() |