Real world application, upsampling historic recordings?

[go-dustin]

Hi I've been testing your model for a side project I'm working on. I'd like to take early historic recordings (1890-1920s), denoise & upsample them. I've already denoised them (amazingly so!), I'm trying to upsample using your model but it doesn't seem to be doing much. I used the code from the code lab and the config that's in the repo.

Is this not a good application of the model or did I do something incorrectly?

Here is the results I produced
example_and_prediction_wav_files.zip

Spectrogram - Top is the example wav (Thomas Edison speaking 1912), bottom is the prediction. I can't hear a discernible difference and I'm well versed in audio engineering.

from infer import *

set_hparams(config='config.yaml')

model = WaveGlowMelHF(**hparams['waveglow_config']).cuda()

load_ckpt(model, 'model_ckpt_best.pt')
model.eval()

fns = ['te_small.wav']

sigma = 1
for lr_fn in fns:
lr, sr = load_wav(lr_fn)
print(f'sampling rate (lr) = {sr}')
print(f'lr.shape = {lr.shape}', flush=True)
with torch.no_grad():
pred = run(model, lr, sigma=sigma)
print(lr.shape, pred.shape)
pred_fn = f'pred_{lr_fn}'
print(f'sampling rate = {sr * 2}')
sf.write(open(pred_fn, 'wb'), pred, sr * 2)