Problem about using denoiser as preprocess

[lfgogogo]

Hi,thanks for the great job,denoiser can really denoise.
But when i use it as the preprocess of asr ,the result is worse,i use denoiser as bellow:

from denoiser.demucs import Demucs
import torch
import time
import torchaudio
from scipy.io.wavfile import write

ROOT = "https://dl.fbaipublicfiles.com/adiyoss/denoiser/"
DNS_48_URL = ROOT + "dns48-11decc9d8e3f0998.th"
DNS_64_URL = ROOT + "dns64-a7761ff99a7d5bb6.th"
MASTER_64_URL = ROOT + "master64-8a5dfb4bb92753dd.th"

def _demucs(pretrained, url, **kwargs):
    model = Demucs(**kwargs, sample_rate=16_000)
    if pretrained:
        state_dict = torch.hub.load_state_dict_from_url(url, map_location='cpu')
        model.load_state_dict(state_dict)
    return model

def dns48(pretrained=True):
    return _demucs(pretrained, DNS_48_URL, hidden=48)

def dns64(pretrained=True):
    return _demucs(pretrained, DNS_64_URL, hidden=64)

def master64(pretrained=True):
    return _demucs(pretrained, MASTER_64_URL, hidden=64)

if __name__=='__main__':
    model = master64().cuda().eval()
    if model is None:
        print('model is none')
    x,_=torchaudio.load(r'noise.wav')
    out=model(x.cuda())
    #ASR
    write(r'denoise.wav', 16000,out[0][0].cpu().detach().numpy())

Do i miss any important process?Or someone has a similar problem as me?Hope for answer.

[Alexandre Défossez]

can you make sure that the sample rate of your wav files are indeed sampled at 16000 Hz ? that could be one issue.

[lfgogogo]

Of course，they are 16000 Hz.

[Alexandre Défossez]

if the noise level is not super high, it is possible that the artefacts from the separation are actually hurting the ASR performance. for noisy samples though i would expect it to work. also it depends if your ASR model was trained on noisy data or not.