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on
Zero
Running
on
Zero
import os | |
from typing import Union | |
import soundfile as sf | |
import torch | |
import torchaudio | |
from modules.Denoiser.AudioDenoiser import AudioDenoiser | |
from modules.devices import devices | |
from modules.utils.constants import MODELS_DIR | |
ad: Union[AudioDenoiser, None] = None | |
class TTSAudioDenoiser: | |
def load_ad(self): | |
global ad | |
if ad is None: | |
ad = AudioDenoiser( | |
os.path.join( | |
MODELS_DIR, | |
"Denoise", | |
"audio-denoiser-512-32-v1", | |
), | |
device=devices.device, | |
) | |
ad.model.to(devices.device) | |
return ad | |
def denoise(self, audio_data, sample_rate, auto_scale=False): | |
ad = self.load_ad() | |
sr = ad.model_sample_rate | |
return sr, ad.process_waveform(audio_data, sample_rate, auto_scale) | |
if __name__ == "__main__": | |
tts_deno = TTSAudioDenoiser() | |
data, sr = sf.read("test.wav") | |
audio_tensor = torch.from_numpy(data).unsqueeze(0).float() | |
print(audio_tensor) | |
# data, sr = torchaudio.load("test.wav") | |
# print(data) | |
# data = data.to(devices.device) | |
sr, denoised = tts_deno.denoise(audio_data=audio_tensor, sample_rate=sr) | |
denoised = denoised.cpu() | |
torchaudio.save("denoised.wav", denoised, sample_rate=sr) | |