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import scipy.signal
import librosa
from df.enhance import enhance, init_df, load_audio, save_audio
import torch
from torchaudio.functional import resample
# Load default model
model, df_state, _ = init_df()
def smooth_and_reduce_noise(audio_signal, sampling_rate):
# Apply a low-pass filter for smoothing
cutoff_frequency = 1700 # Adjust as needed
nyquist = 0.5 * sampling_rate
normal_cutoff = cutoff_frequency / nyquist
b, a = scipy.signal.butter(4, normal_cutoff, btype='low', analog=False)
smoothed_signal = scipy.signal.filtfilt(b, a, audio_signal)
# Reduce noise using librosa's denoiser
denoised_signal = librosa.effects.preemphasis(smoothed_signal, coef=0.95)
return denoised_signal
def model_remove_noise(model, df_state, np_audio):
#Read audio
audio = torch.tensor([np_audio])
audio = resample(audio, 16000, df_state.sr())
#Inference
enhanced = enhance(model, df_state, audio).cpu().numpy()
#Save
dtype=torch.int16
out_audio = torch.as_tensor(enhanced)
if out_audio.ndim == 1:
out_audio.unsqueeze_(0)
if dtype == torch.int16 and out_audio.dtype != torch.int16:
out_audio = (out_audio * (1 << 15)).to(torch.int16)
if dtype == torch.float32 and out_audio.dtype != torch.float32:
out_audio = out_audio.to(torch.float32) / (1 << 15)
out_audio = resample(audio, df_state.sr(), 16000)
return out_audio.cpu().numpy() |