Try this
import noisereduce
temp = noisereduce.reduce_noise(noise_clip=noise_clip,audio_clip=temp,verbose=True)
noise_clip
small part of the signal(sample of noise, maybe 1s frame_duration)
audio_clip
actual audio
signal, fs = librosa.load(path)
signln = len(signal)
avg_energy = np.sum(signal ** 2) / float(signln) #avg_energy of acual signal
f_d = 0.02 #frame duration
perc = 0.01
flag = True
j = 0
f_length = fs * f_d #frame length is `frame per second(fs) * frame_duration(f_d)`
signln = len(signal)
retsig = []
noise = signal[0:441] # just considering first little part as noise
avg_energy = np.sum(signal ** 2) / float(signln)
while j < signln:
subsig = signal[int(j): int(j) + int(f_length)]
average_energy = np.sum(subsig ** 2) / float(len(subsig)) # avg energy of current frame
if average_energy <= avg_energy: #if enegy of the current frame is less than actual signal then then we can confirm that this frame as silence or noise part
if flag: #to get first noise or silence appearing on the signal
noise = subsig #if you want to get all the noise frame, then just create a list and append it(noise_list.append(subsig)) and also don't use the flag condition
flag = False
else: # if avg energy of current frame is grater than actual signal energy then this frame contain the data
retsig.append(subsig) # so you need to add that frame to new variable
j += f_length