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misc.py
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import torch
import matplotlib.pyplot as plt
import torchaudio
import math
import cdpam
import semetrics
import norbert
import scipy
from scipy.io.wavfile import write
from sys import platform
if platform == "linux":
from pypesq import pesq
from pystoi import stoi
from torchaudio.backend.sox_io_backend import load
from config import VOICEBANK_ROOT, VOICEBANK_ROOT, OUTPUT_FILES, MATLAB_ROOT, Config, hparams
from network_functions import *
from complexPyTorch.complexFunctions import complex_matmul
from data import *
from torch.utils.data import DataLoader
from tqdm import tqdm
config = Config()
partition = preprocess(config)
# torch.cuda.empty_cache()
RECEPTIVE_FEILD = 0
# Key is the name of the layer and the value is the
# array consisting of kernel size, stride and padding respectively
# alex_net = {
# 'conv1': [11, 4, 0],
# 'pool1': [3, 2, 0],
# 'conv2': [5, 1, 2],
# 'pool2': [3, 2, 0],
# 'conv3': [3, 1, 1],
# 'conv4': [3, 1, 1],
# 'conv5': [3, 1, 1],
# 'pool5': [3, 2, 0],
# 'fc6-conv': [6, 1, 0],
# 'fc7-conv': [1, 1, 0]
# }
# rv = {
# 'conv1': [7, 2, math.floor(7/2)],
# 'conv2': [7, 2, math.floor(7/2)],
# 'conv3': [5, 2, math.floor(5/2)],
# 'conv4': [5, 2, math.floor(5/2)],
# 'conv5': [3, 2, math.floor(3/2)],
# 'conv6': [3, 2, math.floor(3/2)],
# 'conv7': [3, 2, math.floor(3/2)]
# }
# class ReceptiveFieldCalculator():
# def calculate(self, architecture, input_image_size):
# input_layer = ('input_layer', input_image_size, 1, 1, 0.5)
# self._print_layer_info(input_layer)
# for key in architecture:
# current_layer = self._calculate_layer_info(architecture[key], input_layer, key)
# self._print_layer_info(current_layer)
# input_layer = current_layer
# def _print_layer_info(self, layer):
# print(f'------')
# print(f'{layer[0]}: n = {layer[1]}; r = {layer[2]}; j = {layer[3]}; start = {layer[4]}')
# print(f'------')
# def _calculate_layer_info(self, current_layer, input_layer, layer_name):
# n_in = input_layer[1]
# j_in = input_layer[2]
# r_in = input_layer[3]
# start_in = input_layer[4]
# k = current_layer[0]
# s = current_layer[1]
# p = current_layer[2]
# n_out = math.floor((n_in - k + 2*p)/s) + 1
# padding = (n_out-1)*s - n_in + k
# p_right = math.ceil(padding/2)
# p_left = math.floor(padding/2)
# j_out = j_in * s
# r_out = r_in + (k - 1)*j_in
# start_out = start_in + ((k-1)/2 - p_left)*j_in
# return layer_name, n_out, j_out, r_out, start_out
# calculator = ReceptiveFieldCalculator()
# calculator.calculate(rv, 256)
SCALING = 0
# orig = torch.FloatTensor(400, 400).uniform_(config.data_minR, config.data_maxR)
# # orig_view_real = torch.view_as_real(orig)
# print("orig min and max: ", torch.min(orig), torch.max(orig))
# scaled = (orig - config.data_minR) / (config.data_maxR - config.data_minR)
# print("scaled min and max: ", torch.min(scaled), torch.max(scaled))
# scaled_back = ((config.data_maxR + config.data_minR) * ((scaled - 0) / (1 - 0))) + config.data_minR
# print("scaled back min and max: ", torch.min(scaled_back), torch.max(scaled_back))
DATA_LOADING = 0
# orig, sr = load("/import/scratch-01/jhw31/DS_10283_1942/clean_trainset_wav/p287_420.wav")
# orig, sr = load(VOICEBANK_ROOT + "clean_testset_wav/" + "p232_010.wav")
# orig, sr = load("../DS_10283_1942_16/clean_trainset_wav/p226_001.wav")
# print(sr)
# write(OUTPUT_FILES + "voltest.wav", config.file_sr, orig[0].numpy())
# orig, sr = load(VOICEBANK_ROOT + "clean_testset_wav/" + "p232_010.wav", normalize=True)
# resampler = torchaudio.transforms.Resample(sr, config.sr, dtype=orig.dtype)
# resampled = resampler(orig)
# write(OUTPUT_FILES + "orig.wav", config.file_sr, orig[0].numpy())
# write(OUTPUT_FILES + "resampled.wav", config.sr, resampled[0].numpy())
# clean_audio, _ = load(VOICEBANK_ROOT + "clean_testset_wav/" + "p232_053.wav", normalize=True)
# clean_audio = torch.squeeze(clean_audio)
# if clean_audio.shape[0] % config.hop_length != 0:
# remainder = clean_audio.shape[0] % config.hop_length
# new_length = clean_audio.shape[0] - remainder
# clean_audio = clean_audio[:new_length]
# else:
# new_length = clean_audio.shape[0]
# noisy_audio, _ = load(VOICEBANK_ROOT + "noisy_testset_wav/" + "p232_053.wav", normalize=True)
# noisy_audio = torch.squeeze(noisy_audio)[:new_length]
# noise_audio = noisy_audio - clean_audio
# print(noise_audio.shape, noisy_audio.shape, clean_audio.shape, "\n")
# clean_data = torch.stft(clean_audio, n_fft=config.fft_size, hop_length=config.hop_length, \
# win_length=config.window_length, window=config.window, return_complex=True, \
# normalized=config.normalise_stft)
# noisy_data = torch.stft(noisy_audio, n_fft=config.fft_size, hop_length=config.hop_length, \
# win_length=config.window_length, window=config.window, return_complex=True, \
# normalized=config.normalise_stft)
# noise_data = torch.stft(noise_audio, n_fft=config.fft_size, hop_length=config.hop_length, \
# win_length=config.window_length, window=config.window, return_complex=True, \
# normalized=config.normalise_stft)
# print("clean_data mag: ", torch.min(torch.abs(clean_data)), torch.max(torch.abs(clean_data)))
# print("clean_data phase: ", torch.min(torch.angle(clean_data)), torch.max(torch.angle(clean_data)))
# print("clean_data real: ", torch.min(clean_data.real), torch.max(clean_data.real))
# print("clean_data imag: ", torch.min(clean_data.imag), torch.max(clean_data.imag))
# clean_data_db, ref = amp_tensor_to_dB_tensor(clean_data)
# print("clean_data_db: ", torch.min(torch.abs(clean_data_db)), torch.max(torch.abs(clean_data_db)))
# clean_data_recon = dB_tensor_to_amp_tensor(clean_data_db, ref)
# print("clean_data_recon: ", torch.min(torch.abs(clean_data_recon)), torch.max(torch.abs(clean_data_recon)))
# audio_recon = torch.istft(clean_data, n_fft=config.fft_size, hop_length=config.hop_length, \
# win_length=config.window_length, normalized=config.normalise_stft)
# write(OUTPUT_FILES + "audio_recon.wav", config.sr, audio_recon.cpu().numpy())
# clean_mag = torch.abs(clean_data)
# noisy_mag = torch.abs(noisy_data)
# noise_mag = torch.abs(noise_data)
# clean_phase = torch.angle(clean_data)
# noisy_phase = torch.angle(noisy_data)
# noise_phase = torch.angle(noise_data)
# PESQ_TESTING = 0 #https://www.microtronix.ca/pesq.html
# clean, sr = load("../test_files/Or272.wav", normalize=True)
# clean = torch.squeeze(clean)
# deg1, sr = load("../test_files/Dg001.wav", normalize=True)
# deg1 = torch.squeeze(deg1)
# deg2, sr = load("../test_files/Dg002.wav", normalize=True)
# deg2 = torch.squeeze(deg2)
# min_len = min(clean.shape[0], deg1.shape[0], deg2.shape[0])
# clean = clean[:min_len]
# deg1 = deg1[:min_len]
# deg2 = deg2[:min_len]
# pesq1 = pesq(clean.cpu().numpy(), deg1.cpu().numpy(), sr)
# pesq2 = pesq(clean.cpu().numpy(), deg2.cpu().numpy(), sr)
# print("pesq1: ", pesq1)
# print("pesq2: ", pesq2)
CSIG_CBAK_COVL_SEGSNR = 0
# csig_arr = []
# cbak_arr = []
# covl_arr = []
# snrseg_arr = []
# for step, ID in enumerate(tqdm(partition["test"])):
# noisy_audio, noisy_sr = load("/Volumes/HD/Project/R1/" + ID + ".wav", normalize=config.normalise_audio)
# clean_audio, clean_sr = load(VOICEBANK_ROOT + "clean_testset_wav/" + ID + ".wav", normalize=config.normalise_audio)
# csv_file = csv.reader(open("/Volumes/HD/Project/R1/start_points.csv", "r"))
# found = False
# for row in csv_file:
# if ID == row[0]:
# start_point = int(row[1])
# found = True
# if not found:
# raise RuntimeError('ID not found in csv')
# noisy_audio = torch.squeeze(noisy_audio)
# clean_audio = torch.squeeze(clean_audio)
# # noisy_audio = config.resample(noisy_audio.to(torch.float))
# clean_audio = config.resample(clean_audio.to(torch.float))
# data_len = clean_audio.shape[0]
# windowed_data_len = config.integer_win_size - config.hop_length
# clean_audio = clean_audio[start_point:start_point+windowed_data_len]
# # print("clean_audio.shape[0]: ", clean_audio.shape[0])
# # print("noisy_audio.shape[0]: ", noisy_audio.shape[0])
# if windowed_data_len > data_len:
# clean_data = torch.nn.functional.pad(clean_data, (0, windowed_data_len-data_len))
# write(OUTPUT_FILES + "clean_audio.wav", config.sr, clean_audio.numpy())
# write(OUTPUT_FILES + "noisy_audio.wav", config.sr, noisy_audio.numpy())
# result = composite(OUTPUT_FILES + "clean_audio.wav", OUTPUT_FILES + "noisy_audio.wav")
# if not isnan(result[0]):
# csig_arr.append(result[0])
# if not isnan(result[1]):
# cbak_arr.append(result[1])
# if not isnan(result[2]):
# covl_arr.append(result[2])
# if not isnan(result[3]):
# snrseg_arr.append(result[3])
# csig_avg = sum(csig_arr) / len(csig_arr)
# cbak_avg = sum(cbak_arr) / len(cbak_arr)
# covl_avg = sum(covl_arr) / len(covl_arr)
# snrseg_avg = sum(snrseg_arr) / len(snrseg_arr)
# print("csig_avg: ", csig_avg)
# print("cbak_avg: ", cbak_avg)
# print("covl_avg: ", covl_avg)
# print("snrseg_avg: ", snrseg_avg)
WIENER_AND_SPECSUB = 0
# for step, ID in enumerate(tqdm(partition["test"])):
# noisy_audio, data_sr = load(VOICEBANK_ROOT + "noisy_testset_wav/" + ID + ".wav", normalize=config.normalise_audio)
# clean_audio, data_sr = load(VOICEBANK_ROOT + "clean_testset_wav/" + ID + ".wav", normalize=config.normalise_audio)
# noisy_audio = torch.squeeze(noisy_audio)
# clean_audio = torch.squeeze(clean_audio)
# noisy_audio = config.resample(noisy_audio.to(torch.float))
# clean_audio = config.resample(clean_audio.to(torch.float))
# noisy_data = torch.stft(noisy_audio,
# n_fft=config.fft_size,
# hop_length=config.hop_length,
# win_length=config.window_length,
# window=config.window,
# return_complex=True,
# normalized=config.normalise_stft)
# clean_data = torch.stft(clean_audio,
# n_fft=config.fft_size,
# hop_length=config.hop_length,
# win_length=config.window_length,
# window=config.window,
# return_complex=True,
# normalized=config.normalise_stft)
# clean_mag = torch.abs(clean_data)
# clean_mag = torch.unsqueeze(torch.unsqueeze(clean_mag, 2), 2)
# noisy_data = torch.unsqueeze(noisy_data, 2)
# wiener_est = torch.tensor(norbert.wiener(clean_mag.numpy(), noisy_data.numpy(), iterations=1, use_softmask=True, eps=10e-8))
# wiener_est = torch.squeeze(torch.squeeze(wiener_est))
# est_audio = torch.istft(wiener_est, n_fft=config.fft_size, hop_length=config.hop_length, \
# win_length=config.window_length, normalized=config.normalise_stft)
# min_length = min(noisy_audio.shape[0], clean_audio.shape[0], est_audio.shape[0])
# est_audio = est_audio[:min_length]
# write("/import/scratch-01/jhw31/wiener/" + ID + ".wav", config.sr, est_audio.numpy())
pesq_noisy_arr = []
pesq_wiener_arr = []
stoi_noisy_arr = []
stoi_wiener_arr = []
for step, ID in enumerate(tqdm(partition["test"])):
noisy_audio, data_sr = load(VOICEBANK_ROOT + "noisy_testset_wav/" + ID + ".wav", normalize=config.normalise_audio)
clean_audio, data_sr = load(VOICEBANK_ROOT + "clean_testset_wav/" + ID + ".wav", normalize=config.normalise_audio)
noisy_audio = torch.squeeze(noisy_audio)
clean_audio = torch.squeeze(clean_audio)
noisy_audio = config.resample(noisy_audio.to(torch.float))
clean_audio = config.resample(clean_audio.to(torch.float))
noisy_data = torch.stft(noisy_audio,
n_fft=config.fft_size,
hop_length=config.hop_length,
win_length=config.window_length,
window=config.window,
return_complex=True,
normalized=config.normalise_stft)
clean_data = torch.stft(clean_audio,
n_fft=config.fft_size,
hop_length=config.hop_length,
win_length=config.window_length,
window=config.window,
return_complex=True,
normalized=config.normalise_stft)
clean_mag = torch.abs(clean_data)
clean_mag = torch.unsqueeze(torch.unsqueeze(clean_mag, 2), 2)
noisy_data = torch.unsqueeze(noisy_data, 2)
wiener_est = torch.tensor(norbert.wiener(clean_mag.numpy(), noisy_data.numpy(), iterations=1, use_softmask=True, eps=10e-8))
wiener_est = torch.squeeze(torch.squeeze(wiener_est))
est_audio = torch.istft(wiener_est, n_fft=config.fft_size, hop_length=config.hop_length, \
win_length=config.window_length, normalized=config.normalise_stft)
min_length = min(noisy_audio.shape[0], clean_audio.shape[0], est_audio.shape[0])
noisy_audio = noisy_audio[:min_length]
clean_audio = clean_audio[:min_length]
est_audio = est_audio[:min_length]
pesq_noisy = pesq(clean_audio.cpu().numpy(), noisy_audio.cpu().numpy(), config.sr)
pesq_wiener = pesq(clean_audio.cpu().numpy(), est_audio.cpu().numpy(), config.sr)
stoi_noisy = stoi(clean_audio.cpu().numpy(), noisy_audio.cpu().numpy(), config.sr)
stoi_wiener = stoi(clean_audio.cpu().numpy(), est_audio.cpu().numpy(), config.sr)
if not isnan(pesq_noisy):
pesq_noisy_arr.append(pesq_noisy)
if not isnan(pesq_wiener):
pesq_wiener_arr.append(pesq_wiener)
if not isnan(stoi_noisy):
stoi_noisy_arr.append(stoi_noisy)
if not isnan(stoi_wiener):
stoi_wiener_arr.append(stoi_wiener)
pesq_noisy_avg = sum(pesq_noisy_arr) / len(pesq_noisy_arr)
pesq_wiener_avg = sum(pesq_wiener_arr) / len(pesq_wiener_arr)
stoi_noisy_avg = sum(stoi_noisy_arr) / len(stoi_noisy_arr)
stoi_wiener_avg = sum(stoi_wiener_arr) / len(stoi_wiener_arr)
print("pesq_noisy avg: ", pesq_noisy_avg)
print("pesq_wiener avg: ", pesq_wiener_avg)
print("stoi_noisy avg: ", stoi_noisy_avg)
print("stoi_wiener avg: ", stoi_wiener_avg)
pesq_ss_arr = []
stoi_ss_arr = []
for step, ID in enumerate(tqdm(partition["test"])):
ss_audio, _ = load("/import/scratch-01/jhw31/" + "specsub/" + ID + ".wav", \
normalize=config.normalise_audio)
clean_audio, _ = load(VOICEBANK_ROOT + "clean_testset_wav/" + ID + ".wav", \
normalize=config.normalise_audio)
ss_audio = torch.squeeze(ss_audio)
clean_audio = torch.squeeze(clean_audio)
ss_audio = config.resample(ss_audio.to(torch.float))
clean_audio = config.resample(clean_audio.to(torch.float))
min_length = min(ss_audio.shape[0], clean_audio.shape[0])
ss_audio = ss_audio[:min_length]
clean_audio = clean_audio[:min_length]
pesq_ss = pesq(clean_audio.cpu().numpy(), ss_audio.cpu().numpy(), config.sr)
stoi_ss = stoi(clean_audio.cpu().numpy(), ss_audio.cpu().numpy(), config.sr)
if not isnan(pesq_ss):
pesq_ss_arr.append(pesq_ss)
if not isnan(stoi_ss):
stoi_ss_arr.append(stoi_ss)
pesq_ss_avg = sum(pesq_ss_arr) / len(pesq_ss_arr)
stoi_ss_avg = sum(stoi_ss_arr) / len(stoi_ss_arr)
print("pesq_ss avg: ", pesq_ss_avg)
print("stoi_ss avg: ", stoi_ss_avg)
LOSS_TESTING = 0
# print("cL1: ", cL1)
# caudio = torch.istft(ccomp, n_fft=config.fft_size, hop_length=config.hop_length, \
# win_length=config.window_length, normalized=config.normalise_stft)
# write(OUTPUT_FILES + "caudio.wav", config.sr, caudio.numpy())
# cSiSNR = config.SiSNR(noise_audio, caudio)
# print("cSiSNR: ", cSiSNR)
# cwSDR = config.wSDR(torch.unsqueeze(noisy_audio, 0), torch.unsqueeze(noise_audio, 0), torch.unsqueeze(caudio, 0))
# print("cwSDR: ", cwSDR)
# cSTOI = stoi(noise_audio.numpy(), caudio.numpy(), config.sr)
# print("cSTOI: ", cSTOI)
# clean_data_mod = torch.complex(clean_data[...,0], noisy_data[...,1])
# mod_clean_data = clean_data
# print(clean_data[...,1])
# L1_loss = config.noise_criterion(noisy_data, clean_data)
# print("L1 loss: ", L1_loss)
# altered = complex_mat_mult(noisy_data, mask)
# mod_audio = torch.istft(mod_clean_data, n_fft=config.fft_size, hop_length=config.hop_length, \
# win_length=config.window_length, normalized=config.normalise_stft)
# write(OUTPUT_FILES + "orig.wav", config.sr, clean_audio.numpy())
# write(OUTPUT_FILES + "mod.wav", config.file_sr, mod_audio.numpy())
# L1 = config.noise_criterion(torch.abs(clean_data), torch.abs(noisy_data))
# print("L1: ", L1)
# SiSNR = config.speech_criterion(clean_audio, noisy_audio)
# print("SiSNR: ", SiSNR)
# pesq_i = pesq(clean_data.cpu().numpy(), noisy_data.cpu().numpy(), 16000)
# print(pesq_i)
MATLAB_TESTING = 0
# clean_audio, _ = load(MATLAB_ROOT + "audio/" + "clean.wav", normalize=True)
# clean_audio = torch.squeeze(clean_audio)
# if clean_audio.shape[0] % config.hop_length != 0:
# remainder = clean_audio.shape[0] % config.hop_length
# new_length = clean_audio.shape[0] - remainder
# clean_audio = clean_audio[:50688]
# else:
# new_length = clean_audio.shape[0]
# noisy_audio, _ = load(MATLAB_ROOT + "audio/" + "noisy.wav", normalize=True)
# noisy_audio = torch.squeeze(noisy_audio)[:50688]
# noise_audio = noisy_audio - clean_audio
# MATLAB_enhanced_audio, _ = load(MATLAB_ROOT + "audio/" + "enhanced.wav", normalize=True)
# MATLAB_enhanced_audio = torch.squeeze(MATLAB_enhanced_audio)[:50688]
# print(noise_audio.shape[0], noisy_audio.shape[0], clean_audio.shape[0], MATLAB_enhanced_audio.shape[0], "\n")
# pesq_noisy = pesq(clean_audio.cpu().numpy(), noisy_audio.cpu().numpy(), config.sr)
# pesq_MATLAB_enhanced = pesq(clean_audio.cpu().numpy(), MATLAB_enhanced_audio.cpu().numpy(), config.sr)
# print("pesq_noisy: ", pesq_noisy)
# print("pesq_MATLAB_enhanced: ", pesq_MATLAB_enhanced)
# stoi_noisy = stoi(clean_audio.cpu().numpy(), noisy_audio.cpu().numpy(), config.sr)
# stoi_MATLAB_enhanced = stoi(clean_audio.cpu().numpy(), MATLAB_enhanced_audio.cpu().numpy(), config.sr)
# print("stoi_noisy: ", stoi_noisy)
# print("stoi_MATLAB_enhanced: ", stoi_MATLAB_enhanced)
# train_set = VoiceBankDataset(partition['train'], config, mode="train", seed=config.seed)
# train_loader = DataLoader(train_set, **config.data_params)
# validation_set = VoiceBankDataset(partition['val'], config, mode="val", seed=config.seed)
# validation_loader = DataLoader(validation_set, **config.data_params)
RESHAPING = 0
# e7 = torch.rand(32, 256, 2, 16)
# latent_shape = e7.shape
# flattened = torch.flatten(e7, 2, 3).permute(0, 2, 1)
# unflattened = flattened.permute(0, 2, 1).reshape(latent_shape[0], latent_shape[1], latent_shape[2], latent_shape[3])
# print("are tensors equal? ", torch.equal(e7, unflattened))
MIN_AND_MAX_OF_DATASET = 0
# maximum = 0
# minimum = 0
# for step, ID in enumerate(tqdm(partition["train"])):
# clean_data, _ = load(VOICEBANK_ROOT + "clean_trainset_wav/" + ID + ".wav", \
# normalize=config.normalise_audio)
# noisy_data, _ = load(VOICEBANK_ROOT + "clean_trainset_wav/" + ID + ".wav", \
# normalize=config.normalise_audio)
# clean_data = torch.squeeze(clean_data).float()
# noisy_data = torch.squeeze(noisy_data).float()
# noise_data = noisy_data - clean_data
# noise_data = torch.stft(noise_data,
# n_fft=config.fft_size,
# hop_length=config.hop_length,
# win_length=config.window_length,
# window=config.window,
# return_complex=True,
# normalized=config.normalise_stft)[1:int(config.fft_size / 2) + 1, :]
# noisy_data = torch.stft(noisy_data,
# n_fft=config.fft_size,
# hop_length=config.hop_length,
# win_length=config.window_length,
# window=config.window,
# return_complex=True,
# normalized=config.normalise_stft)[1:int(config.fft_size / 2) + 1, :]
# clean_data = torch.stft(clean_data,
# n_fft=config.fft_size,
# hop_length=config.hop_length,
# win_length=config.window_length,
# window=config.window,
# return_complex=True,
# normalized=config.normalise_stft)[1:int(config.fft_size / 2) + 1, :]
# # noise_max = torch.max(torch.view_as_real(noise_data))
# # noisy_max = torch.max(torch.view_as_real(noisy_data))
# # clean_max = torch.max(torch.view_as_real(clean_data))
# # noise_min = torch.min(torch.view_as_real(noise_data))
# # noisy_min = torch.min(torch.view_as_real(noisy_data))
# # clean_min = torch.min(torch.view_as_real(clean_data))
# noise_max = torch.max(torch.abs(noise_data))
# noisy_max = torch.max(torch.abs(noisy_data))
# clean_max = torch.max(torch.abs(clean_data))
# noise_min = torch.min(torch.abs(noise_data))
# noisy_min = torch.min(torch.abs(noisy_data))
# clean_min = torch.min(torch.abs(clean_data))
# max_max = max(noise_max, noisy_max, clean_max)
# min_min = min(noise_min, noisy_min, clean_min)
# if max_max > maximum:
# maximum = max_max
# if min_min < minimum:
# minimum = min_min
# for step, ID in enumerate(tqdm(partition["val"])):
# clean_data, _ = load(VOICEBANK_ROOT + "clean_trainset_wav/" + ID + ".wav", \
# normalize=config.normalise_audio)
# noisy_data, _ = load(VOICEBANK_ROOT + "clean_trainset_wav/" + ID + ".wav", \
# normalize=config.normalise_audio)
# clean_data = torch.squeeze(clean_data).float()
# noisy_data = torch.squeeze(noisy_data).float()
# noise_data = noisy_data - clean_data
# noise_data = torch.stft(noise_data,
# n_fft=config.fft_size,
# hop_length=config.hop_length,
# win_length=config.window_length,
# window=config.window,
# return_complex=True,
# normalized=config.normalise_stft)[1:int(config.fft_size / 2) + 1, :]
# noisy_data = torch.stft(noisy_data,
# n_fft=config.fft_size,
# hop_length=config.hop_length,
# win_length=config.window_length,
# window=config.window,
# return_complex=True,
# normalized=config.normalise_stft)[1:int(config.fft_size / 2) + 1, :]
# clean_data = torch.stft(clean_data,
# n_fft=config.fft_size,
# hop_length=config.hop_length,
# win_length=config.window_length,
# window=config.window,
# return_complex=True,
# normalized=config.normalise_stft)[1:int(config.fft_size / 2) + 1, :]
# # noise_max = torch.max(torch.view_as_real(noise_data))
# # noisy_max = torch.max(torch.view_as_real(noisy_data))
# # clean_max = torch.max(torch.view_as_real(clean_data))
# # noise_min = torch.min(torch.view_as_real(noise_data))
# # noisy_min = torch.min(torch.view_as_real(noisy_data))
# # clean_min = torch.min(torch.view_as_real(clean_data))
# noise_max = torch.max(torch.abs(noise_data))
# noisy_max = torch.max(torch.abs(noisy_data))
# clean_max = torch.max(torch.abs(clean_data))
# noise_min = torch.min(torch.abs(noise_data))
# noisy_min = torch.min(torch.abs(noisy_data))
# clean_min = torch.min(torch.abs(clean_data))
# max_max = max(noise_max, noisy_max, clean_max)
# min_min = min(noise_min, noisy_min, clean_min)
# if max_max > maximum:
# maximum = max_max
# if min_min < minimum:
# minimum = min_min
# print("max: ", maximum)
# print("min: ", minimum)