def __init__(
self,
frame_length=3,
sample_rate=44100,
num_worker=1,
MUSDB18_PATH="",
BIG_DATA=False,
additional_background_data=[],
additional_vocal_data=[],
):
np.random.seed(1)
self.sample_rate = sample_rate
self.wh = WaveHandler()
self.BIG_DATA = BIG_DATA
self.music_folders = []
for each in additional_background_data:
self.music_folders += self.readList(each)
self.vocal_folders = []
for each in additional_vocal_data:
self.vocal_folders += self.readList(each)
self.frame_length = frame_length
self.bac_file_num = len(self.music_folders)
self.voc_file_num = len(self.vocal_folders)
self.num_worker = num_worker
self.mus = musdb.DB(MUSDB18_PATH, is_wav=True, subsets='train')
self.pitch_shift_high = [2.0, 3.0, 4.0, 5.0, 6.0, 7.0]
self.pitch_shift_low = [-2.0, -3.0, -4.0, -5.0, -6.0, -7.0]
def read_wav(estimate_fname, target_fname):
from util.wave_util import WaveHandler
wh = WaveHandler()
estimate = wh.read_wave(estimate_fname, channel=1)
truth = wh.read_wave(target_fname, channel=2)
min_length = min(estimate.shape[0], truth.shape[0])
estimate, truth = estimate[:min_length].reshape(
(1, min_length, 1)), truth[:min_length].reshape((1, min_length, 1))
return estimate, truth
def get_total_time_in_folder(path):
if (not path[-1] == '/'):
raise ValueError("Error: path should end with /")
wh = WaveHandler()
total_time = 0
for cnt, file in enumerate(os.listdir(path)):
total_time += wh.get_duration(path + file)
print("total: ")
print(total_time, "s")
print(total_time / 60, "min")
print(total_time / 3600, "h")
def delete_unproper_training_data(path):
if (not path[-1] == '/'):
raise ValueError("Error: path should end with /")
wh = WaveHandler()
files = os.listdir(path)
for cnt, each in enumerate(files):
file_pth = path + each
if (file_pth.split('.')[-1] == 'wav'):
judge = wh.get_channels_sampwidth_and_sample_rate(file_pth)
if (not judge[0]):
print(each, "Unproper! params:", judge[1])
os.remove(file_pth)
def eval_spleeter():
from evaluate.sdr import sdr_evaluate
wh = WaveHandler()
from evaluate.si_sdr_numpy import sdr, si_sdr
output_test_pth = Config.datahub_root + "musdb18hq/spleeter_out/test/"
mus_test_pth = Config.datahub_root + "musdb18hq/test/"
vocal = []
background = []
#
# for each in os.listdir(mus_train_pth):
# mus_dir = mus_train_pth + each + "/"
# out_dir = output_train_pth + each + "/output/combined/"
# # try:
# mus_vocal = wh.read_wave(mus_dir + "vocals.wav")
# mus_background = wh.read_wave(mus_dir + "background.wav")
# output_vocal = wh.read_wave(out_dir + "vocals.wav")
# output_background = wh.read_wave(out_dir + "accompaniment.wav")
#
# output_vocal, mus_vocal = unify(output_vocal, mus_vocal)
# output_background, mus_background = unify(output_background, mus_background)
#
# v = sdr(output_vocal, mus_vocal)
# b = sdr(output_background, mus_background)
# vocal.append(v)
# background.append(b)
# print("FileName: ",each, "\tSDR-VOCAL: ",v,"SDR-BACKGROUND: " ,b)
for each in sorted(os.listdir(musdb_test_pth)):
mus_dir = mus_test_pth + each + "/"
out_dir = output_test_pth + each + "/output/combined/"
# try:
mus_vocal = wh.read_wave(mus_dir + "vocals.wav")
mus_background = wh.read_wave(mus_dir + "background.wav")
output_vocal = wh.read_wave(out_dir + "vocals.wav")
output_background = wh.read_wave(out_dir + "accompaniment.wav")
output_vocal, mus_vocal = unify(output_vocal, mus_vocal)
output_background, mus_background = unify(output_background,
mus_background)
v = sdr(output_vocal, mus_vocal)
b = sdr(output_background, mus_background)
vocal.append(v)
background.append(b)
print("FileName: ", each, "\tSDR-BACKGROUND: ", b, "\tSDR-VOCAL: ", v)
# except:
# print("Error",each)
print("AVG-SDR-VOCAL", sum(vocal) / len(vocal))
print("AVG-SDR-BACKGROUND", sum(background) / len(background))
def seg_data():
wh = WaveHandler()
dir = Config.datahub_root + "song/441_song_data/"
seg_dir = Config.datahub_root + "song/seg_song_data/"
for cnt, fname in enumerate(os.listdir(dir)):
print("Doing segmentation on ", fname + "...")
unseg_f = dir + fname
data = wh.read_wave(unseg_f, channel=2)
length = data.shape[0]
for start in np.linspace(0, 0.95, 20):
seg_data = data[int(start * length):int((start + 0.05) * length)]
wh.save_wave(seg_data,
seg_dir + fname.split('.')[-2] + "_" +
str('%.2f' % start) + ".wav",
channels=2)
def get_total_time_in_txt(txtpath):
wh = WaveHandler()
cnt = 0
files = readList(txtpath)
total_time = 0
for file in files:
try:
total_time += wh.get_duration(file)
cnt += 1
except:
print("error:", file)
# print(total_time,"s")
# print(total_time/60,"min")
print(
txtpath.split('/')[-1].split('.')[-2], ",",
str(total_time / 3600) + "h,", cnt, ", " + txtpath)
return total_time / 3600, cnt
def __init__(
self,
frame_length=Config.frame_length,
sample_rate=Config.sample_rate,
num_worker=Config.num_workers,
sampleNo=20000,
mu=Config.mu,
empty_every_n=50,
sigma=Config.sigma,
alpha_low=Config.alpha_low,
alpha_high=Config.
alpha_high # If alpha_high get a value greater than 0.5, it would have probability to overflow
):
np.random.seed(1)
self.sample_rate = sample_rate
self.frame_length = frame_length
# self.music_folders = self.readList(Config.musdb_train_background)
self.music_folders = []
for each in Config.background_data:
self.music_folders += self.readList(each)
self.vocal_folders = []
for each in Config.vocal_data:
self.vocal_folders += self.readList(each)
# prev_data_size = len(self.vocal_folders)
# if(Config.exclude_list != ""):
# for each in self.readList(Config.exclude_list):
# self.vocal_folders.remove(each)
# print(prev_data_size-len(self.vocal_folders)," songs were removed from vocal datasets")
self.sample_length = int(self.sample_rate * self.frame_length)
self.cnt = 0
self.data_counter = 0
self.empty_every_n = empty_every_n
self.sampleNo = sampleNo
self.num_worker = num_worker
self.wh = WaveHandler()
# This alpha is to balance the energy between vocal and background
# Also, this alpha is used to simulate different energy leval between vocal and background
self.normal_distribution = np.random.normal(mu, sigma, sampleNo)
self.normal_distribution = self.normal_distribution[
self.normal_distribution > alpha_low]
self.normal_distribution = self.normal_distribution[
self.normal_distribution < alpha_high]
self.sampleNo = self.normal_distribution.shape[0]
def __init__(self):
self.vad = webrtcvad.Vad()
self.wh = WaveHandler()
self.kernal = np.ones(44100*1)/4410*5
self.threshold = 20
Config.trail_name + "/" + "data_background.txt")
write_list(
Config.vocal_data, Config.project_root + "saved_models/" +
Config.trail_name + "/" + "data_vocal.txt")
# Cache for data
freq_bac_loss_cache = []
freq_voc_loss_cache = []
freq_cons_loss_cache = []
best_sdr_vocal, best_sdr_background = Config.best_sdr_vocal, Config.best_sdr_background
# exclude_dict = load_json("config/json/ExcludeData.json")
# exclude_start_point,vocal_sisdr_min,vocal_sisdr_max,background_sisdr_min,background_sisdr_max = exclude_dict["start_exclude_point"],exclude_dict["vocal_sisdr"][0],exclude_dict["vocal_sisdr"][1],exclude_dict["background_sisdr"][0],exclude_dict["background_sisdr"][1]
wh = WaveHandler()
loss = torch.nn.L1Loss()
if (not Config.start_point == 0):
model = torch.load(Config.load_model_path + "/model" +
str(Config.start_point) + ".pkl",
map_location=Config.device)
else:
if (Config.split_band):
model = Spleeter(channels=2,
unet_inchannels=2 * Config.subband,
unet_outchannels=2 * Config.subband).cuda(
Config.device)
else:
model = Spleeter(channels=2, unet_inchannels=2,
unet_outchannels=2).cuda(Config.device)