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utils_encode.py
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utils_encode.py
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import os
import numpy as np
import tensorflow as tf
from pydub import AudioSegment
from glob import glob
from tqdm import tqdm
from utils import Utils_functions
class UtilsEncode_functions:
def __init__(self, args):
self.args = args
self.U = Utils_functions(args)
self.paths = sorted(glob(self.args.files_path + "/*"))
def audio_generator(self):
for p in self.paths:
try:
tp, ext = os.path.splitext(p)
bname = os.path.basename(tp)
wvo = AudioSegment.from_file(p, format=ext[1:])
wvo = wvo.set_frame_rate(self.args.sr)
wvls = wvo.split_to_mono()
wvls = [s.get_array_of_samples() for s in wvls]
wv = np.array(wvls).T.astype(np.float32)
wv /= np.iinfo(wvls[0].typecode).max
yield np.squeeze(wv), bname
except Exception as e:
print(e)
print("Exception ignored! Continuing...")
pass
# def create_dataset(self):
# self.ds = (
# tf.data.Dataset.from_generator(
# self.audio_generator, output_signature=(tf.TensorSpec(shape=(None, 2), dtype=tf.float32))
# )
# .prefetch(tf.data.experimental.AUTOTUNE)
# .apply(tf.data.experimental.ignore_errors())
# )
def compress_files(self, models_ls=None):
critic, gen, enc, dec, enc2, dec2, gen_ema, [opt_dec, opt_disc], switch = models_ls
# self.create_dataset()
os.makedirs(self.args.save_path, exist_ok=True)
c = 0
time_compression_ratio = 16 # TODO: infer time compression ratio
shape2 = self.args.shape
pbar = tqdm(self.audio_generator(), position=0, leave=True, total=len(self.paths))
for (wv,bname) in pbar:
try:
if wv.shape[0] > self.args.hop * self.args.shape * 2 + 3 * self.args.hop:
split_limit = (
5 * 60 * self.args.sr
) # split very long waveforms (> 5 minutes) and process separately to avoid out of memory errors
nsplits = (wv.shape[0] // split_limit) + 1
wvsplits = []
for ns in range(nsplits):
if wv.shape[0] - (ns * split_limit) > self.args.hop * self.args.shape * 2 + 3 * self.args.hop:
wvsplits.append(wv[ns * split_limit : (ns + 1) * split_limit, :])
for wv in wvsplits:
wv = tf.image.random_crop(
wv,
size=[
(((wv.shape[0] - (3 * self.args.hop)) // (self.args.shape * self.args.hop)))
* self.args.shape
* self.args.hop
+ 3 * self.args.hop,
2,
],
)
chls = []
for channel in range(2):
x = wv[:, channel]
x = tf.expand_dims(tf.transpose(self.U.wv2spec(x, hop_size=self.args.hop), (1, 0)), -1)
ds = []
num = x.shape[1] // self.args.shape
rn = 0
for i in range(num):
ds.append(
x[:, rn + (i * self.args.shape) : rn + (i * self.args.shape) + self.args.shape, :]
)
del x
ds = tf.convert_to_tensor(ds, dtype=tf.float32)
lat = self.U.distribute_enc(ds, enc)
del ds
lat = tf.split(lat, lat.shape[0], 0)
lat = tf.concat(lat, -2)
lat = tf.squeeze(lat)
switch = False
if lat.shape[0] > (self.args.max_lat_len * time_compression_ratio):
switch = True
ds2 = []
num2 = lat.shape[-2] // shape2
rn2 = 0
for j in range(num2):
ds2.append(lat[rn2 + (j * shape2) : rn2 + (j * shape2) + shape2, :])
ds2 = tf.convert_to_tensor(ds2, dtype=tf.float32)
lat = self.U.distribute_enc(tf.expand_dims(ds2, -3), enc2)
del ds2
lat = tf.split(lat, lat.shape[0], 0)
lat = tf.concat(lat, -2)
lat = tf.squeeze(lat)
chls.append(lat)
if lat.shape[0] > self.args.max_lat_len and switch:
lat = tf.concat(chls, -1)
del chls
latc = lat[: (lat.shape[0] // self.args.max_lat_len) * self.args.max_lat_len, :]
latc = tf.split(latc, latc.shape[0] // self.args.max_lat_len, 0)
for el in latc:
np.save(self.args.save_path + f"/{bname}_{c}.npy", el)
c += 1
pbar.set_postfix({"Saved Files": c})
np.save(self.args.save_path + f"/{bname}_{c}.npy", lat[-self.args.max_lat_len :, :])
c += 1
pbar.set_postfix({"Saved Files": c})
del lat
del latc
except Exception as e:
print(e)
print("Exception ignored! Continuing...")
pass
def compress_whole_files(self, models_ls=None):
critic, gen, enc, dec, enc2, dec2, gen_ema, [opt_dec, opt_disc], switch = models_ls
# self.create_dataset()
os.makedirs(self.args.save_path, exist_ok=True)
c = 0
time_compression_ratio = 16 # TODO: infer time compression ratio
shape2 = self.args.shape
pbar = tqdm(self.audio_generator(), position=0, leave=True, total=len(self.paths))
for (wv,bname) in pbar:
try:
# wv_len_orig = wv.shape[0]
if wv.shape[0] > self.args.hop * self.args.shape * 2 + 3 * self.args.hop:
rem = (wv.shape[0] - (3 * self.args.hop)) % (self.args.shape * self.args.hop)
if rem != 0:
wv = tf.concat([wv, tf.zeros([rem,2], dtype=tf.float32)], 0)
chls = []
for channel in range(2):
x = wv[:, channel]
x = tf.expand_dims(tf.transpose(self.U.wv2spec(x, hop_size=self.args.hop), (1, 0)), -1)
ds = []
num = x.shape[1] // self.args.shape
rn = 0
for i in range(num):
ds.append(
x[:, rn + (i * self.args.shape) : rn + (i * self.args.shape) + self.args.shape, :]
)
del x
ds = tf.convert_to_tensor(ds, dtype=tf.float32)
lat = self.U.distribute_enc(ds, enc)
del ds
lat = tf.split(lat, lat.shape[0], 0)
lat = tf.concat(lat, -2)
lat = tf.squeeze(lat)
ds2 = []
num2 = lat.shape[-2] // shape2
rn2 = 0
for j in range(num2):
ds2.append(lat[rn2 + (j * shape2) : rn2 + (j * shape2) + shape2, :])
ds2 = tf.convert_to_tensor(ds2, dtype=tf.float32)
lat = self.U.distribute_enc(tf.expand_dims(ds2, -3), enc2)
del ds2
lat = tf.split(lat, lat.shape[0], 0)
lat = tf.concat(lat, -2)
lat = tf.squeeze(lat)
chls.append(lat)
lat = tf.concat(chls, -1)
del chls
np.save(self.args.save_path + f"/{bname}.npy", lat)
c += 1
pbar.set_postfix({"Saved Files": c})
del lat
except Exception as e:
print(e)
print("Exception ignored! Continuing...")
pass