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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import argparse
import getpass
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from collections import Counter, defaultdict
from typing import List
import cv2
import numpy as np
import tqdm
from apistar.exceptions import ErrorResponse
from kaldi_data_generator.image_utils import (
determine_rotate_angle,
download_image,
extract_min_area_rect_image,
extract_polygon_image,
from kaldi_data_generator.utils import (
CachedApiClient,
TranscriptionData,
logger,
write_file,
)
SEED = 42
random.seed(SEED)
MANUAL = "manual"
TEXT_LINE = "text_line"
WHITE = 255
ROTATION_CLASSES_TO_ANGLES = {
"rotate_0": 0,
"rotate_left_90": 90,
"rotate_180": 180,
"rotate_right_90": -90,
}
# return ArkindexClient(**options_from_env())
return CachedApiClient(cache_root=cache_dir, **options_from_env())
class Extraction(Enum):
boundingRect: int = 0
polygon: int = 1
# minimum containing rectangle with an angle (cv2.min_area_rect)
min_area_rect: int = 2
deskew_polygon: int = 3
deskew_min_area_rect: int = 4
skew_polygon: int = 5
skew_min_area_rect: int = 6
dataset_name="foo",
out_dir_base="/tmp/kaldi_data",
grayscale=True,
extraction=Extraction.boundingRect,
accepted_classes=None,
filter_printed=False,
skip_vertical_lines=False,
accepted_worker_version_ids=None,
transcription_type=TEXT_LINE,
max_deskew_angle=45,
scale_x=None,
scale_y_top=None,
scale_y_bottom=None,
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cache_dir=None,
self.out_dir_base = out_dir_base
self.dataset_name = dataset_name
self.grayscale = grayscale
self.extraction_mode = extraction
self.accepted_classes = accepted_classes
self.should_filter_by_class = bool(self.accepted_classes)
self.accepted_worker_version_ids = accepted_worker_version_ids
self.should_filter_by_worker = bool(self.accepted_worker_version_ids)
self.should_filter_printed = filter_printed
self.transcription_type = transcription_type
self.skip_vertical_lines = skip_vertical_lines
self.skipped_pages_count = 0
self.skipped_vertical_lines_count = 0
self.accepted_lines_count = 0
self.max_deskew_angle = max_deskew_angle
if scale_x or scale_y_top or scale_y_bottom:
self.should_resize_polygons = True
# use 1.0 as default - no resize, if not specified
self.scale_x = scale_x or DEFAULT_RESCALE
self.scale_y_top = scale_y_top or DEFAULT_RESCALE
self.scale_y_bottom = scale_y_bottom or DEFAULT_RESCALE
else:
self.should_resize_polygons = False
if MANUAL in self.accepted_worker_version_ids:
self.accepted_worker_version_ids[
self.accepted_worker_version_ids.index(MANUAL)
] = None
self.out_line_dir = out_dir_base
os.makedirs(self.out_line_dir, exist_ok=True)
else:
self.out_line_text_dir = os.path.join(
self.out_dir_base, "Transcriptions", self.dataset_name
)
os.makedirs(self.out_line_text_dir, exist_ok=True)
self.out_line_img_dir = os.path.join(
self.out_dir_base, "Lines", self.dataset_name
)
os.makedirs(self.out_line_img_dir, exist_ok=True)
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self.cache_dir = cache_dir
logger.info(f"Setting up cache to {self.cache_dir}")
self.img_cache_dir = self.cache_dir / "images"
self.img_cache_dir.mkdir(exist_ok=True, parents=True)
if not any(self.img_cache_dir.iterdir()):
logger.info("Cache is empty, no need to check")
self._cache_is_empty = True
else:
self._cache_is_empty = False
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self._color = "grayscale"
self._cv2_flag = cv2.IMREAD_GRAYSCALE
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self._color = "rgb"
self._cv2_flag = cv2.IMREAD_COLOR
def get_image(self, image_url: str, page_id: str) -> "np.ndarray":
# id is last part before full/full/0/default.jpg
img_id = image_url.split("/")[-5].replace("%2F", "/")
cached_img_path = self.img_cache_dir / self._color / img_id
if not self._cache_is_empty and cached_img_path.exists():
logger.info(f"Cached image exists: {cached_img_path} - {page_id}")
else:
logger.info(f"Image not in cache: {cached_img_path} - {page_id}")
cached_img_path.parent.mkdir(exist_ok=True, parents=True)
pil_img = download_image(image_url)
if self.grayscale:
pil_img = pil_img.convert("L")
pil_img.save(cached_img_path, format="jpeg")
img = cv2.imread(str(cached_img_path), self._cv2_flag)
return img
def get_accepted_zones(self, page_id: str):
try:
accepted_zones = []
if classification["ml_class"]["name"] == "handwritten":
printed = False
if classification["ml_class"]["name"] in self.accepted_classes:
if self.should_filter_printed:
if not printed:
accepted_zones.append(elt["zone"]["id"])
else:
accepted_zones.append(elt["zone"]["id"])
logger.info(
"Number of accepted zone for page {} : {}".format(
page_id, len(accepted_zones)
)
)
return accepted_zones
except ErrorResponse as e:
logger.info(
f"ListTranscriptions failed {e.status_code} - {e.title} - {e.content} - {page_id}"
)
raise e
def _validate_transcriptions(self, page_id: str, lines: List[TranscriptionData]):
if not lines:
return
line_elem_counter = Counter([trans.element_id for trans in lines])
most_common = line_elem_counter.most_common(10)
if most_common[0][-1] > 1:
logger.error("Line elements have multiple transcriptions! Showing top 10:")
logger.error(f"{most_common}")
raise ValueError(f"Multiple transcriptions: {most_common[0]}")
worker_version_counter = Counter([trans.worker_version_id for trans in lines])
if len(worker_version_counter) > 1:
logger.warning(
f"There are transcriptions from multiple worker versions on this page: {page_id}:"
)
logger.warning(
f"Top 10 worker versions: {worker_version_counter.most_common(10)}"
)
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def _choose_best_transcriptions(
self, lines: List[TranscriptionData]
) -> List[TranscriptionData]:
"""
Get the best transcription based on the order of accepted worker version ids.
:param lines:
:return:
"""
if not lines:
return []
trans_by_element = defaultdict(list)
for line in lines:
trans_by_element[line.element_id].append(line)
best_transcriptions = []
for elem, trans_list in trans_by_element.items():
tmp_dict = {t.worker_version_id: t for t in trans_list}
for wv in self.accepted_worker_version_ids:
if wv in tmp_dict:
best_transcriptions.append(tmp_dict[wv])
break
else:
logger.info(f"No suitable trans found for {elem}")
return best_transcriptions
def get_transcriptions(self, page_id: str, accepted_zones):
lines = []
try:
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"ListTranscriptions", id=page_id, recursive=True
):
if (
self.should_filter_by_worker
and res["worker_version_id"] not in self.accepted_worker_version_ids
):
continue
if (
self.should_filter_by_class
and res["element"]["zone"]["id"] not in accepted_zones
):
continue
if res["element"]["type"] != self.transcription_type:
continue
text = res["text"]
if not text or not text.strip():
continue
if "zone" in res:
polygon = res["zone"]["polygon"]
elif "element" in res:
polygon = res["element"]["zone"]["polygon"]
else:
raise ValueError(f"Data problem with polygon :: {res}")
trans_data = TranscriptionData(
element_id=res["element"]["id"],
polygon=polygon,
text=text,
trans_id=res["id"],
worker_version_id=res["worker_version_id"],
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if self.accepted_worker_version_ids:
# if accepted worker versions have been defined then use them
lines = self._choose_best_transcriptions(lines)
else:
# if no accepted worker versions have been defined
# then check that there aren't multiple transcriptions
# on the same text line
self._validate_transcriptions(page_id, lines)
if self.should_rotate:
classes_by_elem = self.get_children_classes(page_id)
for trans in lines:
rotation_classes = [
c
for c in classes_by_elem[trans.element_id]
if c in ROTATION_CLASSES_TO_ANGLES
]
if len(rotation_classes) > 0:
if len(rotation_classes) > 1:
logger.warning(
f"Several rotation classes = {len(rotation_classes)} - {trans.element_id}"
)
trans.rotation_class = rotation_classes[0]
else:
logger.warning(f"No rotation classes on {trans.element_id}")
count_skipped = 0
if self.skip_vertical_lines:
filtered_lines = []
for line in lines:
if line.is_vertical:
count_skipped += 1
continue
filtered_lines.append(line)
lines = filtered_lines
count = len(lines)
return lines, count, count_skipped
except ErrorResponse as e:
logger.info(
f"ListTranscriptions failed {e.status_code} - {e.title} - {e.content} - {page_id}"
)
raise e
def get_children_classes(self, page_id):
return {
elem["id"]: [
best_class["ml_class"]["name"]
"ListElementChildren",
id=page_id,
recursive=True,
type=TEXT_LINE,
)
}
def _save_line_image(
self, page_id, i, line_img, manifest_fp=None, trans: TranscriptionData = None
):
if self.should_rotate:
if trans.rotation_class:
rotate_angle = ROTATION_CLASSES_TO_ANGLES[trans.rotation_class]
line_img = self.rotate_and_trim(line_img, rotate_angle)
if self.format == "kraken":
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cv2.imwrite(f"{self.out_line_dir}/{page_id}_{i}.png", line_img)
manifest_fp.write(f"{page_id}_{i}.png\n")
else:
cv2.imwrite(f"{self.out_line_img_dir}/{page_id}_{i}.jpg", line_img)
def extract_lines(self, page_id: str, image_data: dict):
if self.should_filter_by_class:
accepted_zones = self.get_accepted_zones(page_id)
else:
accepted_zones = []
lines, count, count_skipped = self.get_transcriptions(page_id, accepted_zones)
if count == 0:
self.skipped_pages_count += 1
logger.info(f"Page {page_id} skipped, because it has no lines")
return
logger.debug(f"Total num of lines {count + count_skipped}")
logger.debug(f"Num of accepted lines {count}")
logger.debug(f"Num of skipped lines {count_skipped}")
self.skipped_vertical_lines_count += count_skipped
self.accepted_lines_count += count
full_image_url = image_data["s3_url"]
if full_image_url is None:
full_image_url = image_data["url"] + "/full/full/0/default.jpg"
img = self.get_image(full_image_url, page_id=page_id)
# sort vertically then horizontally
sorted_lines = sorted(lines, key=lambda key: (key.rect.y, key.rect.x))
if self.should_resize_polygons:
sorted_lines = [
resize_transcription_data(
line,
image_data["width"],
image_data["height"],
self.scale_x,
self.scale_y_top,
self.scale_y_bottom,
)
for line in sorted_lines
]
manifest_fp = open(f"{self.out_line_dir}/manifest.txt", "a")
# append to file, not re-write it
else:
# not needed for kaldi
manifest_fp = None
if self.extraction_mode == Extraction.boundingRect:
for i, trans in enumerate(sorted_lines):
(x, y, w, h) = trans.rect
self._save_line_image(page_id, i, cropped, manifest_fp, trans)
for i, trans in enumerate(sorted_lines):
polygon_img = extract_polygon_image(
img, polygon=trans.polygon, rect=trans.rect
)
self._save_line_image(page_id, i, polygon_img, manifest_fp, trans)
elif self.extraction_mode == Extraction.min_area_rect:
self._save_line_image(page_id, i, min_rect_img, manifest_fp, trans)
elif self.extraction_mode == Extraction.deskew_polygon:
rotate_angle = determine_rotate_angle(trans.polygon)
if abs(rotate_angle) > self.max_deskew_angle:
logger.warning(
f"Deskew angle ({rotate_angle}) over the limit ({self.max_deskew_angle}), won't rotate"
)
rotate_angle = 0
# get polygon image
polygon_img = extract_polygon_image(
img, polygon=trans.polygon, rect=trans.rect
)
trimmed_img = self.rotate_and_trim(polygon_img, rotate_angle)
self._save_line_image(page_id, i, trimmed_img, manifest_fp, trans)
elif self.extraction_mode == Extraction.deskew_min_area_rect:
rotate_angle = determine_rotate_angle(trans.polygon)
if abs(rotate_angle) > self.max_deskew_angle:
logger.warning(
f"Deskew angle ({rotate_angle}) over the limit ({self.max_deskew_angle}), won't rotate"
)
rotate_angle = 0
min_rect_img = extract_min_area_rect_image(
)
trimmed_img = self.rotate_and_trim(min_rect_img, rotate_angle)
self._save_line_image(page_id, i, trimmed_img, manifest_fp, trans)
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elif self.extraction_mode == Extraction.skew_polygon:
for i, trans in enumerate(sorted_lines):
rotate_angle = self.skew_angle
# get polygon image
polygon_img = extract_polygon_image(
img, polygon=trans.polygon, rect=trans.rect
)
trimmed_img = self.rotate_and_trim(polygon_img, rotate_angle)
self._save_line_image(page_id, i, trimmed_img, manifest_fp, trans)
elif self.extraction_mode == Extraction.skew_min_area_rect:
for i, trans in enumerate(sorted_lines):
rotate_angle = self.skew_angle
min_rect_img = extract_min_area_rect_image(
img, polygon=trans.polygon, rect=trans.rect
)
trimmed_img = self.rotate_and_trim(min_rect_img, rotate_angle)
self._save_line_image(page_id, i, trimmed_img, manifest_fp, trans)
else:
raise ValueError(f"Unsupported extraction mode: {self.extraction_mode}")
for i, trans in enumerate(sorted_lines):
if self.format == "kraken":
write_file(f"{self.out_line_dir}/{page_id}_{i}.gt.txt", trans.text)
write_file(f"{self.out_line_text_dir}/{page_id}_{i}.txt", trans.text)
def rotate_and_trim(self, img, rotate_angle):
"""
Rotate image by given an angle and trim extra whitespace left after rotating
"""
if self.grayscale:
background = WHITE
else:
background = (WHITE, WHITE, WHITE)
# rotate polygon image
deskewed_img = rotate(img, rotate_angle, background)
# trim extra whitespace left after rotating
trimmed_img = trim(deskewed_img, background)
trimmed_img = np.array(trimmed_img)
return trimmed_img
def run_pages(self, pages: list):
if all(isinstance(n, str) for n in pages):
for page in pages:
elt = self.api_client.request("RetrieveElement", id=page)
page_id = elt["id"]
image_data = elt["zone"]["image"]
logger.debug(f"Page {page_id}")
self.extract_lines(page_id, image_data)
else:
for page in tqdm.tqdm(pages):
page_id = page["id"]
image_data = page["zone"]["image"]
logger.debug(f"Page {page_id}")
self.extract_lines(page_id, image_data)
def run_volumes(self, volume_ids: list):
for volume_id in tqdm.tqdm(volume_ids):
logger.info(f"Volume {volume_id}")
pages = [
page
"ListElementChildren", id=volume_id, recursive=True, type="page"
)
]
self.run_pages(pages)
def run_folders(self, element_ids: list, volume_type: str):
for elem_id in tqdm.tqdm(element_ids):
logger.info(f"Folder {elem_id}")
vol_ids = [
page["id"]
"ListElementChildren", id=elem_id, recursive=True, type=volume_type
)
]
self.run_volumes(vol_ids)
def run_corpora(self, corpus_ids: list, volume_type: str):
for corpus_id in tqdm.tqdm(corpus_ids):
logger.info(f"Corpus {corpus_id}")
vol_ids = [
vol["id"]
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"ListElements", corpus=corpus_id, type=volume_type
)
]
self.run_volumes(vol_ids)
class Split(Enum):
Train: int = 0
Test: int = 1
Validation: int = 2
@property
def short_name(self) -> str:
if self == self.Validation:
return "val"
return self.name.lower()
class KaldiPartitionSplitter:
def __init__(
self,
out_dir_base="/tmp/kaldi_data",
split_train_ratio=0.8,
split_test_ratio=0.1,
use_existing_split=False,
):
self.out_dir_base = out_dir_base
self.split_train_ratio = split_train_ratio
self.split_test_ratio = split_test_ratio
self.split_val_ratio = 1 - self.split_train_ratio - self.split_test_ratio
self.use_existing_split = use_existing_split
def page_level_split(self, line_ids: list) -> dict:
# need to sort again, because `set` will lose the order
page_ids = sorted({"_".join(line_id.split("_")[:-1]) for line_id in line_ids})
random.Random(SEED).shuffle(page_ids)
page_count = len(page_ids)
train_page_ids = page_ids[: round(page_count * self.split_train_ratio)]
page_ids = page_ids[round(page_count * self.split_train_ratio) :]
test_page_ids = page_ids[: round(page_count * self.split_test_ratio)]
page_ids = page_ids[round(page_count * self.split_test_ratio) :]
val_page_ids = page_ids
page_dict = {page_id: Split.Train.value for page_id in train_page_ids}
page_dict.update({page_id: Split.Test.value for page_id in test_page_ids})
page_dict.update({page_id: Split.Validation.value for page_id in val_page_ids})
return page_dict
def existing_split(self, line_ids: list) -> list:
split_dict = {split.short_name: [] for split in Split}
for line_id in line_ids:
split_prefix = line_id.split("/")[0].lower()
split_dict[split_prefix].append(line_id)
splits = [split_dict[split.short_name] for split in Split]
return splits
def create_partitions(self):
logger.info("Creating partitions")
lines_path = Path(f"{self.out_dir_base}/Lines")
line_ids = [
str(file.relative_to(lines_path).with_suffix(""))
for file in sorted(lines_path.glob("**/*.jpg"))
]
if self.use_existing_split:
logger.info("Using existing split")
datasets = self.existing_split(line_ids)
else:
page_dict = self.page_level_split(line_ids)
datasets = [[] for _ in range(3)]
for line_id in line_ids:
page_id = "_".join(line_id.split("_")[:-1])
split_id = page_dict[page_id]
datasets[split_id].append(line_id)
partitions_dir = os.path.join(self.out_dir_base, "Partitions")
os.makedirs(partitions_dir, exist_ok=True)
for i, dataset in enumerate(datasets):
if not dataset:
logger.info(f"Partition {Split(i).name} is empty! Skipping..")
continue
file_name = f"{partitions_dir}/{Split(i).name}Lines.lst"
write_file(file_name, "\n".join(dataset) + "\n")
def create_parser():
user_name = getpass.getuser()
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parser = argparse.ArgumentParser(
description="Script to generate Kaldi or kraken training data from annotations from Arkindex",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"-f",
"--format",
type=str,
help="is the data generated going to be used for kaldi or kraken",
)
parser.add_argument(
"-n",
"--dataset_name",
type=str,
help="Name of the dataset being created for kaldi or kraken "
"(useful for distinguishing different datasets when in Lines or Transcriptions directory)",
)
parser.add_argument(
"-o", "--out_dir", type=str, required=True, help="output directory"
)
parser.add_argument(
"--train_ratio",
type=float,
default=0.8,
help="Ratio of pages to be used in train (between 0 and 1)",
)
parser.add_argument(
"--test_ratio",
type=float,
default=0.1,
help="Ratio of pages to be used in test (between 0 and 1 - train_ratio)",
)
parser.add_argument(
"--use_existing_split",
action="store_true",
default=False,
help="Use an existing split instead of random. "
"Expecting line_ids to be prefixed with (train, val and test)",
)
parser.add_argument(
"--split_only",
"--no_download",
action="store_true",
default=False,
help="Create the split from already downloaded lines, don't download the lines",
)
parser.add_argument(
"--no_split",
action="store_true",
default=False,
help="No splitting of the data to be done just download the line in the right format",
)
parser.add_argument(
"-e",
"--extraction_mode",
type=lambda x: Extraction[x],
default=Extraction.boundingRect,
help=f"Mode for extracting the line images: {[e.name for e in Extraction]}",
)
parser.add_argument(
"--max_deskew_angle",
type=int,
default=45,
help="Maximum angle by which deskewing is allowed to rotate the line image. "
"If the angle determined by deskew tool is bigger than max "
"then that line won't be deskewed/rotated.",
)
parser.add_argument(
"--skew_angle",
type=int,
default=0,
help="Angle by which the line image will be rotated. Useful for data augmnetation"
" - creating skewed text lines for a more robust model."
" Only used with skew_* extraction modes.",
)
parser.add_argument(
"--should_rotate",
action="store_true",
default=False,
help="Use text line rotation class to rotate lines if possible",
)
parser.add_argument(
"--transcription_type",
type=str,
default="text_line",
help="Which type of elements' transcriptions to use? (page, paragraph, text_line, etc)",
)
group = parser.add_mutually_exclusive_group(required=False)
group.add_argument(
"--grayscale",
action="store_true",
dest="grayscale",
help="Convert images to grayscale (By default grayscale)",
)
group.add_argument(
"--color", action="store_false", dest="grayscale", help="Use color images"
group.set_defaults(grayscale=True)
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parser.add_argument(
"--corpora",
nargs="*",
help="List of corpus ids to be used, separated by spaces",
)
parser.add_argument(
"--folders",
type=str,
nargs="*",
help="List of folder ids to be used, separated by spaces. "
"Elements of `volume_type` will be searched recursively in these folders",
)
parser.add_argument(
"--volumes",
nargs="*",
help="List of volume ids to be used, separated by spaces",
)
parser.add_argument(
"--pages", nargs="*", help="List of page ids to be used, separated by spaces"
)
parser.add_argument(
"-v",
"--volume_type",
type=str,
default="volume",
help="Volumes (1 level above page) may have a different name on corpora",
)
parser.add_argument(
"--skip_vertical_lines",
action="store_true",
default=False,
help="skips vertical lines when downloading",
)
parser.add_argument(
"--accepted_classes",
nargs="*",
help="List of accepted ml_class names. Filter lines by class of related elements",
)
parser.add_argument(
"--accepted_worker_version_ids",
nargs="*",
default=[],
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help="List of accepted worker version ids. Filter transcriptions by worker version ids."
"The order is important - only up to one transcription will be chosen per element (text_line)"
" and the worker version order defines the precedence. If there exists a transcription for"
" the first worker version then it will be chosen, otherwise will continue on to the next"
" worker version."
" Use `--accepted_worker_version_ids manual` to get only manual transcriptions",
)
parser.add_argument(
"--filter_printed",
action="store_true",
help="Filter lines annotated as printed",
)
parser.add_argument(
"--scale_x",
type=float,
default=None,
help="Ratio of how much to scale the polygon horizontally (1.0 means no rescaling)",
)
parser.add_argument(
"--scale_y_top",
type=float,
default=None,
help="Ratio of how much to scale the polygon vertically on the top (1.0 means no rescaling)",
)
parser.add_argument(
"--scale_y_bottom",
type=float,
default=None,
help="Ratio of how much to scale the polygon vertically on the bottom (1.0 means no rescaling)",
)
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parser.add_argument(
"--cache_dir",
type=Path,
default=Path(f"/tmp/kaldi_data_generator_{user_name}/cache/"),
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help="Cache dir where to save the full size downloaded images. Change it to force redownload.",
)
return parser
def main():
parser = create_parser()
args = parser.parse_args()
if not args.dataset_name and not args.split_only and not args.format == "kraken":
parser.error("--dataset_name must be specified (unless --split-only)")
logger.info(f"ARGS {args} \n")
if not args.split_only:
data_generator = HTRDataGenerator(
dataset_name=args.dataset_name,
out_dir_base=args.out_dir,
grayscale=args.grayscale,
extraction=args.extraction_mode,
accepted_classes=args.accepted_classes,
filter_printed=args.filter_printed,
skip_vertical_lines=args.skip_vertical_lines,
transcription_type=args.transcription_type,
accepted_worker_version_ids=args.accepted_worker_version_ids,
max_deskew_angle=args.max_deskew_angle,
scale_x=args.scale_x,
scale_y_top=args.scale_y_top,
scale_y_bottom=args.scale_y_bottom,
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cache_dir=args.cache_dir,
)
# extract all the lines and transcriptions
if args.pages:
data_generator.run_pages(args.pages)
if args.volumes:
data_generator.run_volumes(args.volumes)
if args.folders:
data_generator.run_folders(args.folders, args.volume_type)
if args.corpora:
data_generator.run_corpora(args.corpora, args.volume_type)
if data_generator.skipped_vertical_lines_count > 0:
logger.info(
f"Number of skipped pages: {data_generator.skipped_pages_count}"
)
_skipped_vertical_count = data_generator.skipped_vertical_lines_count
_total_count = _skipped_vertical_count + data_generator.accepted_lines_count
skipped_ratio = _skipped_vertical_count / _total_count * 100
f"Skipped {data_generator.skipped_vertical_lines_count} vertical lines ({round(skipped_ratio, 2)}%)"
)
else:
logger.info("Creating a split from already downloaded files")
if not args.no_split:
kaldi_partitioner = KaldiPartitionSplitter(
out_dir_base=args.out_dir,
split_train_ratio=args.train_ratio,
split_test_ratio=args.test_ratio,
use_existing_split=args.use_existing_split,
)
# create partitions from all the extracted data
kaldi_partitioner.create_partitions()
else:
logger.info("No split to be done")
logger.info("DONE")
if __name__ == "__main__":
main()