Commit
•
4902179
1
Parent(s):
e1e7365
Delete loading script
Browse files- superb_demo.py +0 -435
superb_demo.py
DELETED
@@ -1,435 +0,0 @@
|
|
1 |
-
# coding=utf-8
|
2 |
-
# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
|
3 |
-
#
|
4 |
-
# Licensed under the Apache License, Version 2.0 (the "License");
|
5 |
-
# you may not use this file except in compliance with the License.
|
6 |
-
# You may obtain a copy of the License at
|
7 |
-
#
|
8 |
-
# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
-
#
|
10 |
-
# Unless required by applicable law or agreed to in writing, software
|
11 |
-
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
-
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
-
# See the License for the specific language governing permissions and
|
14 |
-
# limitations under the License.
|
15 |
-
|
16 |
-
# Lint as: python3
|
17 |
-
"""SUPERB: Speech processing Universal PERformance Benchmark."""
|
18 |
-
|
19 |
-
import csv
|
20 |
-
import glob
|
21 |
-
import os
|
22 |
-
import textwrap
|
23 |
-
|
24 |
-
import datasets
|
25 |
-
from datasets.tasks import AutomaticSpeechRecognition
|
26 |
-
|
27 |
-
_CITATION = """\
|
28 |
-
@article{DBLP:journals/corr/abs-2105-01051,
|
29 |
-
author = {Shu{-}Wen Yang and
|
30 |
-
Po{-}Han Chi and
|
31 |
-
Yung{-}Sung Chuang and
|
32 |
-
Cheng{-}I Jeff Lai and
|
33 |
-
Kushal Lakhotia and
|
34 |
-
Yist Y. Lin and
|
35 |
-
Andy T. Liu and
|
36 |
-
Jiatong Shi and
|
37 |
-
Xuankai Chang and
|
38 |
-
Guan{-}Ting Lin and
|
39 |
-
Tzu{-}Hsien Huang and
|
40 |
-
Wei{-}Cheng Tseng and
|
41 |
-
Ko{-}tik Lee and
|
42 |
-
Da{-}Rong Liu and
|
43 |
-
Zili Huang and
|
44 |
-
Shuyan Dong and
|
45 |
-
Shang{-}Wen Li and
|
46 |
-
Shinji Watanabe and
|
47 |
-
Abdelrahman Mohamed and
|
48 |
-
Hung{-}yi Lee},
|
49 |
-
title = {{SUPERB:} Speech processing Universal PERformance Benchmark},
|
50 |
-
journal = {CoRR},
|
51 |
-
volume = {abs/2105.01051},
|
52 |
-
year = {2021},
|
53 |
-
url = {https://arxiv.org/abs/2105.01051},
|
54 |
-
archivePrefix = {arXiv},
|
55 |
-
eprint = {2105.01051},
|
56 |
-
timestamp = {Thu, 01 Jul 2021 13:30:22 +0200},
|
57 |
-
biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib},
|
58 |
-
bibsource = {dblp computer science bibliography, https://dblp.org}
|
59 |
-
}
|
60 |
-
"""
|
61 |
-
|
62 |
-
_DESCRIPTION = """\
|
63 |
-
Self-supervised learning (SSL) has proven vital for advancing research in
|
64 |
-
natural language processing (NLP) and computer vision (CV). The paradigm
|
65 |
-
pretrains a shared model on large volumes of unlabeled data and achieves
|
66 |
-
state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the
|
67 |
-
speech processing community lacks a similar setup to systematically explore the
|
68 |
-
paradigm. To bridge this gap, we introduce Speech processing Universal
|
69 |
-
PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the
|
70 |
-
performance of a shared model across a wide range of speech processing tasks
|
71 |
-
with minimal architecture changes and labeled data. Among multiple usages of the
|
72 |
-
shared model, we especially focus on extracting the representation learned from
|
73 |
-
SSL due to its preferable re-usability. We present a simple framework to solve
|
74 |
-
SUPERB tasks by learning task-specialized lightweight prediction heads on top of
|
75 |
-
the frozen shared model. Our results demonstrate that the framework is promising
|
76 |
-
as SSL representations show competitive generalizability and accessibility
|
77 |
-
across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a
|
78 |
-
benchmark toolkit to fuel the research in representation learning and general
|
79 |
-
speech processing.
|
80 |
-
|
81 |
-
Note that in order to limit the required storage for preparing this dataset, the
|
82 |
-
audio is stored in the .flac format and is not converted to a float32 array. To
|
83 |
-
convert, the audio file to a float32 array, please make use of the `.map()`
|
84 |
-
function as follows:
|
85 |
-
|
86 |
-
|
87 |
-
```python
|
88 |
-
import soundfile as sf
|
89 |
-
|
90 |
-
def map_to_array(batch):
|
91 |
-
speech_array, _ = sf.read(batch["file"])
|
92 |
-
batch["speech"] = speech_array
|
93 |
-
return batch
|
94 |
-
|
95 |
-
dataset = dataset.map(map_to_array, remove_columns=["file"])
|
96 |
-
```
|
97 |
-
"""
|
98 |
-
|
99 |
-
|
100 |
-
class SuperbConfig(datasets.BuilderConfig):
|
101 |
-
"""BuilderConfig for Superb."""
|
102 |
-
|
103 |
-
def __init__(
|
104 |
-
self,
|
105 |
-
features,
|
106 |
-
url,
|
107 |
-
data_url=None,
|
108 |
-
supervised_keys=None,
|
109 |
-
task_templates=None,
|
110 |
-
**kwargs,
|
111 |
-
):
|
112 |
-
super().__init__(version=datasets.Version("1.9.0", ""), **kwargs)
|
113 |
-
self.features = features
|
114 |
-
self.data_url = data_url
|
115 |
-
self.url = url
|
116 |
-
self.supervised_keys = supervised_keys
|
117 |
-
self.task_templates = task_templates
|
118 |
-
|
119 |
-
|
120 |
-
class Superb(datasets.GeneratorBasedBuilder):
|
121 |
-
"""Superb dataset."""
|
122 |
-
|
123 |
-
BUILDER_CONFIGS = [
|
124 |
-
SuperbConfig(
|
125 |
-
name="asr",
|
126 |
-
description=textwrap.dedent(
|
127 |
-
"""\
|
128 |
-
ASR transcribes utterances into words. While PR analyzes the
|
129 |
-
improvement in modeling phonetics, ASR reflects the significance of
|
130 |
-
the improvement in a real-world scenario. LibriSpeech
|
131 |
-
train-clean-100/dev-clean/test-clean subsets are used for
|
132 |
-
training/validation/testing. The evaluation metric is word error
|
133 |
-
rate (WER)."""
|
134 |
-
),
|
135 |
-
features=datasets.Features(
|
136 |
-
{
|
137 |
-
"file": datasets.Value("string"),
|
138 |
-
"audio": datasets.features.Audio(sampling_rate=16_000),
|
139 |
-
"text": datasets.Value("string"),
|
140 |
-
"speaker_id": datasets.Value("int64"),
|
141 |
-
"chapter_id": datasets.Value("int64"),
|
142 |
-
"id": datasets.Value("string"),
|
143 |
-
}
|
144 |
-
),
|
145 |
-
supervised_keys=("file", "text"),
|
146 |
-
url="http://www.openslr.org/12",
|
147 |
-
data_url="data/LibriSpeech-test-clean.zip",
|
148 |
-
),
|
149 |
-
SuperbConfig(
|
150 |
-
name="ks",
|
151 |
-
description=textwrap.dedent(
|
152 |
-
"""\
|
153 |
-
Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of
|
154 |
-
words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and
|
155 |
-
inference time are all crucial. SUPERB uses the widely used Speech Commands dataset v1.0 for the task.
|
156 |
-
The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the
|
157 |
-
false positive. The evaluation metric is accuracy (ACC)"""
|
158 |
-
),
|
159 |
-
features=datasets.Features(
|
160 |
-
{
|
161 |
-
"file": datasets.Value("string"),
|
162 |
-
"audio": datasets.features.Audio(sampling_rate=16_000),
|
163 |
-
"label": datasets.ClassLabel(
|
164 |
-
names=[
|
165 |
-
"yes",
|
166 |
-
"no",
|
167 |
-
"up",
|
168 |
-
"down",
|
169 |
-
"left",
|
170 |
-
"right",
|
171 |
-
"on",
|
172 |
-
"off",
|
173 |
-
"stop",
|
174 |
-
"go",
|
175 |
-
"_silence_",
|
176 |
-
"_unknown_",
|
177 |
-
]
|
178 |
-
),
|
179 |
-
}
|
180 |
-
),
|
181 |
-
supervised_keys=("file", "label"),
|
182 |
-
url="https://www.tensorflow.org/datasets/catalog/speech_commands",
|
183 |
-
data_url="data/speech_commands_test_set_v0.01.zip",
|
184 |
-
),
|
185 |
-
SuperbConfig(
|
186 |
-
name="ic",
|
187 |
-
description=textwrap.dedent(
|
188 |
-
"""\
|
189 |
-
Intent Classification (IC) classifies utterances into predefined classes to determine the intent of
|
190 |
-
speakers. SUPERB uses the Fluent Speech Commands dataset, where each utterance is tagged with three intent
|
191 |
-
labels: action, object, and location. The evaluation metric is accuracy (ACC)."""
|
192 |
-
),
|
193 |
-
features=datasets.Features(
|
194 |
-
{
|
195 |
-
"file": datasets.Value("string"),
|
196 |
-
"audio": datasets.features.Audio(sampling_rate=16_000),
|
197 |
-
"speaker_id": datasets.Value("string"),
|
198 |
-
"text": datasets.Value("string"),
|
199 |
-
"action": datasets.ClassLabel(
|
200 |
-
names=["activate", "bring", "change language", "deactivate", "decrease", "increase"]
|
201 |
-
),
|
202 |
-
"object": datasets.ClassLabel(
|
203 |
-
names=[
|
204 |
-
"Chinese",
|
205 |
-
"English",
|
206 |
-
"German",
|
207 |
-
"Korean",
|
208 |
-
"heat",
|
209 |
-
"juice",
|
210 |
-
"lamp",
|
211 |
-
"lights",
|
212 |
-
"music",
|
213 |
-
"newspaper",
|
214 |
-
"none",
|
215 |
-
"shoes",
|
216 |
-
"socks",
|
217 |
-
"volume",
|
218 |
-
]
|
219 |
-
),
|
220 |
-
"location": datasets.ClassLabel(names=["bedroom", "kitchen", "none", "washroom"]),
|
221 |
-
}
|
222 |
-
),
|
223 |
-
# no default supervised keys, since there are 3 labels
|
224 |
-
supervised_keys=None,
|
225 |
-
url="https://fluent.ai/fluent-speech-commands-a-dataset-for-spoken-language-understanding-research/",
|
226 |
-
data_url="data/fluent_speech_commands_dataset.zip",
|
227 |
-
),
|
228 |
-
SuperbConfig(
|
229 |
-
name="si",
|
230 |
-
description=textwrap.dedent(
|
231 |
-
"""\
|
232 |
-
Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class
|
233 |
-
classification, where speakers are in the same predefined set for both training and testing. The widely
|
234 |
-
used VoxCeleb1 dataset is adopted, and the evaluation metric is accuracy (ACC)."""
|
235 |
-
),
|
236 |
-
features=datasets.Features(
|
237 |
-
{
|
238 |
-
"file": datasets.Value("string"),
|
239 |
-
"audio": datasets.features.Audio(sampling_rate=16_000),
|
240 |
-
"label": datasets.ClassLabel(names=[f"id{i + 10001}" for i in range(1251)]),
|
241 |
-
}
|
242 |
-
),
|
243 |
-
supervised_keys=("file", "label"),
|
244 |
-
url="https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html",
|
245 |
-
data_url="data/VoxCeleb1.zip"
|
246 |
-
),
|
247 |
-
SuperbConfig(
|
248 |
-
name="er",
|
249 |
-
description=textwrap.dedent(
|
250 |
-
"""\
|
251 |
-
Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset
|
252 |
-
IEMOCAP is adopted, and we follow the conventional evaluation protocol: we drop the unbalance emotion
|
253 |
-
classes to leave the final four classes with a similar amount of data points and cross-validates on five
|
254 |
-
folds of the standard splits. The evaluation metric is accuracy (ACC)."""
|
255 |
-
),
|
256 |
-
features=datasets.Features(
|
257 |
-
{
|
258 |
-
"file": datasets.Value("string"),
|
259 |
-
"audio": datasets.features.Audio(sampling_rate=16_000),
|
260 |
-
"label": datasets.ClassLabel(names=['neu', 'hap', 'ang', 'sad']),
|
261 |
-
}
|
262 |
-
),
|
263 |
-
supervised_keys=("file", "label"),
|
264 |
-
url="https://sail.usc.edu/iemocap/",
|
265 |
-
data_url="data/IEMOCAP_full_release.zip"
|
266 |
-
),
|
267 |
-
]
|
268 |
-
|
269 |
-
def _info(self):
|
270 |
-
return datasets.DatasetInfo(
|
271 |
-
description=_DESCRIPTION,
|
272 |
-
features=self.config.features,
|
273 |
-
supervised_keys=self.config.supervised_keys,
|
274 |
-
homepage=self.config.url,
|
275 |
-
citation=_CITATION,
|
276 |
-
task_templates=self.config.task_templates,
|
277 |
-
)
|
278 |
-
|
279 |
-
def _split_generators(self, dl_manager):
|
280 |
-
if self.config.name == "asr":
|
281 |
-
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
282 |
-
return [
|
283 |
-
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path}),
|
284 |
-
]
|
285 |
-
elif self.config.name == "ks":
|
286 |
-
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
287 |
-
return [
|
288 |
-
datasets.SplitGenerator(
|
289 |
-
name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"}
|
290 |
-
),
|
291 |
-
]
|
292 |
-
elif self.config.name == "ic":
|
293 |
-
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
294 |
-
return [
|
295 |
-
datasets.SplitGenerator(
|
296 |
-
name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"}
|
297 |
-
),
|
298 |
-
]
|
299 |
-
elif self.config.name == "si":
|
300 |
-
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
301 |
-
return [
|
302 |
-
datasets.SplitGenerator(
|
303 |
-
name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": 3}
|
304 |
-
),
|
305 |
-
]
|
306 |
-
elif self.config.name == "sd":
|
307 |
-
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
308 |
-
return [
|
309 |
-
datasets.SplitGenerator(
|
310 |
-
name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"}
|
311 |
-
)
|
312 |
-
]
|
313 |
-
elif self.config.name == "er":
|
314 |
-
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
315 |
-
return [
|
316 |
-
datasets.SplitGenerator(
|
317 |
-
name="session1", gen_kwargs={"archive_path": archive_path, "split": 1},
|
318 |
-
)
|
319 |
-
]
|
320 |
-
|
321 |
-
def _generate_examples(self, archive_path, split=None):
|
322 |
-
"""Generate examples."""
|
323 |
-
if self.config.name == "asr":
|
324 |
-
transcripts_glob = os.path.join(archive_path, "LibriSpeech", "*/*/*/*.txt")
|
325 |
-
key = 0
|
326 |
-
for transcript_path in sorted(glob.glob(transcripts_glob)):
|
327 |
-
transcript_dir_path = os.path.dirname(transcript_path)
|
328 |
-
with open(transcript_path, "r", encoding="utf-8") as f:
|
329 |
-
for line in f:
|
330 |
-
line = line.strip()
|
331 |
-
id_, transcript = line.split(" ", 1)
|
332 |
-
audio_file = f"{id_}.flac"
|
333 |
-
speaker_id, chapter_id = [int(el) for el in id_.split("-")[:2]]
|
334 |
-
audio_path = os.path.join(transcript_dir_path, audio_file)
|
335 |
-
yield key, {
|
336 |
-
"id": id_,
|
337 |
-
"speaker_id": speaker_id,
|
338 |
-
"chapter_id": chapter_id,
|
339 |
-
"file": audio_path,
|
340 |
-
"audio": audio_path,
|
341 |
-
"text": transcript,
|
342 |
-
}
|
343 |
-
key += 1
|
344 |
-
elif self.config.name == "ks":
|
345 |
-
words = ["yes", "no", "up", "down", "left", "right", "on", "off", "stop", "go"]
|
346 |
-
splits = _split_ks_files(archive_path, split)
|
347 |
-
for key, audio_file in enumerate(sorted(splits[split])):
|
348 |
-
base_dir, file_name = os.path.split(audio_file)
|
349 |
-
_, word = os.path.split(base_dir)
|
350 |
-
if word in words:
|
351 |
-
label = word
|
352 |
-
elif word == "_silence_" or word == "_background_noise_":
|
353 |
-
label = "_silence_"
|
354 |
-
else:
|
355 |
-
label = "_unknown_"
|
356 |
-
yield key, {"file": audio_file, "audio": audio_file, "label": label}
|
357 |
-
elif self.config.name == "ic":
|
358 |
-
root_path = os.path.join(archive_path, "fluent_speech_commands_dataset/")
|
359 |
-
csv_path = os.path.join(root_path, f"data/{split}_data.csv")
|
360 |
-
with open(csv_path, encoding="utf-8") as csv_file:
|
361 |
-
csv_reader = csv.reader(csv_file, delimiter=",", skipinitialspace=True)
|
362 |
-
next(csv_reader)
|
363 |
-
for row in csv_reader:
|
364 |
-
key, file_path, speaker_id, text, action, object_, location = row
|
365 |
-
audio_path = os.path.join(root_path, file_path)
|
366 |
-
yield key, {
|
367 |
-
"file": audio_path,
|
368 |
-
"audio": audio_path,
|
369 |
-
"speaker_id": speaker_id,
|
370 |
-
"text": text,
|
371 |
-
"action": action,
|
372 |
-
"object": object_,
|
373 |
-
"location": location,
|
374 |
-
}
|
375 |
-
elif self.config.name == "si":
|
376 |
-
wav_path = os.path.join(archive_path, "wav/")
|
377 |
-
splits_path = os.path.join(archive_path, "veri_test_class.txt")
|
378 |
-
with open(splits_path, "r", encoding="utf-8") as f:
|
379 |
-
for key, line in enumerate(f):
|
380 |
-
split_id, file_path = line.strip().split(" ")
|
381 |
-
if int(split_id) != split:
|
382 |
-
continue
|
383 |
-
speaker_id = file_path.split("/")[0]
|
384 |
-
audio_path = os.path.join(wav_path, file_path)
|
385 |
-
yield key, {
|
386 |
-
"file": audio_path,
|
387 |
-
"audio": audio_path,
|
388 |
-
"label": speaker_id,
|
389 |
-
}
|
390 |
-
elif self.config.name == "er":
|
391 |
-
root_path = os.path.join(archive_path, f"Session{split}/")
|
392 |
-
wav_path = os.path.join(root_path, "sentences/wav/")
|
393 |
-
labels_path = os.path.join(root_path, "dialog/EmoEvaluation/*.txt")
|
394 |
-
emotions = ['neu', 'hap', 'ang', 'sad', 'exc']
|
395 |
-
key = 0
|
396 |
-
for labels_file in sorted(glob.glob(labels_path)):
|
397 |
-
with open(labels_file, "r", encoding="utf-8") as f:
|
398 |
-
for line in f:
|
399 |
-
if line[0] != "[":
|
400 |
-
continue
|
401 |
-
_, filename, emo, _ = line.split("\t")
|
402 |
-
if emo not in emotions:
|
403 |
-
continue
|
404 |
-
wav_subdir = filename.rsplit("_", 1)[0]
|
405 |
-
filename = f"{filename}.wav"
|
406 |
-
audio_path = os.path.join(wav_path, wav_subdir, filename)
|
407 |
-
yield key, {
|
408 |
-
"file": audio_path,
|
409 |
-
"audio": audio_path,
|
410 |
-
"label": emo.replace("exc", "hap"),
|
411 |
-
}
|
412 |
-
key += 1
|
413 |
-
|
414 |
-
|
415 |
-
def _split_ks_files(archive_path, split):
|
416 |
-
audio_path = os.path.join(archive_path, "**/*.wav")
|
417 |
-
audio_paths = glob.glob(audio_path)
|
418 |
-
if split == "test":
|
419 |
-
# use all available files for the test archive
|
420 |
-
return {"test": audio_paths}
|
421 |
-
|
422 |
-
val_list_file = os.path.join(archive_path, "validation_list.txt")
|
423 |
-
test_list_file = os.path.join(archive_path, "testing_list.txt")
|
424 |
-
with open(val_list_file, encoding="utf-8") as f:
|
425 |
-
val_paths = f.read().strip().splitlines()
|
426 |
-
val_paths = [os.path.join(archive_path, p) for p in val_paths]
|
427 |
-
with open(test_list_file, encoding="utf-8") as f:
|
428 |
-
test_paths = f.read().strip().splitlines()
|
429 |
-
test_paths = [os.path.join(archive_path, p) for p in test_paths]
|
430 |
-
|
431 |
-
# the paths for the train set is just whichever paths that do not exist in
|
432 |
-
# either the test or validation splits
|
433 |
-
train_paths = list(set(audio_paths) - set(val_paths) - set(test_paths))
|
434 |
-
|
435 |
-
return {"train": train_paths, "val": val_paths}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|