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715e154
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Parent(s):
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Upload data
Browse files- data/IEMOCAP_full_release.zip +3 -0
- data/LibriSpeech-test-clean.zip +3 -0
- data/VoxCeleb1.zip +3 -0
- data/fluent_speech_commands_dataset.zip +3 -0
- data/speech_commands_test_set_v0.01.zip +3 -0
- superb_demo.py +423 -0
data/IEMOCAP_full_release.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:99da5de585066b100f2a16b8960a350a6620fa2487f1127e969198f7d7f9bcba
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size 1209515
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data/LibriSpeech-test-clean.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:a59633668a54ac2fbd99283afa291be3c3db130a1cf36687e18d8876db9f2df1
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size 626257
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data/VoxCeleb1.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:539e1de8d0158ab7cb7fbb7dd793bab99bada17319038e31bccfbed16c9b2219
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size 1512582
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data/fluent_speech_commands_dataset.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:9d0ef6e970baffb1b6ca6f370c07240bdcd0dd32b1436426fa025019c00d9894
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size 494518
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data/speech_commands_test_set_v0.01.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:026ed5d467c50a07dec3ada87a9d833ba9d21cd7367721d3dcb08a28482d4c06
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size 211385
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superb_demo.py
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@@ -0,0 +1,423 @@
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+
# coding=utf-8
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# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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#
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4 |
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# Licensed under the Apache License, Version 2.0 (the "License");
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5 |
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# 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 |
+
"text": datasets.Value("string"),
|
139 |
+
"speaker_id": datasets.Value("int64"),
|
140 |
+
"chapter_id": datasets.Value("int64"),
|
141 |
+
"id": datasets.Value("string"),
|
142 |
+
}
|
143 |
+
),
|
144 |
+
supervised_keys=("file", "text"),
|
145 |
+
url="http://www.openslr.org/12",
|
146 |
+
data_url="data/LibriSpeech-test-clean.zip",
|
147 |
+
task_templates=[AutomaticSpeechRecognition(audio_file_path_column="file", transcription_column="text")],
|
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 |
+
"label": datasets.ClassLabel(
|
163 |
+
names=[
|
164 |
+
"yes",
|
165 |
+
"no",
|
166 |
+
"up",
|
167 |
+
"down",
|
168 |
+
"left",
|
169 |
+
"right",
|
170 |
+
"on",
|
171 |
+
"off",
|
172 |
+
"stop",
|
173 |
+
"go",
|
174 |
+
"_silence_",
|
175 |
+
"_unknown_",
|
176 |
+
]
|
177 |
+
),
|
178 |
+
}
|
179 |
+
),
|
180 |
+
supervised_keys=("file", "label"),
|
181 |
+
url="https://www.tensorflow.org/datasets/catalog/speech_commands",
|
182 |
+
data_url="data/speech_commands_test_set_v0.01.zip",
|
183 |
+
),
|
184 |
+
SuperbConfig(
|
185 |
+
name="ic",
|
186 |
+
description=textwrap.dedent(
|
187 |
+
"""\
|
188 |
+
Intent Classification (IC) classifies utterances into predefined classes to determine the intent of
|
189 |
+
speakers. SUPERB uses the Fluent Speech Commands dataset, where each utterance is tagged with three intent
|
190 |
+
labels: action, object, and location. The evaluation metric is accuracy (ACC)."""
|
191 |
+
),
|
192 |
+
features=datasets.Features(
|
193 |
+
{
|
194 |
+
"file": datasets.Value("string"),
|
195 |
+
"speaker_id": datasets.Value("string"),
|
196 |
+
"text": datasets.Value("string"),
|
197 |
+
"action": datasets.ClassLabel(
|
198 |
+
names=["activate", "bring", "change language", "deactivate", "decrease", "increase"]
|
199 |
+
),
|
200 |
+
"object": datasets.ClassLabel(
|
201 |
+
names=[
|
202 |
+
"Chinese",
|
203 |
+
"English",
|
204 |
+
"German",
|
205 |
+
"Korean",
|
206 |
+
"heat",
|
207 |
+
"juice",
|
208 |
+
"lamp",
|
209 |
+
"lights",
|
210 |
+
"music",
|
211 |
+
"newspaper",
|
212 |
+
"none",
|
213 |
+
"shoes",
|
214 |
+
"socks",
|
215 |
+
"volume",
|
216 |
+
]
|
217 |
+
),
|
218 |
+
"location": datasets.ClassLabel(names=["bedroom", "kitchen", "none", "washroom"]),
|
219 |
+
}
|
220 |
+
),
|
221 |
+
# no default supervised keys, since there are 3 labels
|
222 |
+
supervised_keys=None,
|
223 |
+
url="https://fluent.ai/fluent-speech-commands-a-dataset-for-spoken-language-understanding-research/",
|
224 |
+
data_url="data/fluent_speech_commands_dataset.zip",
|
225 |
+
),
|
226 |
+
SuperbConfig(
|
227 |
+
name="si",
|
228 |
+
description=textwrap.dedent(
|
229 |
+
"""\
|
230 |
+
Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class
|
231 |
+
classification, where speakers are in the same predefined set for both training and testing. The widely
|
232 |
+
used VoxCeleb1 dataset is adopted, and the evaluation metric is accuracy (ACC)."""
|
233 |
+
),
|
234 |
+
features=datasets.Features(
|
235 |
+
{
|
236 |
+
"file": datasets.Value("string"),
|
237 |
+
"label": datasets.ClassLabel(names=[f"id{i + 10001}" for i in range(1251)]),
|
238 |
+
}
|
239 |
+
),
|
240 |
+
supervised_keys=("file", "label"),
|
241 |
+
url="https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html",
|
242 |
+
data_url="data/VoxCeleb1.zip"
|
243 |
+
),
|
244 |
+
SuperbConfig(
|
245 |
+
name="er",
|
246 |
+
description=textwrap.dedent(
|
247 |
+
"""\
|
248 |
+
Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset
|
249 |
+
IEMOCAP is adopted, and we follow the conventional evaluation protocol: we drop the unbalance emotion
|
250 |
+
classes to leave the final four classes with a similar amount of data points and cross-validates on five
|
251 |
+
folds of the standard splits. The evaluation metric is accuracy (ACC)."""
|
252 |
+
),
|
253 |
+
features=datasets.Features(
|
254 |
+
{
|
255 |
+
"file": datasets.Value("string"),
|
256 |
+
"label": datasets.ClassLabel(names=['neu', 'hap', 'ang', 'sad']),
|
257 |
+
}
|
258 |
+
),
|
259 |
+
supervised_keys=("file", "label"),
|
260 |
+
url="https://sail.usc.edu/iemocap/",
|
261 |
+
data_url="data/IEMOCAP_full_release.zip"
|
262 |
+
),
|
263 |
+
]
|
264 |
+
|
265 |
+
def _info(self):
|
266 |
+
return datasets.DatasetInfo(
|
267 |
+
description=_DESCRIPTION,
|
268 |
+
features=self.config.features,
|
269 |
+
supervised_keys=self.config.supervised_keys,
|
270 |
+
homepage=self.config.url,
|
271 |
+
citation=_CITATION,
|
272 |
+
task_templates=self.config.task_templates,
|
273 |
+
)
|
274 |
+
|
275 |
+
def _split_generators(self, dl_manager):
|
276 |
+
if self.config.name == "asr":
|
277 |
+
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
278 |
+
return [
|
279 |
+
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path}),
|
280 |
+
]
|
281 |
+
elif self.config.name == "ks":
|
282 |
+
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
283 |
+
return [
|
284 |
+
datasets.SplitGenerator(
|
285 |
+
name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"}
|
286 |
+
),
|
287 |
+
]
|
288 |
+
elif self.config.name == "ic":
|
289 |
+
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
290 |
+
return [
|
291 |
+
datasets.SplitGenerator(
|
292 |
+
name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"}
|
293 |
+
),
|
294 |
+
]
|
295 |
+
elif self.config.name == "si":
|
296 |
+
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
297 |
+
return [
|
298 |
+
datasets.SplitGenerator(
|
299 |
+
name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": 3}
|
300 |
+
),
|
301 |
+
]
|
302 |
+
elif self.config.name == "sd":
|
303 |
+
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
304 |
+
return [
|
305 |
+
datasets.SplitGenerator(
|
306 |
+
name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"}
|
307 |
+
)
|
308 |
+
]
|
309 |
+
elif self.config.name == "er":
|
310 |
+
archive_path = dl_manager.download_and_extract(self.config.data_url)
|
311 |
+
return [
|
312 |
+
datasets.SplitGenerator(
|
313 |
+
name="session1", gen_kwargs={"archive_path": archive_path, "split": 1},
|
314 |
+
)
|
315 |
+
]
|
316 |
+
|
317 |
+
def _generate_examples(self, archive_path, split=None):
|
318 |
+
"""Generate examples."""
|
319 |
+
if self.config.name == "asr":
|
320 |
+
transcripts_glob = os.path.join(archive_path, "LibriSpeech", "*/*/*/*.txt")
|
321 |
+
key = 0
|
322 |
+
for transcript_path in sorted(glob.glob(transcripts_glob)):
|
323 |
+
transcript_dir_path = os.path.dirname(transcript_path)
|
324 |
+
with open(transcript_path, "r", encoding="utf-8") as f:
|
325 |
+
for line in f:
|
326 |
+
line = line.strip()
|
327 |
+
id_, transcript = line.split(" ", 1)
|
328 |
+
audio_file = f"{id_}.flac"
|
329 |
+
speaker_id, chapter_id = [int(el) for el in id_.split("-")[:2]]
|
330 |
+
yield key, {
|
331 |
+
"id": id_,
|
332 |
+
"speaker_id": speaker_id,
|
333 |
+
"chapter_id": chapter_id,
|
334 |
+
"file": os.path.join(transcript_dir_path, audio_file),
|
335 |
+
"text": transcript,
|
336 |
+
}
|
337 |
+
key += 1
|
338 |
+
elif self.config.name == "ks":
|
339 |
+
words = ["yes", "no", "up", "down", "left", "right", "on", "off", "stop", "go"]
|
340 |
+
splits = _split_ks_files(archive_path, split)
|
341 |
+
for key, audio_file in enumerate(sorted(splits[split])):
|
342 |
+
base_dir, file_name = os.path.split(audio_file)
|
343 |
+
_, word = os.path.split(base_dir)
|
344 |
+
if word in words:
|
345 |
+
label = word
|
346 |
+
elif word == "_silence_" or word == "_background_noise_":
|
347 |
+
label = "_silence_"
|
348 |
+
else:
|
349 |
+
label = "_unknown_"
|
350 |
+
yield key, {"file": audio_file, "label": label}
|
351 |
+
elif self.config.name == "ic":
|
352 |
+
root_path = os.path.join(archive_path, "fluent_speech_commands_dataset/")
|
353 |
+
csv_path = os.path.join(root_path, f"data/{split}_data.csv")
|
354 |
+
with open(csv_path, encoding="utf-8") as csv_file:
|
355 |
+
csv_reader = csv.reader(csv_file, delimiter=",", skipinitialspace=True)
|
356 |
+
next(csv_reader)
|
357 |
+
for row in csv_reader:
|
358 |
+
key, file_path, speaker_id, text, action, object_, location = row
|
359 |
+
yield key, {
|
360 |
+
"file": os.path.join(root_path, file_path),
|
361 |
+
"speaker_id": speaker_id,
|
362 |
+
"text": text,
|
363 |
+
"action": action,
|
364 |
+
"object": object_,
|
365 |
+
"location": location,
|
366 |
+
}
|
367 |
+
elif self.config.name == "si":
|
368 |
+
wav_path = os.path.join(archive_path, "wav/")
|
369 |
+
splits_path = os.path.join(archive_path, "veri_test_class.txt")
|
370 |
+
with open(splits_path, "r", encoding="utf-8") as f:
|
371 |
+
for key, line in enumerate(f):
|
372 |
+
split_id, file_path = line.strip().split(" ")
|
373 |
+
if int(split_id) != split:
|
374 |
+
continue
|
375 |
+
speaker_id = file_path.split("/")[0]
|
376 |
+
yield key, {
|
377 |
+
"file": os.path.join(wav_path, file_path),
|
378 |
+
"label": speaker_id,
|
379 |
+
}
|
380 |
+
elif self.config.name == "er":
|
381 |
+
root_path = os.path.join(archive_path, f"Session{split}/")
|
382 |
+
wav_path = os.path.join(root_path, "sentences/wav/")
|
383 |
+
labels_path = os.path.join(root_path, "dialog/EmoEvaluation/*.txt")
|
384 |
+
emotions = ['neu', 'hap', 'ang', 'sad', 'exc']
|
385 |
+
key = 0
|
386 |
+
for labels_file in sorted(glob.glob(labels_path)):
|
387 |
+
with open(labels_file, "r", encoding="utf-8") as f:
|
388 |
+
for line in f:
|
389 |
+
if line[0] != "[":
|
390 |
+
continue
|
391 |
+
_, filename, emo, _ = line.split("\t")
|
392 |
+
if emo not in emotions:
|
393 |
+
continue
|
394 |
+
wav_subdir = filename.rsplit("_", 1)[0]
|
395 |
+
filename = f"{filename}.wav"
|
396 |
+
yield key, {
|
397 |
+
"file": os.path.join(wav_path, wav_subdir, filename),
|
398 |
+
"label": emo.replace('exc', 'hap'),
|
399 |
+
}
|
400 |
+
key += 1
|
401 |
+
|
402 |
+
|
403 |
+
def _split_ks_files(archive_path, split):
|
404 |
+
audio_path = os.path.join(archive_path, "**/*.wav")
|
405 |
+
audio_paths = glob.glob(audio_path)
|
406 |
+
if split == "test":
|
407 |
+
# use all available files for the test archive
|
408 |
+
return {"test": audio_paths}
|
409 |
+
|
410 |
+
val_list_file = os.path.join(archive_path, "validation_list.txt")
|
411 |
+
test_list_file = os.path.join(archive_path, "testing_list.txt")
|
412 |
+
with open(val_list_file, encoding="utf-8") as f:
|
413 |
+
val_paths = f.read().strip().splitlines()
|
414 |
+
val_paths = [os.path.join(archive_path, p) for p in val_paths]
|
415 |
+
with open(test_list_file, encoding="utf-8") as f:
|
416 |
+
test_paths = f.read().strip().splitlines()
|
417 |
+
test_paths = [os.path.join(archive_path, p) for p in test_paths]
|
418 |
+
|
419 |
+
# the paths for the train set is just whichever paths that do not exist in
|
420 |
+
# either the test or validation splits
|
421 |
+
train_paths = list(set(audio_paths) - set(val_paths) - set(test_paths))
|
422 |
+
|
423 |
+
return {"train": train_paths, "val": val_paths}
|