Dummy api test.
Browse files- .gitattributes +1 -0
- 1.flac +3 -0
- 2.flac +3 -0
- 3.flac +3 -0
- 4.flac +3 -0
- asr_dummy.py +182 -0
- asr_dummy.py.lock +0 -0
- automatic_speech_recognition_dummy.py +167 -0
- canterville.ogg +3 -0
.gitattributes
CHANGED
@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
35 |
*.mp3 filter=lfs diff=lfs merge=lfs -text
|
36 |
*.ogg filter=lfs diff=lfs merge=lfs -text
|
37 |
*.wav filter=lfs diff=lfs merge=lfs -text
|
|
|
|
35 |
*.mp3 filter=lfs diff=lfs merge=lfs -text
|
36 |
*.ogg filter=lfs diff=lfs merge=lfs -text
|
37 |
*.wav filter=lfs diff=lfs merge=lfs -text
|
38 |
+
canterville.ogg filter=lfs diff=lfs merge=lfs -text
|
1.flac
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:30885601173f96b0d8ddd020dc959b055c6c1582b85a33e3fcab8c4b08ed94c2
|
3 |
+
size 183318
|
2.flac
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3fc09ec6d4cc496c530b2019b17bd8fc8ef8a43d6697090971dd1d52cc3d4d89
|
3 |
+
size 58350
|
3.flac
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:66277a3fa3df407261dc2a3ce685a7ceef19999ab0c10531bee5257cb64cb59d
|
3 |
+
size 116299
|
4.flac
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:17b4a44454b65c0e40417ac0b183a618b0225e90ca3d8610ce688b452ddc7983
|
3 |
+
size 565675
|
asr_dummy.py
ADDED
@@ -0,0 +1,182 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
+
|
20 |
+
import glob
|
21 |
+
import os
|
22 |
+
import textwrap
|
23 |
+
|
24 |
+
import datasets
|
25 |
+
from datasets.tasks import AutomaticSpeechRecognition
|
26 |
+
|
27 |
+
|
28 |
+
_CITATION = """\
|
29 |
+
@article{DBLP:journals/corr/abs-2105-01051,
|
30 |
+
author = {Shu{-}Wen Yang and
|
31 |
+
Po{-}Han Chi and
|
32 |
+
Yung{-}Sung Chuang and
|
33 |
+
Cheng{-}I Jeff Lai and
|
34 |
+
Kushal Lakhotia and
|
35 |
+
Yist Y. Lin and
|
36 |
+
Andy T. Liu and
|
37 |
+
Jiatong Shi and
|
38 |
+
Xuankai Chang and
|
39 |
+
Guan{-}Ting Lin and
|
40 |
+
Tzu{-}Hsien Huang and
|
41 |
+
Wei{-}Cheng Tseng and
|
42 |
+
Ko{-}tik Lee and
|
43 |
+
Da{-}Rong Liu and
|
44 |
+
Zili Huang and
|
45 |
+
Shuyan Dong and
|
46 |
+
Shang{-}Wen Li and
|
47 |
+
Shinji Watanabe and
|
48 |
+
Abdelrahman Mohamed and
|
49 |
+
Hung{-}yi Lee},
|
50 |
+
title = {{SUPERB:} Speech processing Universal PERformance Benchmark},
|
51 |
+
journal = {CoRR},
|
52 |
+
volume = {abs/2105.01051},
|
53 |
+
year = {2021},
|
54 |
+
url = {https://arxiv.org/abs/2105.01051},
|
55 |
+
archivePrefix = {arXiv},
|
56 |
+
eprint = {2105.01051},
|
57 |
+
timestamp = {Thu, 01 Jul 2021 13:30:22 +0200},
|
58 |
+
biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib},
|
59 |
+
bibsource = {dblp computer science bibliography, https://dblp.org}
|
60 |
+
}
|
61 |
+
"""
|
62 |
+
|
63 |
+
_DESCRIPTION = """\
|
64 |
+
Self-supervised learning (SSL) has proven vital for advancing research in
|
65 |
+
natural language processing (NLP) and computer vision (CV). The paradigm
|
66 |
+
pretrains a shared model on large volumes of unlabeled data and achieves
|
67 |
+
state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the
|
68 |
+
speech processing community lacks a similar setup to systematically explore the
|
69 |
+
paradigm. To bridge this gap, we introduce Speech processing Universal
|
70 |
+
PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the
|
71 |
+
performance of a shared model across a wide range of speech processing tasks
|
72 |
+
with minimal architecture changes and labeled data. Among multiple usages of the
|
73 |
+
shared model, we especially focus on extracting the representation learned from
|
74 |
+
SSL due to its preferable re-usability. We present a simple framework to solve
|
75 |
+
SUPERB tasks by learning task-specialized lightweight prediction heads on top of
|
76 |
+
the frozen shared model. Our results demonstrate that the framework is promising
|
77 |
+
as SSL representations show competitive generalizability and accessibility
|
78 |
+
across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a
|
79 |
+
benchmark toolkit to fuel the research in representation learning and general
|
80 |
+
speech processing.
|
81 |
+
|
82 |
+
Note that in order to limit the required storage for preparing this dataset, the
|
83 |
+
audio is stored in the .flac format and is not converted to a float32 array. To
|
84 |
+
convert, the audio file to a float32 array, please make use of the `.map()`
|
85 |
+
function as follows:
|
86 |
+
|
87 |
+
|
88 |
+
```python
|
89 |
+
import soundfile as sf
|
90 |
+
|
91 |
+
def map_to_array(batch):
|
92 |
+
speech_array, _ = sf.read(batch["file"])
|
93 |
+
batch["speech"] = speech_array
|
94 |
+
return batch
|
95 |
+
|
96 |
+
dataset = dataset.map(map_to_array, remove_columns=["file"])
|
97 |
+
```
|
98 |
+
"""
|
99 |
+
|
100 |
+
|
101 |
+
class AsrDummybConfig(datasets.BuilderConfig):
|
102 |
+
"""BuilderConfig for Superb."""
|
103 |
+
|
104 |
+
def __init__(
|
105 |
+
self,
|
106 |
+
data_url,
|
107 |
+
url,
|
108 |
+
task_templates=None,
|
109 |
+
**kwargs,
|
110 |
+
):
|
111 |
+
super(AsrDummybConfig, self).__init__(
|
112 |
+
version=datasets.Version("1.9.0", ""), **kwargs
|
113 |
+
)
|
114 |
+
self.data_url = data_url
|
115 |
+
self.url = url
|
116 |
+
self.task_templates = task_templates
|
117 |
+
|
118 |
+
|
119 |
+
class AsrDummy(datasets.GeneratorBasedBuilder):
|
120 |
+
"""Superb dataset."""
|
121 |
+
|
122 |
+
BUILDER_CONFIGS = [
|
123 |
+
AsrDummybConfig(
|
124 |
+
name="asr",
|
125 |
+
description=textwrap.dedent(
|
126 |
+
"""\
|
127 |
+
ASR transcribes utterances into words. While PR analyzes the
|
128 |
+
improvement in modeling phonetics, ASR reflects the significance of
|
129 |
+
the improvement in a real-world scenario. LibriSpeech
|
130 |
+
train-clean-100/dev-clean/test-clean subsets are used for
|
131 |
+
training/validation/testing. The evaluation metric is word error
|
132 |
+
rate (WER)."""
|
133 |
+
),
|
134 |
+
url="http://www.openslr.org/12",
|
135 |
+
data_url="http://www.openslr.org/resources/12/",
|
136 |
+
task_templates=[
|
137 |
+
AutomaticSpeechRecognition(
|
138 |
+
audio_file_path_column="file", transcription_column="text"
|
139 |
+
)
|
140 |
+
],
|
141 |
+
)
|
142 |
+
]
|
143 |
+
|
144 |
+
DEFAULT_CONFIG_NAME = "asr"
|
145 |
+
|
146 |
+
def _info(self):
|
147 |
+
return datasets.DatasetInfo(
|
148 |
+
description=_DESCRIPTION,
|
149 |
+
features=datasets.Features(
|
150 |
+
{
|
151 |
+
"id": datasets.Value("string"),
|
152 |
+
"file": datasets.Value("string"),
|
153 |
+
}
|
154 |
+
),
|
155 |
+
supervised_keys=("file",),
|
156 |
+
homepage=self.config.url,
|
157 |
+
citation=_CITATION,
|
158 |
+
task_templates=self.config.task_templates,
|
159 |
+
)
|
160 |
+
|
161 |
+
def _split_generators(self, dl_manager):
|
162 |
+
DL_URLS = [
|
163 |
+
f"https://huggingface.co/datasets/Narsil/asr_dummy/raw/main/{i}.flac"
|
164 |
+
for i in range(1, 5)
|
165 |
+
]
|
166 |
+
archive_path = dl_manager.download_and_extract(DL_URLS)
|
167 |
+
return [
|
168 |
+
datasets.SplitGenerator(
|
169 |
+
name=datasets.Split.TEST,
|
170 |
+
gen_kwargs={"archive_path": archive_path},
|
171 |
+
),
|
172 |
+
]
|
173 |
+
|
174 |
+
def _generate_examples(self, archive_path):
|
175 |
+
"""Generate examples."""
|
176 |
+
for i, filename in enumerate(archive_path):
|
177 |
+
key = str(i)
|
178 |
+
example = {
|
179 |
+
"id": key,
|
180 |
+
"file": filename,
|
181 |
+
}
|
182 |
+
yield key, example
|
asr_dummy.py.lock
ADDED
File without changes
|
automatic_speech_recognition_dummy.py
ADDED
@@ -0,0 +1,167 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
+
# Lint as: python3
|
16 |
+
"""SUPERB: Speech processing Universal PERformance Benchmark."""
|
17 |
+
import glob
|
18 |
+
import os
|
19 |
+
import textwrap
|
20 |
+
import datasets
|
21 |
+
from datasets.tasks import AutomaticSpeechRecognition
|
22 |
+
|
23 |
+
_CITATION = """\
|
24 |
+
@article{DBLP:journals/corr/abs-2105-01051,
|
25 |
+
author = {Shu{-}Wen Yang and
|
26 |
+
Po{-}Han Chi and
|
27 |
+
Yung{-}Sung Chuang and
|
28 |
+
Cheng{-}I Jeff Lai and
|
29 |
+
Kushal Lakhotia and
|
30 |
+
Yist Y. Lin and
|
31 |
+
Andy T. Liu and
|
32 |
+
Jiatong Shi and
|
33 |
+
Xuankai Chang and
|
34 |
+
Guan{-}Ting Lin and
|
35 |
+
Tzu{-}Hsien Huang and
|
36 |
+
Wei{-}Cheng Tseng and
|
37 |
+
Ko{-}tik Lee and
|
38 |
+
Da{-}Rong Liu and
|
39 |
+
Zili Huang and
|
40 |
+
Shuyan Dong and
|
41 |
+
Shang{-}Wen Li and
|
42 |
+
Shinji Watanabe and
|
43 |
+
Abdelrahman Mohamed and
|
44 |
+
Hung{-}yi Lee},
|
45 |
+
title = {{SUPERB:} Speech processing Universal PERformance Benchmark},
|
46 |
+
journal = {CoRR},
|
47 |
+
volume = {abs/2105.01051},
|
48 |
+
year = {2021},
|
49 |
+
url = {https://arxiv.org/abs/2105.01051},
|
50 |
+
archivePrefix = {arXiv},
|
51 |
+
eprint = {2105.01051},
|
52 |
+
timestamp = {Thu, 01 Jul 2021 13:30:22 +0200},
|
53 |
+
biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib},
|
54 |
+
bibsource = {dblp computer science bibliography, https://dblp.org}
|
55 |
+
}
|
56 |
+
"""
|
57 |
+
|
58 |
+
_DESCRIPTION = """\
|
59 |
+
Self-supervised learning (SSL) has proven vital for advancing research in
|
60 |
+
natural language processing (NLP) and computer vision (CV). The paradigm
|
61 |
+
pretrains a shared model on large volumes of unlabeled data and achieves
|
62 |
+
state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the
|
63 |
+
speech processing community lacks a similar setup to systematically explore the
|
64 |
+
paradigm. To bridge this gap, we introduce Speech processing Universal
|
65 |
+
PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the
|
66 |
+
performance of a shared model across a wide range of speech processing tasks
|
67 |
+
with minimal architecture changes and labeled data. Among multiple usages of the
|
68 |
+
shared model, we especially focus on extracting the representation learned from
|
69 |
+
SSL due to its preferable re-usability. We present a simple framework to solve
|
70 |
+
SUPERB tasks by learning task-specialized lightweight prediction heads on top of
|
71 |
+
the frozen shared model. Our results demonstrate that the framework is promising
|
72 |
+
as SSL representations show competitive generalizability and accessibility
|
73 |
+
across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a
|
74 |
+
benchmark toolkit to fuel the research in representation learning and general
|
75 |
+
speech processing.
|
76 |
+
Note that in order to limit the required storage for preparing this dataset, the
|
77 |
+
audio is stored in the .flac format and is not converted to a float32 array. To
|
78 |
+
convert, the audio file to a float32 array, please make use of the `.map()`
|
79 |
+
function as follows:
|
80 |
+
```python
|
81 |
+
import soundfile as sf
|
82 |
+
def map_to_array(batch):
|
83 |
+
speech_array, _ = sf.read(batch["file"])
|
84 |
+
batch["speech"] = speech_array
|
85 |
+
return batch
|
86 |
+
dataset = dataset.map(map_to_array, remove_columns=["file"])
|
87 |
+
```
|
88 |
+
"""
|
89 |
+
|
90 |
+
class AsrDummybConfig(datasets.BuilderConfig):
|
91 |
+
"""BuilderConfig for Superb."""
|
92 |
+
def __init__(
|
93 |
+
self,
|
94 |
+
data_url,
|
95 |
+
url,
|
96 |
+
task_templates=None,
|
97 |
+
**kwargs,
|
98 |
+
):
|
99 |
+
super(AsrDummybConfig, self).__init__(
|
100 |
+
version=datasets.Version("1.9.0", ""), **kwargs
|
101 |
+
)
|
102 |
+
self.data_url = data_url
|
103 |
+
self.url = url
|
104 |
+
self.task_templates = task_templates
|
105 |
+
|
106 |
+
class AsrDummy(datasets.GeneratorBasedBuilder):
|
107 |
+
"""Superb dataset."""
|
108 |
+
BUILDER_CONFIGS = [
|
109 |
+
AsrDummybConfig(
|
110 |
+
name="asr",
|
111 |
+
description=textwrap.dedent(
|
112 |
+
"""\
|
113 |
+
ASR transcribes utterances into words. While PR analyzes the
|
114 |
+
improvement in modeling phonetics, ASR reflects the significance of
|
115 |
+
the improvement in a real-world scenario. LibriSpeech
|
116 |
+
train-clean-100/dev-clean/test-clean subsets are used for
|
117 |
+
training/validation/testing. The evaluation metric is word error
|
118 |
+
rate (WER)."""
|
119 |
+
),
|
120 |
+
url="http://www.openslr.org/12",
|
121 |
+
data_url="http://www.openslr.org/resources/12/",
|
122 |
+
task_templates=[
|
123 |
+
AutomaticSpeechRecognition(
|
124 |
+
audio_file_path_column="file", transcription_column="text"
|
125 |
+
)
|
126 |
+
],
|
127 |
+
)
|
128 |
+
]
|
129 |
+
|
130 |
+
DEFAULT_CONFIG_NAME = "asr"
|
131 |
+
def _info(self):
|
132 |
+
return datasets.DatasetInfo(
|
133 |
+
description=_DESCRIPTION,
|
134 |
+
features=datasets.Features(
|
135 |
+
{
|
136 |
+
"id": datasets.Value("string"),
|
137 |
+
"file": datasets.Value("string"),
|
138 |
+
}
|
139 |
+
),
|
140 |
+
supervised_keys=("file",),
|
141 |
+
homepage=self.config.url,
|
142 |
+
citation=_CITATION,
|
143 |
+
task_templates=self.config.task_templates,
|
144 |
+
)
|
145 |
+
|
146 |
+
def _split_generators(self, dl_manager):
|
147 |
+
DL_URLS = [
|
148 |
+
f"https://huggingface.co/datasets/Narsil/automatic_speech_recognition_dummy/raw/main/{i}.flac"
|
149 |
+
for i in range(1, 4)
|
150 |
+
]
|
151 |
+
archive_path = dl_manager.download_and_extract(DL_URLS)
|
152 |
+
return [
|
153 |
+
datasets.SplitGenerator(
|
154 |
+
name=datasets.Split.TEST,
|
155 |
+
gen_kwargs={"archive_path": archive_path},
|
156 |
+
),
|
157 |
+
]
|
158 |
+
|
159 |
+
def _generate_examples(self, archive_path):
|
160 |
+
"""Generate examples."""
|
161 |
+
for i, filename in enumerate(archive_path):
|
162 |
+
key = str(i)
|
163 |
+
example = {
|
164 |
+
"id": key,
|
165 |
+
"file": filename,
|
166 |
+
}
|
167 |
+
yield key, example
|
canterville.ogg
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3a7c94d683543dd4fef0bebe12bcddbd302ffba5367a3280ecd602ffcf481e85
|
3 |
+
size 31419105
|