mls_without_script
#15
by
ylacombe
- opened
This view is limited to 50 files because it contains too many changes.
See the raw diff here.
- README.md +416 -14
- create_dataset.py +106 -0
- dutch/1_hours-00000-of-00001.parquet +3 -0
- dutch/9_hours-00000-of-00001.parquet +3 -0
- dutch/dev-00000-of-00001.parquet +3 -0
- dutch/test-00000-of-00001.parquet +3 -0
- dutch/train-00000-of-00048.parquet +3 -0
- dutch/train-00001-of-00048.parquet +3 -0
- dutch/train-00002-of-00048.parquet +3 -0
- dutch/train-00003-of-00048.parquet +3 -0
- dutch/train-00004-of-00048.parquet +3 -0
- dutch/train-00005-of-00048.parquet +3 -0
- dutch/train-00006-of-00048.parquet +3 -0
- dutch/train-00007-of-00048.parquet +3 -0
- dutch/train-00008-of-00048.parquet +3 -0
- dutch/train-00009-of-00048.parquet +3 -0
- dutch/train-00010-of-00048.parquet +3 -0
- dutch/train-00011-of-00048.parquet +3 -0
- dutch/train-00012-of-00048.parquet +3 -0
- dutch/train-00013-of-00048.parquet +3 -0
- dutch/train-00014-of-00048.parquet +3 -0
- dutch/train-00015-of-00048.parquet +3 -0
- dutch/train-00016-of-00048.parquet +3 -0
- dutch/train-00017-of-00048.parquet +3 -0
- dutch/train-00018-of-00048.parquet +3 -0
- dutch/train-00019-of-00048.parquet +3 -0
- dutch/train-00020-of-00048.parquet +3 -0
- dutch/train-00021-of-00048.parquet +3 -0
- dutch/train-00022-of-00048.parquet +3 -0
- dutch/train-00023-of-00048.parquet +3 -0
- dutch/train-00024-of-00048.parquet +3 -0
- dutch/train-00025-of-00048.parquet +3 -0
- dutch/train-00026-of-00048.parquet +3 -0
- dutch/train-00027-of-00048.parquet +3 -0
- dutch/train-00028-of-00048.parquet +3 -0
- dutch/train-00029-of-00048.parquet +3 -0
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- dutch/train-00042-of-00048.parquet +3 -0
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README.md
CHANGED
@@ -1,5 +1,4 @@
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---
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-
pretty_name: MultiLingual LibriSpeech
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annotations_creators:
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- expert-generated
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language_creators:
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- es
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- pt
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- pl
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license:
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- cc-by-4.0
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multilinguality:
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- multilingual
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-
paperswithcode_id: multilingual-librispeech
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size_categories:
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- 100K<n<1M
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source_datasets:
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- original
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task_categories:
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- automatic-speech-recognition
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---
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# Dataset Card for MultiLingual LibriSpeech
|
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The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream.
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MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of
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8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.
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### Supported Tasks and Leaderboards
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- `automatic-speech-recognition`, `speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER.
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### Languages
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|
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For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German):
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```python
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from datasets import load_dataset
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-
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mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
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```
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Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
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```python
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from datasets import load_dataset
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-
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mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True)
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-
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print(next(iter(mls)))
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```
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```python
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from datasets import load_dataset
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from torch.utils.data.sampler import BatchSampler, RandomSampler
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-
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mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
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batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False)
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dataloader = DataLoader(mls, batch_sampler=batch_sampler)
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```python
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from datasets import load_dataset
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from torch.utils.data import DataLoader
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-
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mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True)
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dataloader = DataLoader(mls, batch_size=32)
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```
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- id: unique id of the data sample.
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- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
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-
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- chapter_id: id of the audiobook chapter which includes the transcription.
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### Data Splits
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-
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| ----- | ------ | ----- | ---- | ---- | ---- |
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| german | 469942 | 2194 | 241 | 3469 | 3394 |
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| dutch | 374287 | 2153 | 234 | 3095 | 3075 |
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| portuguese | 37533 | 2116 | 236 | 826 | 871 |
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| polish | 25043 | 2173 | 238 | 512 | 520 |
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-
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-
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## Dataset Creation
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### Curation Rationale
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}
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```
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### Contributions
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-
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten)
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-
and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
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1 |
---
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annotations_creators:
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- expert-generated
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language_creators:
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- es
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- pt
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- pl
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+
- en
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license:
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- cc-by-4.0
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multilinguality:
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- multilingual
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size_categories:
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- 100K<n<1M
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source_datasets:
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- original
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task_categories:
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- automatic-speech-recognition
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- text-to-speech
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- text-to-audio
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paperswithcode_id: multilingual-librispeech
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pretty_name: MultiLingual LibriSpeech
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dataset_info:
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- config_name: dutch
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features:
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- name: audio
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dtype: audio
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- name: original_path
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dtype: string
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- name: begin_time
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dtype: float64
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- name: end_time
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dtype: float64
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- name: transcript
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dtype: string
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- name: audio_duration
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dtype: float64
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- name: speaker_id
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dtype: string
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- name: chapter_id
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dtype: string
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- name: file
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dtype: string
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- name: id
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dtype: string
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splits:
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+
download_size: 24376256629
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+
dataset_size: 24486284437.668
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- config_name: french
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features:
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- name: audio
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dtype: audio
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- name: original_path
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features:
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dtype: audio
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dtype: string
|
163 |
+
- name: audio_duration
|
164 |
+
dtype: float64
|
165 |
+
- name: speaker_id
|
166 |
+
dtype: string
|
167 |
+
- name: chapter_id
|
168 |
+
dtype: string
|
169 |
+
- name: file
|
170 |
+
dtype: string
|
171 |
+
- name: id
|
172 |
+
dtype: string
|
173 |
+
splits:
|
174 |
+
- name: dev
|
175 |
+
num_bytes: 81607596.048
|
176 |
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num_examples: 1248
|
177 |
+
- name: test
|
178 |
+
num_bytes: 83216752.046
|
179 |
+
num_examples: 1262
|
180 |
+
- name: train
|
181 |
+
num_bytes: 3896742625
|
182 |
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num_examples: 59623
|
183 |
+
- name: 9_hours
|
184 |
+
num_bytes: 141671904.428
|
185 |
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num_examples: 2173
|
186 |
+
- name: 1_hours
|
187 |
+
num_bytes: 15560398
|
188 |
+
num_examples: 240
|
189 |
+
download_size: 4200633596
|
190 |
+
dataset_size: 4218799275.522
|
191 |
+
- config_name: polish
|
192 |
+
features:
|
193 |
+
- name: audio
|
194 |
+
dtype: audio
|
195 |
+
- name: original_path
|
196 |
+
dtype: string
|
197 |
+
- name: begin_time
|
198 |
+
dtype: float64
|
199 |
+
- name: end_time
|
200 |
+
dtype: float64
|
201 |
+
- name: transcript
|
202 |
+
dtype: string
|
203 |
+
- name: audio_duration
|
204 |
+
dtype: float64
|
205 |
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- name: speaker_id
|
206 |
+
dtype: string
|
207 |
+
- name: chapter_id
|
208 |
+
dtype: string
|
209 |
+
- name: file
|
210 |
+
dtype: string
|
211 |
+
- name: id
|
212 |
+
dtype: string
|
213 |
+
splits:
|
214 |
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- name: dev
|
215 |
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num_bytes: 32746725
|
216 |
+
num_examples: 512
|
217 |
+
- name: test
|
218 |
+
num_bytes: 33735044
|
219 |
+
num_examples: 520
|
220 |
+
- name: train
|
221 |
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num_bytes: 1638889846
|
222 |
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num_examples: 25043
|
223 |
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- name: 9_hours
|
224 |
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num_bytes: 142005461
|
225 |
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num_examples: 2173
|
226 |
+
- name: 1_hours
|
227 |
+
num_bytes: 15681216
|
228 |
+
num_examples: 238
|
229 |
+
download_size: 1855342312
|
230 |
+
dataset_size: 1863058292
|
231 |
+
- config_name: portuguese
|
232 |
+
features:
|
233 |
+
- name: audio
|
234 |
+
dtype: audio
|
235 |
+
- name: original_path
|
236 |
+
dtype: string
|
237 |
+
- name: begin_time
|
238 |
+
dtype: float64
|
239 |
+
- name: end_time
|
240 |
+
dtype: float64
|
241 |
+
- name: transcript
|
242 |
+
dtype: string
|
243 |
+
- name: audio_duration
|
244 |
+
dtype: float64
|
245 |
+
- name: speaker_id
|
246 |
+
dtype: string
|
247 |
+
- name: chapter_id
|
248 |
+
dtype: string
|
249 |
+
- name: file
|
250 |
+
dtype: string
|
251 |
+
- name: id
|
252 |
+
dtype: string
|
253 |
+
splits:
|
254 |
+
- name: dev
|
255 |
+
num_bytes: 57533473
|
256 |
+
num_examples: 826
|
257 |
+
- name: test
|
258 |
+
num_bytes: 59141979
|
259 |
+
num_examples: 871
|
260 |
+
- name: train
|
261 |
+
num_bytes: 2518553713.946
|
262 |
+
num_examples: 37533
|
263 |
+
- name: 9_hours
|
264 |
+
num_bytes: 141641902.42
|
265 |
+
num_examples: 2116
|
266 |
+
- name: 1_hours
|
267 |
+
num_bytes: 15697139
|
268 |
+
num_examples: 236
|
269 |
+
download_size: 2780836500
|
270 |
+
dataset_size: 2792568207.366
|
271 |
+
- config_name: spanish
|
272 |
+
features:
|
273 |
+
- name: audio
|
274 |
+
dtype: audio
|
275 |
+
- name: original_path
|
276 |
+
dtype: string
|
277 |
+
- name: begin_time
|
278 |
+
dtype: float64
|
279 |
+
- name: end_time
|
280 |
+
dtype: float64
|
281 |
+
- name: transcript
|
282 |
+
dtype: string
|
283 |
+
- name: audio_duration
|
284 |
+
dtype: float64
|
285 |
+
- name: speaker_id
|
286 |
+
dtype: string
|
287 |
+
- name: chapter_id
|
288 |
+
dtype: string
|
289 |
+
- name: file
|
290 |
+
dtype: string
|
291 |
+
- name: id
|
292 |
+
dtype: string
|
293 |
+
splits:
|
294 |
+
- name: dev
|
295 |
+
num_bytes: 157804903.144
|
296 |
+
num_examples: 2408
|
297 |
+
- name: test
|
298 |
+
num_bytes: 158526899.32
|
299 |
+
num_examples: 2385
|
300 |
+
- name: train
|
301 |
+
num_bytes: 14562584188
|
302 |
+
num_examples: 220701
|
303 |
+
- name: 9_hours
|
304 |
+
num_bytes: 142473624.48
|
305 |
+
num_examples: 2110
|
306 |
+
- name: 1_hours
|
307 |
+
num_bytes: 15702048
|
308 |
+
num_examples: 233
|
309 |
+
download_size: 14971394533
|
310 |
+
dataset_size: 15037091662.944
|
311 |
+
configs:
|
312 |
+
- config_name: dutch
|
313 |
+
data_files:
|
314 |
+
- split: dev
|
315 |
+
path: dutch/dev-*
|
316 |
+
- split: test
|
317 |
+
path: dutch/test-*
|
318 |
+
- split: train
|
319 |
+
path: dutch/train-*
|
320 |
+
- split: 9_hours
|
321 |
+
path: dutch/9_hours-*
|
322 |
+
- split: 1_hours
|
323 |
+
path: dutch/1_hours-*
|
324 |
+
- config_name: french
|
325 |
+
data_files:
|
326 |
+
- split: dev
|
327 |
+
path: french/dev-*
|
328 |
+
- split: test
|
329 |
+
path: french/test-*
|
330 |
+
- split: train
|
331 |
+
path: french/train-*
|
332 |
+
- split: 9_hours
|
333 |
+
path: french/9_hours-*
|
334 |
+
- split: 1_hours
|
335 |
+
path: french/1_hours-*
|
336 |
+
- config_name: german
|
337 |
+
data_files:
|
338 |
+
- split: dev
|
339 |
+
path: german/dev-*
|
340 |
+
- split: test
|
341 |
+
path: german/test-*
|
342 |
+
- split: train
|
343 |
+
path: german/train-*
|
344 |
+
- split: 9_hours
|
345 |
+
path: german/9_hours-*
|
346 |
+
- split: 1_hours
|
347 |
+
path: german/1_hours-*
|
348 |
+
- config_name: italian
|
349 |
+
data_files:
|
350 |
+
- split: dev
|
351 |
+
path: italian/dev-*
|
352 |
+
- split: test
|
353 |
+
path: italian/test-*
|
354 |
+
- split: train
|
355 |
+
path: italian/train-*
|
356 |
+
- split: 9_hours
|
357 |
+
path: italian/9_hours-*
|
358 |
+
- split: 1_hours
|
359 |
+
path: italian/1_hours-*
|
360 |
+
- config_name: polish
|
361 |
+
data_files:
|
362 |
+
- split: dev
|
363 |
+
path: polish/dev-*
|
364 |
+
- split: test
|
365 |
+
path: polish/test-*
|
366 |
+
- split: train
|
367 |
+
path: polish/train-*
|
368 |
+
- split: 9_hours
|
369 |
+
path: polish/9_hours-*
|
370 |
+
- split: 1_hours
|
371 |
+
path: polish/1_hours-*
|
372 |
+
- config_name: portuguese
|
373 |
+
data_files:
|
374 |
+
- split: dev
|
375 |
+
path: portuguese/dev-*
|
376 |
+
- split: test
|
377 |
+
path: portuguese/test-*
|
378 |
+
- split: train
|
379 |
+
path: portuguese/train-*
|
380 |
+
- split: 9_hours
|
381 |
+
path: portuguese/9_hours-*
|
382 |
+
- split: 1_hours
|
383 |
+
path: portuguese/1_hours-*
|
384 |
+
- config_name: spanish
|
385 |
+
data_files:
|
386 |
+
- split: dev
|
387 |
+
path: spanish/dev-*
|
388 |
+
- split: test
|
389 |
+
path: spanish/test-*
|
390 |
+
- split: train
|
391 |
+
path: spanish/train-*
|
392 |
+
- split: 9_hours
|
393 |
+
path: spanish/9_hours-*
|
394 |
+
- split: 1_hours
|
395 |
+
path: spanish/1_hours-*
|
396 |
---
|
397 |
|
398 |
# Dataset Card for MultiLingual LibriSpeech
|
|
|
435 |
The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream.
|
436 |
|
437 |
MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of
|
438 |
+
8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish. It includes about 44.5K hours of English and a total of about 6K hours for other languages.
|
439 |
|
440 |
### Supported Tasks and Leaderboards
|
441 |
|
442 |
- `automatic-speech-recognition`, `speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER.
|
443 |
+
- `text-to-speech`, `text-to-audio`: The dataset can also be used to train a model for Text-To-Speech (TTS).
|
444 |
|
445 |
### Languages
|
446 |
|
|
|
453 |
For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German):
|
454 |
```python
|
455 |
from datasets import load_dataset
|
|
|
456 |
mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
|
457 |
```
|
458 |
|
459 |
Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
|
460 |
```python
|
461 |
from datasets import load_dataset
|
|
|
462 |
mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True)
|
|
|
463 |
print(next(iter(mls)))
|
464 |
```
|
465 |
|
|
|
470 |
```python
|
471 |
from datasets import load_dataset
|
472 |
from torch.utils.data.sampler import BatchSampler, RandomSampler
|
|
|
473 |
mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
|
474 |
batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False)
|
475 |
dataloader = DataLoader(mls, batch_sampler=batch_sampler)
|
|
|
480 |
```python
|
481 |
from datasets import load_dataset
|
482 |
from torch.utils.data import DataLoader
|
|
|
483 |
mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True)
|
484 |
dataloader = DataLoader(mls, batch_size=32)
|
485 |
```
|
|
|
520 |
- id: unique id of the data sample.
|
521 |
|
522 |
- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
|
|
|
523 |
- chapter_id: id of the audiobook chapter which includes the transcription.
|
524 |
|
525 |
### Data Splits
|
526 |
|
527 |
+
| Number of samples | Train | Train.9h | Train.1h | Dev | Test |
|
528 |
| ----- | ------ | ----- | ---- | ---- | ---- |
|
529 |
| german | 469942 | 2194 | 241 | 3469 | 3394 |
|
530 |
| dutch | 374287 | 2153 | 234 | 3095 | 3075 |
|
|
|
534 |
| portuguese | 37533 | 2116 | 236 | 826 | 871 |
|
535 |
| polish | 25043 | 2173 | 238 | 512 | 520 |
|
536 |
|
|
|
|
|
537 |
## Dataset Creation
|
538 |
|
539 |
### Curation Rationale
|
|
|
600 |
}
|
601 |
```
|
602 |
|
603 |
+
|
604 |
+
### Data Statistics
|
605 |
+
|
606 |
+
| Duration (h) | Train | Dev | Test |
|
607 |
+
|--------------|-----------|-------|-------|
|
608 |
+
| English | 44,659.74 | 15.75 | 15.55 |
|
609 |
+
| German | 1,966.51 | 14.28 | 14.29 |
|
610 |
+
| Dutch | 1,554.24 | 12.76 | 12.76 |
|
611 |
+
| French | 1,076.58 | 10.07 | 10.07 |
|
612 |
+
| Spanish | 917.68 | 9.99 | 10 |
|
613 |
+
| Italian | 247.38 | 5.18 | 5.27 |
|
614 |
+
| Portuguese | 160.96 | 3.64 | 3.74 |
|
615 |
+
| Polish | 103.65 | 2.08 | 2.14 |
|
616 |
+
|
617 |
+
| # Speakers | Train | | Dev | | Test | |
|
618 |
+
|------------|-------|------|-----|----|------|----|
|
619 |
+
| Gender | M | F | M | F | M | F |
|
620 |
+
| English | 2742 | 2748 | 21 | 21 | 21 | 21 |
|
621 |
+
| German | 81 | 95 | 15 | 15 | 15 | 15 |
|
622 |
+
| Dutch | 9 | 31 | 3 | 3 | 3 | 3 |
|
623 |
+
| French | 62 | 80 | 9 | 9 | 9 | 9 |
|
624 |
+
| Spanish | 36 | 50 | 10 | 10 | 10 | 10 |
|
625 |
+
| Italian | 22 | 43 | 5 | 5 | 5 | 5 |
|
626 |
+
| Portuguese | 26 | 16 | 5 | 5 | 5 | 5 |
|
627 |
+
| Polish | 6 | 5 | 2 | 2 | 2 | 2 |
|
628 |
+
|
629 |
+
| # Hours / Gender | Dev | | Test | |
|
630 |
+
|------------------|------|------|------|------|
|
631 |
+
| Gender | M | F | M | F |
|
632 |
+
| English | 7.76 | 7.99 | 7.62 | 7.93 |
|
633 |
+
| German | 7.06 | 7.22 | 7 | 7.29 |
|
634 |
+
| Dutch | 6.44 | 6.32 | 6.72 | 6.04 |
|
635 |
+
| French | 5.13 | 4.94 | 5.04 | 5.02 |
|
636 |
+
| Spanish | 4.91 | 5.08 | 4.78 | 5.23 |
|
637 |
+
| Italian | 2.5 | 2.68 | 2.38 | 2.9 |
|
638 |
+
| Portuguese | 1.84 | 1.81 | 1.83 | 1.9 |
|
639 |
+
| Polish | 1.12 | 0.95 | 1.09 | 1.05 |
|
640 |
+
|
641 |
+
|
642 |
+
|
643 |
+
|
644 |
### Contributions
|
645 |
|
646 |
+
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
|
|
create_dataset.py
ADDED
@@ -0,0 +1,106 @@
|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
from datasets import DatasetDict, Audio
|
3 |
+
import pandas as pd
|
4 |
+
from datasets.table import embed_table_storage
|
5 |
+
import argparse
|
6 |
+
|
7 |
+
|
8 |
+
if __name__ == "__main__":
|
9 |
+
parser = argparse.ArgumentParser()
|
10 |
+
|
11 |
+
|
12 |
+
parser.add_argument("main_folder_path", type=str, help="Path of the base mls folder")
|
13 |
+
parser.add_argument("configuration", type=str, help="Dataset configuration to use, if necessary. Here corresponds to the language name.")
|
14 |
+
parser.add_argument("output_dir", type=str, help="Save the dataset on disk with this path.")
|
15 |
+
|
16 |
+
parser.add_argument("--cpu_num_workers", default=1, type=int, help="Number of CPU workers.")
|
17 |
+
parser.add_argument("--csv_folder_path", default=None, type=str, help="Path where to save intermediate csv, by default will be main_foldr_path")
|
18 |
+
parser.add_argument("--repo_id", default="facebook/multilingual_librispeech", type=str, help="Push the dataset to the hub.")
|
19 |
+
|
20 |
+
|
21 |
+
args = parser.parse_args()
|
22 |
+
|
23 |
+
main_folder_path = args.main_folder_path
|
24 |
+
csv_folder_path = args.csv_folder_path if args.csv_folder_path is not None else main_folder_path
|
25 |
+
if not os.path.exists(csv_folder_path):
|
26 |
+
os.makedirs(csv_folder_path)
|
27 |
+
|
28 |
+
splits = ["dev", "test", "train"]
|
29 |
+
|
30 |
+
# total_length_per_split = 10_000 * 60 * 60 # in sec -> 10k hours
|
31 |
+
|
32 |
+
csv_dict = {}
|
33 |
+
for split in splits:
|
34 |
+
segment_path = os.path.join(main_folder_path, split, "segments.txt")
|
35 |
+
transcript_path = os.path.join(main_folder_path, split, "transcripts.txt")
|
36 |
+
|
37 |
+
segments = pd.read_csv(segment_path, sep='\t', names=["audio", "original_path", "begin_time", "end_time"],
|
38 |
+
index_col="audio")
|
39 |
+
transcripts = pd.read_csv(transcript_path, sep='\t', names=["audio", "transcript"], index_col="audio")
|
40 |
+
|
41 |
+
df = pd.concat([segments, transcripts], axis=1, join="inner")
|
42 |
+
print(
|
43 |
+
f"Segments and transcripts of {split} has been joined: new length {len(df)}, old lengths {(len(segments), len(transcripts))}")
|
44 |
+
|
45 |
+
# add audio duration
|
46 |
+
df["audio_duration"] = df["end_time"] - df["begin_time"]
|
47 |
+
df["split"] = split
|
48 |
+
|
49 |
+
print(f"len df {len(df)}")
|
50 |
+
|
51 |
+
df.to_csv(os.path.join(csv_folder_path, f"{split}.csv"))
|
52 |
+
csv_dict[split] = os.path.join(csv_folder_path, f"{split}.csv")
|
53 |
+
|
54 |
+
# take care of /limited_supervision
|
55 |
+
if split == "train":
|
56 |
+
nine_hours_segment_path = os.path.join(main_folder_path, "train/limited_supervision/9hr/handles.txt")
|
57 |
+
nine_hours_segment = pd.read_csv(nine_hours_segment_path, sep='\t', names=["audio"], index_col="audio").index
|
58 |
+
nine_hours_df = df.filter(items=nine_hours_segment, axis=0)
|
59 |
+
nine_hours_df.to_csv(os.path.join(csv_folder_path, f"9_hours.csv"))
|
60 |
+
csv_dict["9_hours"] = os.path.join(csv_folder_path, f"9_hours.csv")
|
61 |
+
|
62 |
+
one_hours_segments = [ os.path.join(f.path, "handles.txt") for f in os.scandir( os.path.join(main_folder_path, "train/limited_supervision/1hr")) if f.is_dir()]
|
63 |
+
one_hours_segments = pd.concat([pd.read_csv(one, sep='\t', names=["audio"], index_col="audio") for one in one_hours_segments], axis=0).index
|
64 |
+
one_hours_df = df.filter(items=one_hours_segments, axis=0)
|
65 |
+
one_hours_df.to_csv(os.path.join(csv_folder_path, f"1_hours.csv"))
|
66 |
+
csv_dict["1_hours"] = os.path.join(csv_folder_path, f"1_hours.csv")
|
67 |
+
|
68 |
+
|
69 |
+
|
70 |
+
|
71 |
+
dataset = DatasetDict.from_csv(csv_dict)
|
72 |
+
|
73 |
+
def extract_speaker_id_and_format_path(audio, split):
|
74 |
+
speaker_id = audio.split("_")[0]
|
75 |
+
chapter_id = audio.split("_")[1]
|
76 |
+
file = f"{audio}.opus"
|
77 |
+
|
78 |
+
path = os.path.join(main_folder_path, split, "audio", speaker_id, chapter_id, file)
|
79 |
+
return {"audio": path, "speaker_id": speaker_id, "chapter_id": chapter_id, "file": file, "id": audio}
|
80 |
+
|
81 |
+
# correct audio path
|
82 |
+
dataset = dataset.map(extract_speaker_id_and_format_path, input_columns=["audio", "split"], num_proc=args.cpu_num_workers, remove_columns=["split"])
|
83 |
+
dataset = dataset.cast_column("audio", Audio())
|
84 |
+
|
85 |
+
print(dataset)
|
86 |
+
print(dataset["dev"][0])
|
87 |
+
|
88 |
+
print("Embed table storage")
|
89 |
+
|
90 |
+
# load_dataset(...)
|
91 |
+
format = dataset["train"].format
|
92 |
+
dataset = dataset.with_format("arrow")
|
93 |
+
dataset = dataset.map(embed_table_storage, batched=True, num_proc=args.cpu_num_workers)
|
94 |
+
dataset = dataset.with_format(**format)
|
95 |
+
|
96 |
+
|
97 |
+
dataset.save_to_disk(args.output_dir, num_proc=args.cpu_num_workers)
|
98 |
+
|
99 |
+
if args.repo_id:
|
100 |
+
pushed = False
|
101 |
+
while not pushed:
|
102 |
+
try:
|
103 |
+
dataset.push_to_hub(args.repo_id, args.configuration, revision="refs/pr/15")
|
104 |
+
pushed = True
|
105 |
+
except:
|
106 |
+
pass
|
dutch/1_hours-00000-of-00001.parquet
ADDED
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|
|
|
|
|
|
|
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|
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|
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