polinaeterna HF staff reach-vb HF staff commited on
Commit
d22a730
1 Parent(s): afdc9bf

README updates to include code snippets and correct leaderboard URL (#6)

Browse files

- README updates to include code snippets and correct leaderboard URL (4b653d4d838af288df031ebbba95b8edd911adca)
- up (8e9b160e5190a078e2a65acb3b09175d286ff9bf)


Co-authored-by: Vaibhav Srivastav <reach-vb@users.noreply.huggingface.co>

Files changed (1) hide show
  1. README.md +51 -1
README.md CHANGED
@@ -33,6 +33,7 @@ task_categories:
33
  - [Dataset Summary](#dataset-summary)
34
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
35
  - [Languages](#languages)
 
36
  - [Dataset Structure](#dataset-structure)
37
  - [Data Instances](#data-instances)
38
  - [Data Fields](#data-fields)
@@ -57,7 +58,7 @@ task_categories:
57
  - **Homepage:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94)
58
  - **Repository:** [Needs More Information]
59
  - **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411)
60
- - **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/dataset/multilingual-librispeech)
61
 
62
  ### Dataset Summary
63
 
@@ -75,6 +76,55 @@ MLS dataset is a large multilingual corpus suitable for speech research. The dat
75
 
76
  The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish
77
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
78
  ## Dataset Structure
79
 
80
  ### Data Instances
 
33
  - [Dataset Summary](#dataset-summary)
34
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
35
  - [Languages](#languages)
36
+ - [How to use](#how-to-use)
37
  - [Dataset Structure](#dataset-structure)
38
  - [Data Instances](#data-instances)
39
  - [Data Fields](#data-fields)
 
58
  - **Homepage:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94)
59
  - **Repository:** [Needs More Information]
60
  - **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411)
61
+ - **Leaderboard:** [🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=facebook%2Fmultilingual_librispeech&only_verified=0&task=automatic-speech-recognition&config=-unspecified-&split=-unspecified-&metric=wer)
62
 
63
  ### Dataset Summary
64
 
 
76
 
77
  The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish
78
 
79
+ ### How to use
80
+
81
+ The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
82
+
83
+ For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German):
84
+ ```python
85
+ from datasets import load_dataset
86
+
87
+ mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
88
+ ```
89
+
90
+ 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.
91
+ ```python
92
+ from datasets import load_dataset
93
+
94
+ mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True)
95
+
96
+ print(next(iter(mls)))
97
+ ```
98
+
99
+ *Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
100
+
101
+ Local:
102
+
103
+ ```python
104
+ from datasets import load_dataset
105
+ from torch.utils.data.sampler import BatchSampler, RandomSampler
106
+
107
+ mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
108
+ batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False)
109
+ dataloader = DataLoader(mls, batch_sampler=batch_sampler)
110
+ ```
111
+
112
+ Streaming:
113
+
114
+ ```python
115
+ from datasets import load_dataset
116
+ from torch.utils.data import DataLoader
117
+
118
+ mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True)
119
+ dataloader = DataLoader(mls, batch_size=32)
120
+ ```
121
+
122
+ To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
123
+
124
+ ### Example scripts
125
+
126
+ Train your own CTC or Seq2Seq Automatic Speech Recognition models on MultiLingual Librispeech with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
127
+
128
  ## Dataset Structure
129
 
130
  ### Data Instances