|
--- |
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license: apache-2.0 |
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dataset_info: |
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- config_name: commonvoice |
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features: |
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- name: id |
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dtype: string |
|
- name: text |
|
dtype: string |
|
- name: audio |
|
dtype: |
|
audio: |
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sampling_rate: 16000 |
|
- name: words |
|
sequence: string |
|
- name: word_start |
|
sequence: float64 |
|
- name: word_end |
|
sequence: float64 |
|
- name: entity_start |
|
sequence: int64 |
|
- name: entity_end |
|
sequence: int64 |
|
- name: entity_label |
|
sequence: string |
|
splits: |
|
- name: train |
|
num_bytes: 43744079378.659 |
|
num_examples: 948733 |
|
- name: valid |
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num_bytes: 722372503.994 |
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num_examples: 16353 |
|
download_size: 39798988113 |
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dataset_size: 44466451882.653 |
|
- config_name: gigaspeech |
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features: |
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- name: id |
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dtype: string |
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- name: text |
|
dtype: string |
|
- name: audio |
|
dtype: |
|
audio: |
|
sampling_rate: 16000 |
|
- name: words |
|
sequence: string |
|
- name: word_start |
|
sequence: float64 |
|
- name: word_end |
|
sequence: float64 |
|
- name: entity_start |
|
sequence: int64 |
|
- name: entity_end |
|
sequence: int64 |
|
- name: entity_label |
|
sequence: string |
|
splits: |
|
- name: train |
|
num_bytes: 1032024261294.48 |
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num_examples: 8282987 |
|
- name: valid |
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num_bytes: 1340974408.04 |
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num_examples: 5715 |
|
download_size: 1148966064515 |
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dataset_size: 1033365235702.52 |
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- config_name: libris |
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features: |
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- name: id |
|
dtype: string |
|
- name: text |
|
dtype: string |
|
- name: audio |
|
dtype: |
|
audio: |
|
sampling_rate: 16000 |
|
- name: words |
|
sequence: string |
|
- name: word_start |
|
sequence: float64 |
|
- name: word_end |
|
sequence: float64 |
|
- name: entity_start |
|
sequence: int64 |
|
- name: entity_end |
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sequence: int64 |
|
- name: entity_label |
|
sequence: string |
|
splits: |
|
- name: train |
|
num_bytes: 63849575890.896 |
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num_examples: 281241 |
|
- name: valid |
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num_bytes: 793442600.643 |
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num_examples: 5559 |
|
download_size: 61361142328 |
|
dataset_size: 64643018491.539 |
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- config_name: mustc |
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features: |
|
- name: id |
|
dtype: string |
|
- name: text |
|
dtype: string |
|
- name: audio |
|
dtype: |
|
audio: |
|
sampling_rate: 16000 |
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- name: words |
|
sequence: string |
|
- name: word_start |
|
sequence: float64 |
|
- name: word_end |
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sequence: float64 |
|
- name: entity_start |
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sequence: int64 |
|
- name: entity_end |
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sequence: int64 |
|
- name: entity_label |
|
sequence: string |
|
splits: |
|
- name: train |
|
num_bytes: 55552777413.1 |
|
num_examples: 248612 |
|
- name: valid |
|
num_bytes: 313397447.704 |
|
num_examples: 1408 |
|
download_size: 52028374666 |
|
dataset_size: 55866174860.804 |
|
- config_name: tedlium |
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features: |
|
- name: id |
|
dtype: string |
|
- name: text |
|
dtype: string |
|
- name: audio |
|
dtype: |
|
audio: |
|
sampling_rate: 16000 |
|
- name: words |
|
sequence: string |
|
- name: word_start |
|
sequence: float64 |
|
- name: word_end |
|
sequence: float64 |
|
- name: entity_start |
|
sequence: int64 |
|
- name: entity_end |
|
sequence: int64 |
|
- name: entity_label |
|
sequence: string |
|
splits: |
|
- name: train |
|
num_bytes: 56248950771.568 |
|
num_examples: 268216 |
|
- name: valid |
|
num_bytes: 321930549.928 |
|
num_examples: 1456 |
|
download_size: 52557126451 |
|
dataset_size: 56570881321.496 |
|
- config_name: voxpopuli |
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features: |
|
- name: id |
|
dtype: string |
|
- name: text |
|
dtype: string |
|
- name: audio |
|
dtype: |
|
audio: |
|
sampling_rate: 16000 |
|
- name: words |
|
sequence: string |
|
- name: word_start |
|
sequence: float64 |
|
- name: word_end |
|
sequence: float64 |
|
- name: entity_start |
|
sequence: int64 |
|
- name: entity_end |
|
sequence: int64 |
|
- name: entity_label |
|
sequence: string |
|
splits: |
|
- name: train |
|
num_bytes: 118516424284.524 |
|
num_examples: 182463 |
|
- name: valid |
|
num_bytes: 1144543020.808 |
|
num_examples: 1842 |
|
download_size: 98669668241 |
|
dataset_size: 119660967305.332 |
|
configs: |
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- config_name: commonvoice |
|
data_files: |
|
- split: train |
|
path: commonvoice/train-* |
|
- split: valid |
|
path: commonvoice/valid-* |
|
- config_name: gigaspeech |
|
data_files: |
|
- split: train |
|
path: gigaspeech/train-* |
|
- split: valid |
|
path: gigaspeech/valid-* |
|
- config_name: libris |
|
data_files: |
|
- split: train |
|
path: libris/train-* |
|
- split: valid |
|
path: libris/valid-* |
|
- config_name: mustc |
|
data_files: |
|
- split: train |
|
path: mustc/train-* |
|
- split: valid |
|
path: mustc/valid-* |
|
- config_name: tedlium |
|
data_files: |
|
- split: train |
|
path: tedlium/train-* |
|
- split: valid |
|
path: tedlium/valid-* |
|
- config_name: voxpopuli |
|
data_files: |
|
- split: train |
|
path: voxpopuli/train-* |
|
- split: valid |
|
path: voxpopuli/valid-* |
|
language: |
|
- en |
|
pretty_name: Speech Recognition Alignment Dataset |
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size_categories: |
|
- 10M<n<100M |
|
--- |
|
|
|
# Speech Recognition Alignment Dataset |
|
|
|
This dataset is a variation of several widely-used ASR datasets, encompassing Librispeech, MuST-C, TED-LIUM, VoxPopuli, Common Voice, and GigaSpeech. The difference is this dataset includes: |
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- Precise alignment between audio and text. |
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- Text that has been punctuated and made case-sensitive. |
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- Identification of named entities in the text. |
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|
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# Usage |
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|
|
First, install the latest version of the 🤗 Datasets package: |
|
|
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```bash |
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pip install --upgrade pip |
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pip install --upgrade datasets[audio] |
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``` |
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|
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The dataset can be downloaded and pre-processed on disk using the [`load_dataset`](https://huggingface.co/docs/datasets/v2.14.5/en/package_reference/loading_methods#datasets.load_dataset) |
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function: |
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|
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```python |
|
from datasets import load_dataset |
|
|
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# Available dataset: 'libris','mustc','tedlium','voxpopuli','commonvoice','gigaspeech' |
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dataset = load_dataset("nguyenvulebinh/asr-alignment", "libris") |
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|
|
# take the first sample of the validation set |
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sample = dataset["train"][0] |
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``` |
|
|
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It can also be streamed directly from the Hub using Datasets' [streaming mode](https://huggingface.co/blog/audio-datasets#streaming-mode-the-silver-bullet). |
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Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire |
|
dataset to disk: |
|
|
|
```python |
|
from datasets import load_dataset |
|
|
|
dataset = load_dataset("nguyenvulebinh/asr-alignment", "libris", streaming=True) |
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# take the first sample of the validation set |
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sample = next(iter(dataset["train"])) |
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``` |
|
|
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## Citation |
|
|
|
If you use this data, please consider citing the [ICASSP 2024 Paper: SYNTHETIC CONVERSATIONS IMPROVE MULTI-TALKER ASR](): |
|
``` |
|
@INPROCEEDINGS{synthetic-multi-asr-nguyen, |
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author={Nguyen, Thai-Binh and Waibel, Alexander}, |
|
booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
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title={SYNTHETIC CONVERSATIONS IMPROVE MULTI-TALKER ASR}, |
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year={2024}, |
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volume={}, |
|
number={}, |
|
} |
|
|
|
``` |
|
|
|
## License |
|
|
|
This dataset is licensed in accordance with the terms of the original dataset. |
