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--- |
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license: cc |
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pretty_name: M-AILABS Speech Dataset (French) |
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language: |
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- fr |
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task_categories: |
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- speech-processing |
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task_ids: |
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- automatic-speech-recognition |
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size_categories: |
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fr: |
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- 10K<n<100K |
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--- |
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## Dataset Description |
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- **Homepage:** https://www.caito.de/2019/01/the-m-ailabs-speech-dataset/ |
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### Dataset Summary |
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The M-AILABS Speech Dataset is the first large dataset that we are providing free-of-charge, freely usable as training data for speech recognition and speech synthesis. |
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Most of the data is based on LibriVox and Project Gutenberg. The training data consist of nearly thousand hours of audio and the text-files in prepared format. |
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A transcription is provided for each clip. Clips vary in length from 1 to 20 seconds and have a total length of approximately shown in the list (and in the respective info.txt-files) below. |
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The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded by the LibriVox project and is also in the public domain – except for Ukrainian. |
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Ukrainian audio was kindly provided either by Nash Format or Gwara Media for machine learning purposes only (please check the data info.txt files for details). |
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### Languages |
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French |
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## Dataset Structure |
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### Data Instances |
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A typical data point comprises the path to the audio file, called audio and its sentence. |
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### Data Fields |
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- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`. |
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- sentence: The sentence the user was prompted to speak |
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### Data Splits |
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The speech material has not been subdivided into portions, everything is in the "train" split. |
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The train split consists of 82825 audio clips and the related sentences. |
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### Contributions |
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[@gigant](https://huggingface.co/gigant) added this dataset. |
