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Update README.md
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README.md
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@@ -84,20 +84,20 @@ The audio and transcriptions are in English, as per the TED talks at http://www.
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### Data Instances
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```
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-
{'audio': {'path': '/home/sanchitgandhi/cache/downloads/extracted/6e3655f9e735ae3c467deed1df788e0dabd671c1f3e2e386e30aa3b571bd9761/TEDLIUM_release1/train/
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'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346,
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0.00091553, 0.00085449], dtype=float32),
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'sampling_rate': 16000},
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'text': '{COUGH} but <sil> i was so {COUGH} utterly unqualified for(2) this project and {NOISE} so utterly ridiculous {SMACK} and ignored the brief {SMACK} <sil>',
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'speaker_id': 'PaulaScher_2008P',
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'gender': 'female',
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'file': '/home/sanchitgandhi/cache/downloads/extracted/6e3655f9e735ae3c467deed1df788e0dabd671c1f3e2e386e30aa3b571bd9761/TEDLIUM_release1/train/
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'id': 'PaulaScher_2008P-1003.35-1011.16-<o,f0,female>'}
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```
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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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- file: A path to the downloaded audio file in .
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- text: the transcription of the audio file.
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- gender: the gender of the speaker. One of: male, female or N/A.
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- id: unique id of the data sample.
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### Data Splits
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There are three releases for the TED-LIUM corpus, progressively increasing the number of transcribed speech training data from 118 hours (Release 1), to 207 hours (Release 2), to 452 hours (Release 3).
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Release 1
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- 774 audio talks and automatically aligned transcriptions.
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- Contains 118 hours of speech audio data.
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- Homepage: https://www.openslr.org/7/
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@@ -126,6 +126,10 @@ Release 3:
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- Selected monolingual data for language modeling from WMT12 publicly available corpora: these files come from the TED-LIUM 2 release, but have been modified to produce a tokenization more relevant for English language.
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- Homepage: https://www.openslr.org/51/
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Each release is split into a training, validation and test set:
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| Split | Release 1 | Release 2 | Release 3 |
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### Curation Rationale
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TED-LIUM was built during [The International Workshop on Spoken Language Trans- lation (IWSLT) 2011 Evaluation Campaign](https://aclanthology.org/2011.iwslt-evaluation.1/), an annual workshop focused on the automatic translation of public talks and included tracks for speech recognition, speech translation, text translation, and system combination.
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### Source Data
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#### Initial Data Collection and Normalization
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The data was obtained from publicly available TED talks at http://www.ted.com. Proper alignments between the speech and the transcribed text were generated using an in-house speaker segmentation and clustering tool (
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#### Who are the source language producers?
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publisher="Springer International Publishing",
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pages="198--208",
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}
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```
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### Data Instances
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```
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+
{'audio': {'path': '/home/sanchitgandhi/cache/downloads/extracted/6e3655f9e735ae3c467deed1df788e0dabd671c1f3e2e386e30aa3b571bd9761/TEDLIUM_release1/train/sph/PaulaScher_2008P.sph',
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'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346,
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0.00091553, 0.00085449], dtype=float32),
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'sampling_rate': 16000},
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'text': '{COUGH} but <sil> i was so {COUGH} utterly unqualified for(2) this project and {NOISE} so utterly ridiculous {SMACK} and ignored the brief {SMACK} <sil>',
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'speaker_id': 'PaulaScher_2008P',
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'gender': 'female',
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'file': '/home/sanchitgandhi/cache/downloads/extracted/6e3655f9e735ae3c467deed1df788e0dabd671c1f3e2e386e30aa3b571bd9761/TEDLIUM_release1/train/sph/PaulaScher_2008P.sph',
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'id': 'PaulaScher_2008P-1003.35-1011.16-<o,f0,female>'}
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```
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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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- file: A path to the downloaded audio file in .sph format.
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- text: the transcription of the audio file.
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- gender: the gender of the speaker. One of: male, female or N/A.
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- id: unique id of the data sample.
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### Data Splits
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There are three releases for the TED-LIUM corpus, progressively increasing the number of transcribed speech training data from 118 hours (Release 1), to 207 hours (Release 2), to 452 hours (Release 3).
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Release 1:
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- 774 audio talks and automatically aligned transcriptions.
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- Contains 118 hours of speech audio data.
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- Homepage: https://www.openslr.org/7/
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- Selected monolingual data for language modeling from WMT12 publicly available corpora: these files come from the TED-LIUM 2 release, but have been modified to produce a tokenization more relevant for English language.
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- Homepage: https://www.openslr.org/51/
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Release 3 contains two different corpus distributions:
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- The ‘legacy’ one, on which the dev and test datasets are the same as in TED-LIUM 2 (and TED-LIUM 1).
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- The ‘speaker adaptation’ one, specially designed for experiments on speaker adaptation.
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Each release is split into a training, validation and test set:
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| Split | Release 1 | Release 2 | Release 3 |
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### Curation Rationale
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TED-LIUM was built during [The International Workshop on Spoken Language Trans- lation (IWSLT) 2011 Evaluation Campaign](https://aclanthology.org/2011.iwslt-evaluation.1/), an annual workshop focused on the automatic translation of public talks and included tracks for speech recognition, speech translation, text translation, and system combination.
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### Source Data
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#### Initial Data Collection and Normalization
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The data was obtained from publicly available TED talks at http://www.ted.com. Proper alignments between the speech and the transcribed text were generated using an in-house speaker segmentation and clustering tool (_LIUM_SpkDiarization_). Speech disfluencies (e.g. repetitions, hesitations, false starts) were treated in the following way: repetitions were transcribed, hesitations mapped to a specific filler word, and false starts not taken into account. For full details on the data collection and processing, refer to the [TED-LIUM paper](https://aclanthology.org/L12-1405/).
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#### Who are the source language producers?
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publisher="Springer International Publishing",
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pages="198--208",
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}
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```
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