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@@ -67,7 +67,18 @@ _Note: if the data viewer is not working, use the "example" subset._
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  # SUMM-RE
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- SUMM-RE is a collection of transcripts of French conversations, aligned with the audio signal.
 
 
 
 
 
 
 
 
 
 
 
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  Data from the `dev` and `test` splits have been manually transcribed and aligned and so are suitable for the evaluation of automatic speech recognition and voice activity detection models.
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@@ -75,34 +86,20 @@ Data from the `train` split has been automatically transcribed and aligned with
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  The audio and transcripts used to evaluate this pipeline, a subset of the `dev` split<sup>*</sup>, can be found on [Ortolang](https://www.ortolang.fr/market/corpora/summ-re-asru/).
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- The full dataset is described in Hunter et al. (2024): "SUMM-RE: A corpus of French meeting-style conversations".
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  The `dev` and `test` splits of SUMM-RE can be used for the evaluation of automatic speech recognition models and voice activity detection for conversational, spoken French.
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  Speaker diarization can also be evaluated if several tracks of a same meeting are merged together.
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  SUMM-RE transcripts can be used for the training of language models.
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- ## Dataset Description
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- The SUMM-RE dataset is a corpus of meeting-style conversations in French created for the purpose of the SUMM-RE project (ANR-20-CE23-0017).
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  Each conversation lasts roughly 20 minutes. The number of conversations contained in each split is as follows:
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- - `train`: 210 (x ~20 minutes = ~70 hours)
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  - `dev`: 36 (x ~20 minutes = ~12 hours)
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- - `test`: 37 (x ~20 minutes = ~12.3 hours)
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  Each conversation contains 3-4 speakers (and in rare cases, 2) and each participant has an individual microphone and associated audio track, giving rise to the following number of tracks for each split:
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- - `train`: 684
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- - `dev`: 130
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- - `test`: 124
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- - **Created by:** Recording and manual correction of the corpus was carried out by the Language and Speech Lab (LPL) at the University of Aix-Marseille, France.
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- - **Funded by:** The National Research Agency of France (ANR) for the SUMM-RE project (ANR-20-CE23-0017).
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- - **Shared by:** LINAGORA (coordinator of the SUMM-RE project)
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- - **Language:** French
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- - **License:** CC BY-SA 4.0
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  ## Dataset Structure
 
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  # SUMM-RE
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+ The SUMM-RE dataset is a collection of transcripts of French conversations, aligned with the audio signal.
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+ It is a corpus of meeting-style conversations in French created for the purpose of the SUMM-RE project (ANR-20-CE23-0017).
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+
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+ The full dataset is described in Hunter et al. (2024): "SUMM-RE: A corpus of French meeting-style conversations".
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+
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+ - **Created by:** Recording and manual correction of the corpus was carried out by the Language and Speech Lab (LPL) at the University of Aix-Marseille, France.
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+ - **Funded by:** The National Research Agency of France (ANR) for the SUMM-RE project (ANR-20-CE23-0017).
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+ - **Shared by:** LINAGORA (coordinator of the SUMM-RE project)
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+ - **Language:** French
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+ - **License:** CC BY-SA 4.0
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+
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+ ## Dataset Description
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  Data from the `dev` and `test` splits have been manually transcribed and aligned and so are suitable for the evaluation of automatic speech recognition and voice activity detection models.
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  The audio and transcripts used to evaluate this pipeline, a subset of the `dev` split<sup>*</sup>, can be found on [Ortolang](https://www.ortolang.fr/market/corpora/summ-re-asru/).
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  The `dev` and `test` splits of SUMM-RE can be used for the evaluation of automatic speech recognition models and voice activity detection for conversational, spoken French.
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  Speaker diarization can also be evaluated if several tracks of a same meeting are merged together.
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  SUMM-RE transcripts can be used for the training of language models.
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  Each conversation lasts roughly 20 minutes. The number of conversations contained in each split is as follows:
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+ - `train`: 210 (x ~20 minutes = ~67 hours)
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  - `dev`: 36 (x ~20 minutes = ~12 hours)
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+ - `test`: 37 (x ~20 minutes = ~12 hours)
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  Each conversation contains 3-4 speakers (and in rare cases, 2) and each participant has an individual microphone and associated audio track, giving rise to the following number of tracks for each split:
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+ - `train`: 684 (x ~20 minutes = ~226 hours)
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+ - `dev`: 130 (x ~20 minutes = ~43 hours)
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+ - `test`: 124 (x ~20 minutes = ~41 hours)
 
 
 
 
 
 
 
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  ## Dataset Structure