Update README

README.md

@@ -44,4 +44,164 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: cc-by-sa-4.0
+task_categories:
+- automatic-speech-recognition
+- voice-activity-detection
+language:
+- fr
+tags:
+- NLP
+- conversational
+- automatic speech recognition
+- voice activity detection
+- inter-pausal units
+pretty_name: SUMM-RE small
+size_categories:
+- 100K<n<1M
 ---
+# Dataset Card for SUMM-RE small
+
+Manually corrected transcripts of French conversations, aligned with the audio signal.
+
+## Dataset Details
+
+### Dataset Description
+
+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). SUMM-RE small is a subset of the full SUMM-RE corpus for which the transcripts have been manually corrected and aligned with the audio down to phoneme level. It can be used for the evaluation of automatic speech recognition and voice activity detection models.
+
+The SUMM-RE small subset consists of 10 randomly selected conversations. Each conversation lasts roughly 20 minutes and involves 3-4 speakers. Each participant has an individual microphone and associated .wav file leading to 39 audio files in all. 
+
+
+- **Created by:** The corpus was recorded and manually annotated by the Language and Speech Lab (LPL) at the University of Aix-Marseille, France.
+- **Funded by:** The National Research Agency of France (ANR) for the SUMM-RE project (ANR-20-CE23-0017).
+- **Shared by:** LINAGORA (coordinator of the SUMM-RE project)
+- **Language:** French
+- **License:** CC BY-SA 4.0
+
+### Dataset Sources 
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** Both gold corrected and automatic transcripts (produced with Whisper) can be found on [Ortolang](https://www.ortolang.fr/market/corpora/summ-re-asru).
+- **Paper:** [More Information Needed]
+
+
+## Uses
+
+### Direct Use
+
+This version of SUMM-RE small is designed for the evaluation of automatic speech recognition models and voice activity detection for conversational, spoken French.
+
+### Out-of-Scope Use
+
+Due to its size, the corpus is not suitable for model training.
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+
+- **meeting_id**, e.g. 001a_PARL, includes:
+  - experiment number, e.g. 001
+  - meeting order: a|b|c (there were three meetings per experiment)
+  - experiment type: E (experiment) | P (pilot experiment)
+  - scenario/topic: A|B|C|D|E
+  - meeting type: R (reporting) | D (decision) | P (planning)
+  - recording location: L (LPL) | H (H2C2 studio) | Z (Zoom) | D (at home)
+- **speaker_id**
+- **audio_id**: meeting_id + speaker_id
+- **audio**: the .wav file for an individual speaker
+- **transcript**: the manually corrected transcript (corrected from Whisper transcripts)
+- **ipus**: a list of start and end times for manually annotated interpausal units (units of speech from a single speaker that are separated by silences above a certain threshold)
+- **words**: a list of start and end times for each word
+- **phonemes**: a list of start and end times for each phoneme
+
+
+
+## Dataset Creation
+
+### Curation Rationale
+
+The full SUMM-RE corpus, which includes meeting summaries, is designed to train and evaluate models for meeting summarization. SUMM-RE small is an extract of this corpus used to evaluate various stages of the summarization pipeline, starting with automatic transcription of the audio signal.
+
+### Source Data
+
+<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+
+The SUMM-RE corpus is an original corpus designed by members of LINAGORA and the University of Aix-Marseille and recorded by the latter. 
+
+#### Data Collection and Processing
+
+<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+
+[More Information Needed]
+
+#### Who are the source data producers?
+
+<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+
+Corpus design and production:
+- University of Aix-Marseille: Océane Granier (corpus conception, recording, annotation), Laurent Prévot (corpus conception, annotatation, supervision), Hiroyoshi Yamasaki (corpus cleaning, alignment and anonymization), Roxanne Bertrand (corpus conception and annotation) with helpful input from Brigitte Bigi and Stéphane Rauzy.
+
+- LINAGORA: Julie Hunter, Kate Thompson and Guokan Shang (corpus conception)
+
+Corpus participants:
+- Participants for the in-person conversations were recruited on the University of Aix-Marseille campus.
+- Participants for the zoom meetings were recruited through [Prolific](https://www.prolific.com/).
+
+### Annotations
+
+<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+
+#### Annotation process
+
+<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+<!-- This section describes the people or systems who created the annotations. -->
+
+Principal annotator: Océane Granier 
+
+Additional assistance from: Laurent Prévot, Hiroyoshi Yamasaki and Roxane Bertrand
+
+#### Personal and Sensitive Information
+
+<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+
+The audio and transcripts have been (semi-automatically) anonymized.
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+Hiroyoshi Yamasaki, Jérôme Louradour, Julie Hunter and Laurent Prévot (2023): "Transcribing and aligning conversational speech: A hybrid pipeline applied to French conversations," Workshop on Automatic Speech Recognition and Understanding.
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
+
+[More Information Needed]
+