Update the readme with statistics and styling

README.md

@@ -76,39 +76,29 @@ size_categories:
 
 ## Dataset Overview
 
-**CoRal** is a comprehensive Automatic Speech Recognition (ASR) dataset designed to capture the  diversity of the Danish language across various dialects, accents, genders, and age groups. The primary goal of the CoRal dataset is to provide a robust resource for training and evaluating ASR models that can understand and transcribe spoken Danish in all its variations.
+**CoRal** is a comprehensive Automatic Speech Recognition (ASR) dataset designed to
+capture the  diversity of the Danish language across various dialects, accents, genders,
+and age groups. The primary goal of the CoRal dataset is to provide a robust resource
+for training and evaluating ASR models that can understand and transcribe spoken Danish
+in all its variations.
 
 ### Key Features
 
-- **Dialect and Accent Diversity**: The dataset includes speech samples from all major Danish dialects as well as multiple accents, ensuring broad geographical coverage and the inclusion of regional linguistic features.
-- **Gender Representation**: Both male and female speakers are well-represented, offering balanced gender diversity.
-- **Age Range**: The dataset includes speakers from a wide range of age groups, providing a comprehensive resource for age-agnostic ASR model development.
-- **High-Quality Audio**: All recordings are of high quality, ensuring that the dataset can be used for both training and evaluation of high-performance ASR models.
+- **Dialect and Accent Diversity**: The dataset includes speech samples from all major
+  Danish dialects as well as multiple accents, ensuring broad geographical coverage and
+  the inclusion of regional linguistic features.
+- **Gender Representation**: Both male and female speakers are well-represented,
+  offering balanced gender diversity.
+- **Age Range**: The dataset includes speakers from a wide range of age groups,
+  providing a comprehensive resource for age-agnostic ASR model development.
+- **High-Quality Audio**: All recordings are of high quality, ensuring that the dataset
+  can be used for both training and evaluation of high-performance ASR models.
 
-## Data Fields
+### Quick Start
 
-- `id_recording`: Unique identifier for the recording.
-- `id_sentence`: Unique identifier for the text being read aloud.
-- `id_speaker`: Unique identifier for each speaker.
-- `text`: transcription of recording
-- `dialect`: Dialect classification
-- `gender`: Gender of the speaker (Male/Female/Non-binary).
-- `age`: Age of the speaker (Integer)
-- `country_birth`: Country of where speaker was born
-- `location`: adress of recording place.
-- `location_roomdim`: dimension of recording room.
-- `noise_level`: Noise level in the room given in dB.
-- `noise_type`: Noise exposed to the speaker while recording. Note the noise is not present in the audio (ingen/human/trafik).
-- `validated`: Manual 
-validation state of the recording (approved, maybe, rejected, "null").
-- `asr_validation_model`: Model id used for auto validation of the recodings.
-- `asr_prediction`: ASR output prediction.
-- `asr_wer`: Word error rate between `asr_prediction` and `text`
-- `asr_cer`: Character error rate between `asr_prediction` and `text`
-
-## Usage
-
-The CoRal dataset is ideal for training ASR models that need to generalise across different dialects and speaker demographics within the Danish language. Below is an example of how to load and use the dataset with Hugging Face's `datasets` library:
+The CoRal dataset is ideal for training ASR models that need to generalise across
+different dialects and speaker demographics within the Danish language. Below is an
+example of how to load and use the dataset with Hugging Face's `datasets` library:
 
 ```python
 from datasets import load_dataset
@@ -125,17 +115,127 @@ print(f"Audio: {audio['array']}")
 print(f"Text: {transcription}")
 ```
 
+
+## Data Fields
+
+- `id_recording`: Unique identifier for the recording.
+- `id_sentence`: Unique identifier for the text being read aloud.
+- `id_speaker`: Unique identifier for each speaker.
+- `text`: Text being read aloud.
+- `location`: Address of recording place.
+- `location_roomdim`: Dimension of recording room.
+- `noise_level`: Noise level in the room, given in dB.
+- `noise_type`: Noise exposed to the speaker while recording. Note the noise is not
+  present in the audio, but is there to mimic differences in speech in a noisy
+  environment.
+- `source_url`: URL to the source of the text.
+- `age`: Age of the speaker.
+- `gender`: Gender of the speaker.
+- `dialect`: Self-reported dialect of the speaker.
+- `country_birth`: Country where the speaker was born.
+- `validated`: Manual validation state of the recording.
+- `audio`: The audio file of the recording.
+- `asr_prediction`: ASR output prediction of the `asr_validation_model`.
+- `asr_validation_model`: Hugging Face Model ID used for automatic validation of the
+  recordings.
+- `asr_wer`: Word error rate between `asr_prediction` and `text`.
+- `asr_cer`: Character error rate between `asr_prediction` and `text`.
+
+
+## Read-aloud Data Statistics
+
+- **Audio**: 366.6/2.7/7.6 hours in the train/val/test split, respectively.
+- **Speakers**: 566/10/21 speakers in the train/val/test split, respectively.
+
+### Test distribution
+
+Gender distribution:
+- female: 58%
+- male: 42%
+
+Dialect distribution:
+- Bornholmsk: 10%
+- Fynsk: 10%
+- Københavnsk: 10%
+- Nordjysk: 11%
+- Sjællandsk: 11%
+- Sydømål: 14%
+- Sønderjysk: 10%
+- Vestjysk: 12%
+- Østjysk: 11%
+
+Age group distribution:
+- 0-24: 28%
+- 25-49: 32%
+- 50-: 40%
+
+Accent distribution:
+- native: 90%
+- foreign: 10%
+
+### Validation distribution
+
+Gender distribution:
+- female: 45%
+- male: 55%
+
+Dialect distribution:
+- Bornholmsk: 7%
+- Fynsk: 11%
+- Københavnsk: 9%
+- Nordjysk: 8%
+- Sjællandsk: 12%
+- Sydømål: 23%
+- Sønderjysk: 13%
+- Vestjysk: 8%
+- Østjysk: 9%
+
+Age group distribution:
+- 0-24: 11%
+- 25-49: 39%
+- 50-: 51%
+
+Accent distribution:
+- native: 81%
+- foreign: 19%
+
+
+## Conversational Data Statistics
+
+The conversational data is not yet available, but we are working on it and plan to
+release it during 2024.
+
+
 ## Example Applications
-- ASR Model Training: Train robust ASR models that can handle dialectal variations and diverse speaker demographics in Danish.
+- ASR Model Training: Train robust ASR models that can handle dialectal variations and
+  diverse speaker demographics in Danish.
 - Dialect Studies: Analyse the linguistic features of different Danish dialects.
 
-**Note** Speech Synthesis and Biometric Identification are not allowed using CoRal. For more information see [license](https://huggingface.co/datasets/alexandrainst/coral/blob/main/LICENSE) ad. 4  
+**Note** Speech Synthesis and Biometric Identification are not allowed using CoRal. For
+more information see
+[license](https://huggingface.co/datasets/alexandrainst/coral/blob/main/LICENSE) ad. 4
+
 
 ## License
-The dataset is licensed under a custom license, adapted from OpenRAIL-M, which allows commercial use with a few restrictions (speech synthesis and biometric identification). See [license](https://huggingface.co/datasets/alexandrainst/coral/blob/main/LICENSE).
+The dataset is licensed under a custom license, adapted from OpenRAIL-M, which allows
+commercial use with a few restrictions (speech synthesis and biometric identification).
+See [license](https://huggingface.co/datasets/alexandrainst/coral/blob/main/LICENSE).
+
+
+## Creators and Funders
+The CoRal project is funded by the [Danish Innovation
+Fund](https://innovationsfonden.dk/) and consists of the following partners:
+
+- [Alexandra Institute](https://alexandra.dk/)
+- [University of Copenhagen](https://www.ku.dk/)
+- [Agency for Digital Government](https://digst.dk/)
+- [Alvenir](https://www.alvenir.ai/)
+- [Corti](https://www.corti.ai/)
+
 
 ## Citation
-We will submit a research paper soon, but until then, if you use the CoRal dataset in your research or development, please cite it as follows:
+We will submit a research paper soon, but until then, if you use the CoRal dataset in
+your research or development, please cite it as follows:
 
 ```bibtex
 @dataset{coral2024,
@@ -144,4 +244,4 @@ We will submit a research paper soon, but until then, if you use the CoRal datas
   year      = {2024},
   url       = {https://hf.co/datasets/alexandrainst/coral},
 }
-```
+```