
# Fleur Kinyarwanda dataset
Fleur is a multilingual text and audio dataset. The original dataset was created by [Google](https://huggingface.co/datasets/google/fleurs) . The dataset can be used when building speech to text, speech to text translation and speech to speech translation. It is a good tool to benchmark speech application especially across languages. As of present Kinyarwanda did not have a fleur dataset hindering opportunities for building Kinyarwanda speech technology.

This dataset was created by 29 linguists that participated in the Training NLP for Linguist with a focus on Machine translation.

## Dataset Creation
The recordings are made of 2-4 different recordings for each sentence 

### Data Fields

The data fields are the same among all splits.
- **id** (int): ID of audio sample
- **num_samples** (int): Number of float values
- **path** (str): Path to the audio file
- **audio** (dict): Audio object including loaded audio array, sampling rate and path ot audio
- **raw_transcription** (str): The non-normalized transcription of the audio file
- **transcription** (str): Transcription of the audio file
- **gender** (int): Class id of gender
- **lang_id** (int): Class id of language
- **lang_group_id** (int): Class id of language group

### Contribution
Thanks to all the linguist who contributed and their teacher Samuel Olanrewaju and thanks [Kleber Kabanda](https://huggingface.co/Kleber) for curating and uploading the dataset
task_categories:automatic-speech-recognition annotations_creators:expert-generated annotations_creators:crowdsourced language_creators:crowdsourced language_creators:expert-generated size_categories:1K<n<10K language:rw license:cc-by-4.0 speech-recognition fleurs-dataset region:us
{"annotations_creators": ["expert-generated", "crowdsourced"], "language_creators": ["crowdsourced", "expert-generated"], "language": ["rw"], "license": ["cc-by-4.0"], "size_categories": ["1K<n<10K"], "task_categories": ["automatic-speech-recognition"], "pretty_name": "Fleurs dataset Kinyarwanda", "tags": ["speech-recognition", "fleurs-dataset"]}
TAGS
#task_categories-automatic-speech-recognition #annotations_creators-expert-generated #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-expert-generated #size_categories-1K<n<10K #language-Kinyarwanda #license-cc-by-4.0 #speech-recognition #fleurs-dataset #region-us 


# Fleur Kinyarwanda dataset
Fleur is a multilingual text and audio dataset. The original dataset was created by Google . The dataset can be used when building speech to text, speech to text translation and speech to speech translation. It is a good tool to benchmark speech application especially across languages. As of present Kinyarwanda did not have a fleur dataset hindering opportunities for building Kinyarwanda speech technology.

This dataset was created by 29 linguists that participated in the Training NLP for Linguist with a focus on Machine translation.

## Dataset Creation
The recordings are made of 2-4 different recordings for each sentence 

### Data Fields

The data fields are the same among all splits.
- id (int): ID of audio sample
- num_samples (int): Number of float values
- path (str): Path to the audio file
- audio (dict): Audio object including loaded audio array, sampling rate and path ot audio
- raw_transcription (str): The non-normalized transcription of the audio file
- transcription (str): Transcription of the audio file
- gender (int): Class id of gender
- lang_id (int): Class id of language
- lang_group_id (int): Class id of language group

### Contribution
Thanks to all the linguist who contributed and their teacher Samuel Olanrewaju and thanks Kleber Kabanda for curating and uploading the dataset
# Fleur Kinyarwanda dataset
Fleur is a multilingual text and audio dataset. The original dataset was created by Google . The dataset can be used when building speech to text, speech to text translation and speech to speech translation. It is a good tool to benchmark speech application especially across languages. As of present Kinyarwanda did not have a fleur dataset hindering opportunities for building Kinyarwanda speech technology.

This dataset was created by 29 linguists that participated in the Training NLP for Linguist with a focus on Machine translation. ## Dataset Creation
The recordings are made of 2-4 different recordings for each sentence ### Data Fields

The data fields are the same among all splits.
- id (int): ID of audio sample
- num_samples (int): Number of float values
- path (str): Path to the audio file
- audio (dict): Audio object including loaded audio array, sampling rate and path ot audio
- raw_transcription (str): The non-normalized transcription of the audio file
- transcription (str): Transcription of the audio file
- gender (int): Class id of gender
- lang_id (int): Class id of language
- lang_group_id (int): Class id of language group ### Contribution
Thanks to all the linguist who contributed and their teacher Samuel Olanrewaju and thanks Kleber Kabanda for curating and uploading the dataset
TAGS
#task_categories-automatic-speech-recognition #annotations_creators-expert-generated #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-expert-generated #size_categories-1K<n<10K #language-Kinyarwanda #license-cc-by-4.0 #speech-recognition #fleurs-dataset #region-us 
 # Fleur Kinyarwanda dataset
Fleur is a multilingual text and audio dataset. The original dataset was created by Google . The dataset can be used when building speech to text, speech to text translation and speech to speech translation. It is a good tool to benchmark speech application especially across languages. As of present Kinyarwanda did not have a fleur dataset hindering opportunities for building Kinyarwanda speech technology.

This dataset was created by 29 linguists that participated in the Training NLP for Linguist with a focus on Machine translation. ## Dataset Creation
The recordings are made of 2-4 different recordings for each sentence ### Data Fields

The data fields are the same among all splits.
- id (int): ID of audio sample
- num_samples (int): Number of float values
- path (str): Path to the audio file
- audio (dict): Audio object including loaded audio array, sampling rate and path ot audio
- raw_transcription (str): The non-normalized transcription of the audio file
- transcription (str): Transcription of the audio file
- gender (int): Class id of gender
- lang_id (int): Class id of language
- lang_group_id (int): Class id of language group ### Contribution
Thanks to all the linguist who contributed and their teacher Samuel Olanrewaju and thanks Kleber Kabanda for curating and uploading the dataset