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-- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
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-# Dataset Card for Nexdata/Changsha_Dialect_Speech_Data_by_Mobile_Phone
-
-## Table of Contents
-- [Table of Contents](#table-of-contents)
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **Homepage:** https://www.nexdata.ai/datasets/944?source=Huggingface
-- **Repository:**
-- **Paper:**
-- **Leaderboard:**
-- **Point of Contact:**
-
-### Dataset Summary
-
-2,000 Changsha natives participated in the recording, covering multiple age groups, with a balanced gender distribution and authentic accent. The recorded text is rich in content, covering general, interactive, car, home and other categories. Local people in changsha check and proofread. The accuracy of sentences is 95%. It is mainly applied to speech recognition, machine translation and voiceprint recognition.
-                           
-For more details, please refer to the link: https://www.nexdata.ai/datasets/944?source=Huggingface
-
-### Supported Tasks and Leaderboards
-
-automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
-
-### Languages
-
-Changsha Dialect
-## Dataset Structure
-
-### Data Instances
-
-[More Information Needed]
-
-### Data Fields
-
-[More Information Needed]
-
-### Data Splits
-
-[More Information Needed]
-
-## Dataset Creation
-
-### Curation Rationale
-
-[More Information Needed]
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
-
-[More Information Needed]
-
-### Annotations
-
-#### Annotation process
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-[More Information Needed]
-
-### Personal and Sensitive Information
-
-[More Information Needed]
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[More Information Needed]
-
-### Discussion of Biases
-
-[More Information Needed]
-
-### Other Known Limitations
-
-[More Information Needed]
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed]
-
-### Licensing Information
-
-Commerical License: https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing  
-
-### Citation Information
-
-[More Information Needed]
-
-### Contributions
+# Dataset Card for Nexdata/Saudi_Arabic_Spontaneous_Speech_Data
+
+## Description
+849 Hours - Saudi Arabic Spontaneous Speech Data, the content covering multiple topics. All the speech audio was manually transcribed into text content; speaker identity, gender, and other attribution are also annotated. This dataset can be used for voiceprint recognition model training, corpus construction for machine translation, and algorithm research introduction
+
+For more details, please refer to the link: https://www.nexdata.ai/datasets/1150?source=Huggingface
+
+# Specifications
+## Format
+16kHz, 16bit, wav, mono channel;
+## Content category
+including interview, variety show, live, etc.
+## Language
+Arabic;
+## Annotation
+annotation for the transcription text, speaker identification, gender;
+## Application scenarios
+speech recognition, video caption generation and video content review;
+## Accuracy
+at a Sentence Accuracy Rate (SAR) of being no less than 95%.
+
+# Licensing Information
+Commerical License