oddadmix/arabic-audio-collection-sudanese-ahmed-gobara

Ahmed Gobara Sudanese Arabic Speech Dataset

Dataset Summary

The Ahmed Gobara Sudanese Arabic Speech Dataset is a single-speaker Sudanese Arabic speech corpus containing approximately 19 hours of speech recordings and corresponding transcripts.

While compact, the dataset offers a clean, consistent single-speaker resource in Sudanese Arabic — an Arabic variety with very few open speech resources — making it especially valuable for voice cloning, speaker adaptation, and dialect-focused fine-tuning experiments.

Alongside the spoken Arabic text, the transcripts capture paralinguistic vocalizations, breathing, and emotional cues, enabling the development of expressive, natural-sounding AI models that go beyond flat dictation.

The dataset was created to support Arabic speech technology research and development, including:

• —Automatic Speech Recognition (ASR)
• —Text-to-Speech (TTS) and Expressive TTS
• —Speech Foundation Models
• —Audio-Text Alignment
• —Speaker Adaptation
• —Paralinguistic and Emotion Recognition
• —Sudanese Dialect and Arabic Language Technology Research

Transcriptions were generated and curated by the dataset creator using an AI-assisted transcription pipeline and additional quality-control procedures to ensure the accurate logging of non-verbal tokens.

Non-Verbal Vocalization Tags

The transcripts include a comprehensive set of non-verbal tokens to capture the true nuance of human speech and breathing. The supported tags are:

<laugh> <cry> <weep> <sob> <scream> <shout> <whisper> <sigh> <gasp> <groan> <moan> <pause> <hes> <stutter> <breath> <sniff> <cough> <throat_clear>

Research & Application Use Cases

With ~19 hours of single-speaker Sudanese Arabic speech and granular non-verbal tagging, the dataset is particularly suitable for:

• —Sudanese Arabic TTS and voice cloning: A consistent single-speaker voice for dialect-aware, expressive synthesis.
• —Speaker adaptation: Fine-tuning existing Arabic ASR/TTS models to a Sudanese speaker with limited data.
• —ASR fine-tuning and evaluation for Sudanese Arabic.
• —Foundation model fine-tuning.
• —Research on Sudanese Arabic speech, narration styles, and paralinguistics.

Dataset Statistics

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