---
dataset_info:
  features:
  - name: filename
    dtype: int64
  - name: text
    dtype: string
  - name: text_diacritized
    dtype: string
  - name: audio
    dtype: audio
  splits:
  - name: train
    num_bytes: 803886182.84
    num_examples: 1878
  download_size: 772436314
  dataset_size: 803886182.84
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- text-to-speech
- automatic-speech-recognition
tags:
- sudanese-arabic
- dialectal-arabic
- synthetic-audio
- resemble-ai
- diacritized
- social-media
language:
- ar
- su
---
- Lisan Sudanese TTS Dataset
  - A synthetic Text-to-Speech (TTS) and Automatic Speech Recognition
    (ASR) dataset specifically for Sudanese Arabic.
  - 1,878 high-quality sentences featuring 20 synthetic speakers (10
    male, 10 female).
  - Reconstructed from the Lisan-Sudanese Morphological Dataset
    (52K manually annotated social media tokens from Facebook/X).
  - Only sentences with a diacritic density of >=25% were kept to ensure
    enough phonetic information for accurate synthesis.
- model:
   Resemble AI Chatterbox Multilingual [ResembleAI/Chatterbox-Multilingual-TTS](https://huggingface.co/ResembleAI/chatterbox)
  - Accessed via the Resemble AI Python SDK using an API key,
    selecting a target voice ID, and passing highly diacritized text payloads to
    generate .wav files.
  - observed_errors:
    - MSA and Egyptian dialect Bias(Phonetic Mismatches)
      description: The model defaults to Modern Standard Arabic (MSA) pronunciations, and in several actuations to Egyptian dialect 
        struggling with distinct Sudanese letter realizations (e.g., Qaf or
        Jeem).
    - type: Prosody Issues
       The model fails to capture the natural rhythm and intonation of
        informal Sudanese social media slang and code-switched phrases.
- fine_tuning_recommendations:
  - proposed_dataset: A High-Fidelity Human TTS Corpus featuring phonetically
    balanced scripts that highlight unique Sudanese phonemes and slang.
  - assembly_method:
    Hire 2 to 4 professional, native Sudanese voice actors to read
    scripts (derived from the Lisan dataset)  in a noise-free, acoustically
    treated studio. The text and audio must then be perfectly aligned using
    forced alignment tools.
  - size_requirements:
    minimum: 2 to 5 hours of ultra-clean, single-speaker audio to create a highly
      accurate custom voice clone.
    robust: 15 to 25 hours across multiple speakers to robustly fine-tune a
      foundation model's base understanding of the Sudanese dialect.

---