Mohaddz/sada-diarization-preview

SADA 2022 Arabic Diarization

Training-ready speaker-attributed ASR windows derived from SADA 2022. The source recordings are mirrored at khaledalganem/sada2022.

Splits

• train: 36,004 windows, 202.064 hours, 4,062 recordings
• validation: 853 windows, 4.774 hours, 88 recordings
• test: 901 windows, 5.006 hours, 111 recordings

Total: 37,758 windows and 211.844 hours.

The official SADA train, validation, and test partitions are preserved. Windows are 8–28 seconds, contain 1–4 locally remapped speakers, and have at least 45% annotated speech. Windows from the same source recording do not materially overlap. Ambiguous speaker labels and overlapping speech annotations are excluded from v1.

Audio is resampled to 16 kHz mono FLAC. Background noise and music are retained; no denoising is applied.

Columns

• audio: prepared FLAC window
• segments: speaker, timestamps, transcript, dialect, gender, age, environment
• target: SyvAI-style speaker/timestamp/text sequence
• source_file, source_start, source_end: source provenance
• speaker_count, turn_count, speech_coverage, environment
• audio_sha256, qa_flags, review_status, license

Target format

<|spltoken0|><|t:0.0|>السلام عليكم<|t:2.1|>

Speaker IDs are local to each window and ordered by first appearance. Timestamp tokens use 100 ms resolution.

License and attribution

SADA was created by the Saudi Data and Artificial Intelligence Authority (SDAIA) and the Saudi Broadcasting Authority (SBA). This derivative is released under CC BY-NC-SA 4.0 and is restricted to non-commercial use.

Changes made: selected timestamped regions, excluded unsupported annotations, remapped speaker IDs locally, resampled audio, encoded FLAC, and generated speaker-attributed sequence targets.