EGYSpeak

A curated dataset of 147,979 single-speaker Egyptian Arabic (pure dialect) audio clips with transcriptions, sourced from the fadisarwat/egyptian-arabic-lines Kaggle dataset and processed through a rigorous ASR pipeline.

Quick Start

1. Download the dataset:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="MohamedGomaa30/EGYSpeak",
    repo_type="dataset",
    local_dir="EGYSpeak",
)

2. Extract the dataset:

from EGYSpeak.egyspeak_loader import load_egyspeak
ds = load_egyspeak("EGYSpeak", streaming=True) 

Dataset Details

• Total samples: 147,979
• Audio format: WAV (PCM_16, 16 kHz, mono)
• Transcription language: Egyptian Arabic
• Metadata delimiter: Pipe (|)
• License: CC-BY-4.0

Dataset Structure

EGYSpeak/
├── README.md
├── metadata.csv                  # file_name|transcription (pipe-delimited, full dataset)
├── metadata.jsonl                # HF AudioFolder auto-loading format
└── train/
    ├── metadata.jsonl            # HF AudioFolder auto-loading format
    └── audio_shard_XXXX.tar      # 23 tar shards (~1GB each, 147,979 WAV files total)

Audio files are packed into 23 tar shards (~1GB each) to stay within HuggingFace Hub rate limits. Each shard contains WAV files under a wavs/ subdirectory. Use the provided egyspeak_loader.py to stream audio directly from the tars without extracting (saves ~22GB disk space).

metadata.csv Format

Column	Description	Example
file_name	Path to WAV file	train/wavs/D6x81u_1.wav
transcription	Arabic transcription (Egyptian dialect)	خليني ابدا الفيديو ده

Delimiter: pipe (|)

metadata.jsonl Format

Each line is a JSON object with file_name and transcription fields, compatible with HuggingFace AudioFolder auto-loading.

Data Preprocessing Pipeline

The raw Kaggle dataset (455,794 index entries, 408,059 MP3 files) was processed through the following pipeline:

Step 1: Duration Filter

• Reject audio shorter than 0.8s or longer than 30s

Step 2: Audio Processing

• Resample to 16 kHz mono
• Loudness normalization to -16 LUFS (using pyloudnorm)
• Save as PCM_16 WAV
• Create padded version (0.5s silence on each side for ASR)
• Reject completely silent files

Step 3: Speaker Diarization

• Model: pyannote/speaker-diarization-3.1
• Accept only single-speaker audio; reject multi-speaker clips

Step 4: ASR Transcription

• Model: NAMAA-Space/EgypTalk-ASR-v2 (FastConformer Hybrid Large, fine-tuned on 200+ hours of Egyptian Arabic)
• Decoder: Beam search (beam_size=5)
• Batch processing: 64 files per batch

Step 5: Quality Filters

Five sequential quality filters are applied to transcriptions:

#	Filter	Criterion
1	Empty transcription	Transcription is empty
2	Unknown symbols	Contains special unknown characters or ??
3	One-word only	Transcription has 1 word or less
4	Short transcription	ASR word count less than original Kaggle text count
5	Non-Egyptian dialect	Contains MSA-exclusive words (MSA + mixed dialect removed)

Configuration

Parameter	Value
Min duration	0.8 s
Max duration	30 s
Sample rate	16,000 Hz
Target loudness	-16 LUFS
Silence padding	0.5 s (each side, transcription only)
Transcription batch	64
Beam search size	5
ASR model	EgypTalk-ASR-v2
Diarization model	pyannote/speaker-diarization-3.1
Audio format	WAV, PCM_16, 16kHz, mono

Limitations

• Transcriptions are machine-generated (ASR) and may contain errors
• The original Kaggle text field was used as a reference only, not as ground truth
• Numbers appear as Arabic words, not numeric digits
• Some audio clips may contain overlapping speech fragments at boundaries

Source

Kaggle: fadisarwat/egyptian-arabic-lines

License

This dataset is released under the Creative Commons Attribution 4.0 license.