Arabic Dialect Dataset for AI and Language Models

What This Dataset Contains

Our Arabic dialect dataset blends audio, video, transcripts, and human-written context so machine learning teams can train and evaluate systems that need to handle spontaneous, code-switched, regionally diverse speech. We purposely collect moments that rarely appear in public corpora: family storytelling in Akkar, taxi banter in Beirut, spice shopping in Sanaa, and WhatsApp-style voice notes. Each bundle keeps the original dialect expression while providing standardized fields that are easy to parse.

Recordings include ambient noise, varying speaking speeds, and natural interruptions to help ASR models generalize. Where speakers switch between dialect and Modern Standard Arabic, we annotate the boundaries so you can tune code-switching models. We also include parallel text so retrieval-augmented LLMs can ground responses in dialectal nuance.

• High-quality audio tracks paired with 1080p video when available for multimodal fusion.
• Transcripts in dialectal Arabic, Modern Standard Arabic, English translations, and phonetic transliteration.
• Conversational scenarios ranging from informal chats to task-based instructions, interviews, and narration.
• Speaker diversity across Lebanon, Syria, Iraq, and Yemen with balanced age and gender representation.
• Code-switching markers so dialogue agents and ASR pipelines can manage mixed-language utterances.
• Context notes that capture gestures, gaze, and setting for multimodal modeling.

If you want a lighter overview first, you can also browse our Multimodal Data page, which lists the core ingredients we provide to every partner.

Technical Specifications

The dataset is packaged for training and evaluation without extra wrangling. Audio is delivered at standard ML-ready sample rates such as 16 kHz and 48 kHz, and files are normalized so you can slot them directly into speech pipelines. Video tracks stay synchronized with audio to support audio-visual speech recognition and gesture-aware assistants.

• Audio: WAV format, 16-bit PCM, recorded at standard sample rates (16 kHz primary; 48 kHz available for higher-fidelity needs).
• Video: MP4/H.264 with consistent frame rates and clear framing for lip-reading research.
• Text: JSON and CSV manifests referencing transcripts, timestamps, and per-utterance speaker IDs.
• Splits: train/validation/test partitions organized by region and scenario to prevent leakage.
• Delivery: secure cloud transfer with optional checksum manifests and folder-level README files.

We maintain consistent naming conventions so engineering teams can programmatically ingest new drops. If you need a custom split—like holding out Yemeni speech for cross-dialect evaluation—we can provide that without extra cleanup on your end.

Metadata and Annotations

Rich metadata is the backbone of our dataset. We publish structured JSON so you can filter by dialect, topic, acoustic conditions, and consent type. Each field is validated before delivery and kept consistent across batches. Here’s an illustrative snippet you might see in a package manifest:

{
  "recording_id": "YEM_SANA_014",
  "file_paths": {
    "audio_wav": "audio/YEM_SANA_014.wav",
    "video_mp4": "video/YEM_SANA_014.mp4",
    "transcripts": "transcripts/YEM_SANA_014.json"
  },
  "dialect": { "country": "Yemen", "region": "Sana'a" },
  "speakers": [
    { "age": 29, "gender": "Female", "role": "host" },
    { "age": 31, "gender": "Male", "role": "guest" }
  ],
  "recording_environment": { "location_type": "Indoor cafe", "background_noise": "espresso machine" },
  "conversation_context": {
    "topic": "Family recipes",
    "code_switching": true,
    "scripted": false,
    "sentiment": "warm"
  },
  "annotations": {
    "face_visible": true,
    "gesture_notes": "hands emphasize measurements",
    "emotion_tags": ["happy", "nostalgic"],
    "sampling_rate_hz": 16000,
    "audio_format": "wav",
    "transcript_formats": ["dialect_ar", "msa", "en"],
    "split": "train"
  },
  "consent": { "form_signed": true, "release_version": "v2" }
}

This schema is representative of what we provide to clients: enough context to run targeted evaluations, fine-tune LLM prompts, or filter training examples by acoustic environment. Additional fields—like diarization timestamps, overlap ratios, and background language hints—can be appended as needed.

Ethics, Consent, and Privacy

Dialect Data is built with contributor safety at its core. Every participant receives clear, plain-language explanations of how their recordings will be used in AI systems, and we collect signed consent that covers research and commercial training while prohibiting surveillance or harmful use. We avoid sensitive political or religious topics and strip personal identifiers before any data leaves the field team’s devices.

Where recordings include bystanders, we review and blur visuals when needed. Access is controlled and logged, and we can provide data processing addendums for enterprise customers. Ethical sourcing is not a marketing line for us—it is the workflow that makes the dataset safe to deploy.

How to Request Access

Ready to evaluate or license the Arabic dialect dataset? Email us at team@dialectdata.com or send details through our Contact page. We typically respond within a few business days.

When you reach out, it helps to include:

• Your intended use case (ASR training, conversational agents, retrieval-augmented generation, academic research).
• Target dialects or regions plus the volume of hours you need.
• Preferred formats or integration needs (file structure, manifests, security requirements).
• Whether you need samples for pilots or a full production license.

If you are a creator who wants to contribute new recordings, visit our For Creators page to see how we partner and compensate contributors.

Additional Notes for AI Teams

We regularly collaborate with research labs and product teams to design evaluation sets that stress-test ASR, diarization, and NLU pipelines. That can include accented speech recognition for Levantine Arabic, entity extraction on noisy audio, grounding for conversational agents, or training safety filters that understand cultural nuance. We also provide small gold-standard subsets with human QA for benchmarking LLM outputs.

By keeping a consistent schema and providing multilingual transcripts, we help teams fine-tune both encoder-decoder ASR models and decoder-only language models with minimal preprocessing. If you are experimenting with cross-modal embeddings, we can supply synchronized video clips and gesture annotations to support multimodal fusion experiments.

Finally, we document everything: data provenance, collection methods, annotation guidelines, and known limitations. That transparency makes it easier to ship AI features responsibly while respecting the communities whose voices power them.