datahiveai/arabic-multidialect-emotional-speech-demo

DataHive AI — Demo: Arabic Multi-Dialect Emotional Speech

A DataHive AI dataset: a stratified 1-hour demo sample from a full corpus of 50+ hours. We can also create larger audio datasets upon client request.

Most public Arabic speech corpora flatten dialect into a single label and ignore emotion entirely. This corpus does the opposite: every recording is tagged with one of four regional Arabic dialects (Najdi, Hejazi, Jordanian, Moroccan) and one of four target emotions (Sad, Happy, Neutral, Angry), with two-reviewer QA on each utterance. The result is a dense, balanced grid useful for research and product work where regional voice authenticity and expressive prosody both matter.

Quick load

from datasets import load_dataset
ds = load_dataset("datahiveai/arabic-multidialect-emotional-speech-demo", split="train")
print(ds[0]["text"], ds[0]["dialect"], ds[0]["recording_style"], ds[0]["qa_tier"])
# Audio is decoded on access:
import soundfile as sf  # or any wav reader
sf.write("sample.wav", ds[0]["audio"]["array"], ds[0]["audio"]["sampling_rate"])

Contact

• Commercial licensing & enterprise inquiries: contact@datahive.ai
• Bug reports & data issues: open a Discussion on the HF dataset page

Use cases

• Expressive Arabic TTS — train or fine-tune a multi-speaker, multi-emotion voice model that handles Najdi / Hejazi / Jordanian / Moroccan natively instead of collapsing to MSA.
• Emotion-conditional ASR & call-center analytics — train models that not only transcribe but also tag utterances with speaker emotion (frustration detection, escalation triggers, sentiment dashboards).
• Dialect identification — build a 4-class classifier for routing, analytics, or upstream language-model conditioning in conversational AI.
• IVR / voice-agent localization — synthesize regional, emotionally appropriate voice replies for chatbots, banking IVR, healthcare prompts, and educational content targeted at specific Arabic-speaking markets.

At a glance

• Rows: 496
• Audio duration: 62.3 minutes (~1.04 h)
• Unique speakers: 24
• Audio: 337 MB · 48 kHz · 16-bit · mono · WAV
• Quality filter: qa_quality_tier ∈ {gold, silver}

Dialect × emotion grid

	Angry	Happy	Neutral	Sad	Total
Hejazi	31	31	31	31	124
Jordanian	31	31	31	31	124
Moroccan	31	31	31	31	124
Najdi	31	31	31	31	124
Total	124	124	124	124	496

Quality tier distribution

Tier	Count
gold	396
silver	100

Speaker gender

Gender	Count
female	210
male	286

QA scoring — what qa_* fields mean

Every recording in this demo is assessed by two independent reviewers on a 6-dimension rubric (qa_coverage = 2 for every row). We collapse the rubric into a small set of derived fields that you can filter on directly. All demo rows pass the gold / silver bar (qa_quality_score ≥ 0.75).

Per-dimension value mapping

Dimension	Mapping
Script Accuracy	perfect = 1.00 · acceptable = 0.70 · unusable = 0.00
Dialect / Accent Match	yes = 1.00 · no = 0.00
Speech Quality	excellent = 1.00 · good = 0.75 · poor = 0.25 · unusable = 0.00
Background Noise	none = 1.00 · quiet = 0.80 · loud = 0.20
Technical Quality	excellent = 1.00 · good = 0.75 · low = 0.40 · poor = 0.25 · unusable = 0.00
Emotion match	reviewer's perceived emotion == target recording_style → 1.00 else 0.00

Weights (per-reviewer score)

script_accuracy 0.20 · dialect_match 0.20 · speech_quality 0.20 · background_noise 0.15 · technical_quality 0.15 · emotion_match 0.10. Blank dimensions are dropped and the remaining weights renormalize.

The final qa_quality_score is the mean of the available reviewers' scores.

Tier thresholds (on qa_quality_score)

Tier	Range	In this demo
gold	≥ 0.90	yes
silver	0.75 – 0.90	yes
bronze	0.50 – 0.75	excluded
below_threshold	< 0.50	excluded
unreviewed	no QA done	excluded

Per-row QA fields

Field	Meaning
qa_coverage	Number of reviewers.
qa_quality_score	0–1 weighted score.
qa_quality_tier	gold or silver in this demo.
qa_emotion_match	true iff target emotion == reviewer's perception.
qa_inter_reviewer_agreement	Per-row agreement fraction between the two reviewers.
qa_min_dimension	The lowest-scoring dimension on this row — useful for filtering.

Consent & provenance

Recordings were collected through DataHive AI's recording pipeline. Every contributor in this corpus gave explicit, informed consent for their voice to be recorded and used in the production of speech datasets.

All speaker identifiers in this dataset are opaque hashes — no real names, phone numbers, addresses, or other directly-identifying personal information is exposed.

License

This demo is licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC-4.0) (SPDX: CC-BY-NC-4.0).

Two requirements: (1) non-commercial use only, (2) attribution required.

What counts as non-commercial?

CC-BY-NC defines non-commercial as use "not primarily intended for or directed toward commercial advantage or monetary compensation." In practice for this demo:

Allowed (non-commercial):

• Academic research, theses, papers, open peer-reviewed publications
• Educational use — university courses, tutorials, lectures, MOOCs that don't charge for access to materials trained on the demo
• Personal experiments, hobby projects, weekend hacks
• Public-good and non-profit work (NGO, government open initiatives, accessibility / assistive tools provided free of charge)
• Open-source ML benchmarks and research code, provided the project itself is non-commercial and free of charge
• Internal R&D / pre-product experimentation at any organization, as long as no model trained on this demo ships in a revenue-generating product

Not allowed (commercial):

• Training a model that is sold, licensed, or served via a paid API or paid SaaS, even if the training step itself is internal
• Powering a feature inside a paid product (free tier of a paid SaaS counts as commercial — the "free tier" is a marketing channel for the paid one)
• Use in a freemium app where the free version drives users to a paid plan
• Inclusion in a paid course, textbook, conference workshop with a paid ticket, or any monetized media
• Training a model used in advertising, marketing automation, lead generation, or any revenue-attribution funnel
• Internal use at a for-profit company in any flow that ultimately contributes to a paid offering

If your use case sits in the gray zone (e.g., open-source project funded by a commercial sponsor, free tier of a paid SaaS, ad-supported free app), the default answer is commercial — get the commercial license.

Required attribution

In any non-commercial use of this demo, the following credit must be visible to anyone who could reasonably be expected to encounter the work that uses it. Concretely:

Where you use it	Where the credit must appear
Research paper / thesis	Citation in the references (use the BibTeX below)
Model card on Hugging Face / GitHub	"Training data" section + datasets: metadata field
Blog post / article / presentation slide	Footer, credit line, or attribution slide
Open-source code repository	README.md and / or NOTICE / LICENSE-NOTICE.md
Notebook shared publicly	Markdown cell at the top

Plain-text credit line (for any of the above):

Arabic Multi-Dialect Emotional Speech (Demo) — DataHive AI, 2026. Licensed under CC-BY-NC-4.0. https://datahive.ai

BibTeX (for academic citation):

@dataset{datahive_arabic_emotional_demo_2026,
  title     = {Arabic Multi-Dialect Emotional Speech (Demo)},
  author    = {DataHive AI},
  year      = {2026},
  note      = {1-hour stratified demo, 4 dialects × 4 emotions},
  license   = {CC-BY-NC-4.0},
  url       = {https://datahive.ai}
}

Removing or obscuring the credit, or replacing it with a generic "data provider" placeholder, is a license violation.

Want to use it commercially?

The full corpus is available under a custom commercial license that allows training models for paid products, redistribution under your own terms, and voice-cloning carve-outs. Contact your DataHive AI representative.

What this demo is for

• Verifying audio quality, dialect authenticity, and emotion conveyance on real (not cherry-picked) samples.
• Smoke-testing your loading / training pipeline against the production schema.
• Internal evaluation prior to a full-corpus license from DataHive AI.

What this demo is NOT

• Not a free-tier release. Use is restricted to internal evaluation under whatever evaluation terms accompanied this folder.
• Not a representative sample of the unreviewed partition (this demo is drawn entirely from reviewed rows).
• Not for redistribution.