350 hours of Saudi full-duplex customer-service speech
Saudi customer-service audio for models handling two-way speech. Task annotations connect recordings to customer-service behavior, with commercial AI-use rights included.
What this dataset is for
For voice-agent teams working on customer-service interaction and evaluating the labels needed for turn-taking, corrections, and task completion.
Commercial AI-use rights included; permitted uses are specified in the agreement.
What’s included
- 350 hours of Saudi full-duplex customer-service speech
- Task annotation
- Commercial AI-use rights
Full specifications & collection details
- Count
- 350 hours
- Language / dialect
- Saudi Arabic; request variety breakdown
- Speakers
- Count and composition available on request
- Collection year
- Available on request
- Training task
- Full-duplex voice interaction and customer-service task modeling
- Source
- Full-duplex customer-service speech; interaction source and channel details available on request.
- License
- Commercial AI-use rights included; permitted uses are specified in the agreement.
What to check in your sample
- Speaker mix, collection dates, and recording method
- Channels, overlap, interruptions, and available task labels
- Benchmark report: benchmark name, version, evaluated model, metric, and score
We’ll send the available sample and documentation so your team can check the fit before licensing.
Benchmark report
Ask for the benchmark report for this collection: benchmark name and version, evaluated model, metric, and score. The report can help your team judge whether the data fits your task.
Ask for the benchmark reportCommon questions
Does the dataset include overlap and interruption labels?
Task annotation is included, but the exact coverage of overlap, interruptions, channels, and other labels needs to be checked in the product specification. Request a sample that shows the fields your model needs.
Can we review a benchmark report for this collection?
Yes, on request. Ask for the report with the benchmark name, version, evaluated model, metric, and score. That context helps your team judge how closely the test matches the customer-service interactions you need to handle.