Customer Service Persian Diarization Dataset with 80 Hours of Synthetic Speech

The customer_service_persian_diarization_dataset is a synthetic multi-speaker speech dataset designed for training and evaluating speaker diarization models in Persian (Farsi). It contains approximately 80 hours of audio, built using utterances from a customer service dataset and processed through a synthesis framework to simulate realistic conversational dynamics. The dataset was created by atiyehghm and was last updated on the platform in February 2026.

Use Cases

• Training speaker diarization models based on multi-speaker Persian audio.
• Evaluating the performance of speech processing systems on synthetic conversational data.
• Developing and benchmarking models for customer service call analysis in Persian.
• Studying the characteristics of synthetic speech for simulating realistic dialogue dynamics.

Strengths

• Approximately 80 hours of total audio duration provides a substantial resource for model training.
• Synthetic generation framework likely allows for controlled simulation of conversational dynamics.

Limitations

• Dataset is synthetic, which may not fully capture the acoustic and conversational nuances of real-world recordings.
• Column-level documentation is absent; field semantics must be inferred after download.
• The description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

huggingface user atiyehghm

Collection Method

Synthesized from utterances in a customer service dataset using a processing framework.

Freshness

Last updated 2026-02-18 08:28:21; freshness should be verified.