Sign inGet started
Dataset · Hugging Face

kiarashQ/farsi-asr-unified-cleaned

🎧 Farsi ASR Unified Dataset (Parquet Sharded Edition) Overview The Farsi ASR Unified Dataset is a large-scale, high-quality, and fully standardized collection of Persian (Farsi) speech-to-text data — designed specifically for modern machine learning and ASR (Automatic Speech Recognition) workflows. This dataset consolidates audio–text pairs from multiple open sources, applies a rigorous cleaning and normalization pipeline, and stores everything efficiently in… See the full description on the dataset page: https://huggingface.co/datasets/kiarashQ/farsi-asr-unified-cleaned.

Author / organizationkiarashQ
Downloads1.1K (as reported by Hugging Face)
Likes6
Created12 October 2025
Last updated at source3 November 2025
audiospeech-recognitionfarsipersianasr
Embed this entry
<iframe src="https://silytics.si/embed/item/d9b1a40f-6454-4a54-b891-ba1f09fd4649" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Popularity, last 5 days

1,138 now (-8 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.

Recent changes

  • 17 Oct 2026 Description: 🎧 Farsi ASR Unified Dataset (Parquet Sharded Edition) Overview The Farsi ASR Unified Dataset is a large-scale, high-quality, and fully standardized collection of Persian (Farsi) speech-to-text data — designed specifically for modern machin → 🎧 Farsi ASR Unified Dataset (Parquet Sharded Edition) Overview The Farsi ASR Unified Dataset is a large-scale, high-quality, and fully standardized collection of Persian (Farsi) speech-to-text data — designed specifically for modern machin

Changes detected by Silytics between refreshes of the source.

Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 20 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.