Sign in to view source links and access this dataset
Description
InfoBayAI's Gujarati Podcast ASR Dataset is a large-scale collection of 2,471 hours of processed Gujarati podcast audio recordings. The broader collection contains 57,568 hours of processed audio across 12 languages, capturing real-world interactions across diverse topics and formats. The dataset was last updated on June 2, 2026.
Use Cases
Train automatic speech recognition (ASR) models based on the described large volume of Gujarati audio.
Develop conversational AI systems based on the real-world podcast interactions mentioned in the description.
Study natural speech patterns and speaker variability based on the authentic podcast environments described.
Build multilingual speech models based on the dataset's inclusion of 12 languages.
Strengths
Contains 2,471 hours of processed Gujarati podcast audio.
The broader collection includes 57,568 hours of audio across 12 languages.
Captures real-world interactions and natural speech patterns as described.
Limitations
Column-level documentation is absent; field semantics must be inferred after download.
Row count and specific file formats are unknown, which may limit suitability assessment.
Data may reflect geographic or topical bias inherent to the source podcast platforms.
Provenance
Source
InfoBayAI
Collection Method
Processed from podcast audio recordings, likely sourced from various platforms.
Freshness
Last updated 2026-06-02 06:25:47; freshness should be verified.
License is unknown; terms of use must be verified before application.