Tamil Podcast Audio Dataset with 3,315 Hours of Processed Speech

A 3,315-hour collection of processed Tamil podcast audio recordings, part of a larger multilingual corpus of 57,568 hours across 12 languages. Created by InfoBayAI and last updated in June 2026, the dataset is designed to support the development of speech and conversational AI systems. It captures real-world interactions across diverse topics and formats.

Use Cases

• Training automatic speech recognition (ASR) models based on natural Tamil speech patterns.
• Developing text-to-speech (TTS) systems based on speaker variability and authentic podcast environments.
• Building multilingual speech models based on the dataset's inclusion of 12 languages.
• Researching conversational AI and dialogue systems based on real-world podcast interactions.

Strengths

• Large-scale collection of 3,315 hours of processed Tamil audio.
• Part of a broader multilingual corpus totaling 57,568 hours across 12 languages.
• Designed to capture natural speech patterns, speaker variability, and authentic podcast environments.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.
• Specific file formats, licensing information, and exact row counts are unknown.

Provenance

Source

InfoBayAI via Hugging Face.

Collection Method

Collection and processing of podcast audio recordings.

Freshness

Last updated 2026-06-08 04:25:12; freshness should be verified.