Sold by: InfoBay AI Ltd.
Deployed on AWS
Multilingual podcast audio dataset for ASR, Speech Recognition, Speech-to-Text, Voice AI, Conversational AI, NLP, Generative AI, and LLM training workflows.
Overview
Multilingual Podcast Audio Dataset (Single & Dual Channel)
Overview
This dataset is a large-scale multilingual podcast audio corpus designed for training and evaluating Automatic Speech Recognition (ASR), Speech-to-Text (STT), Speech AI, Voice AI, Conversational AI, Natural Language Processing (NLP), Generative AI, and Large Language Models (LLMs).
The corpus contains over 57,000 hours of podcast audio collected from diverse podcast formats, speakers, topics, and conversational styles. The dataset includes both single-channel and dual-channel recordings, enabling a wide range of speech processing, speaker modeling, transcription, and conversational AI applications.
The audio captures authentic human speech with natural accents, speaking styles, conversational dynamics, pauses, interruptions, emotional variation, and real-world recording conditions, making it suitable for enterprise AI development and research.
Key Use Cases
- Automatic Speech Recognition (ASR)
- Speech-to-Text (STT)
- Conversational AI and Voice AI
- Podcast transcription systems
- Large Language Model (LLM) training
- Supervised Fine-Tuning (SFT)
- Retrieval-Augmented Generation (RAG)
- Speaker diarization and speaker identification
- Sentiment and intent analysis
- Audio understanding and speech analytics
- AI assistants and virtual agents
Dataset Features
- 57,000+ hours of podcast audio
- Multilingual speech content
- Single-channel and dual-channel recordings
- Real-world conversational speech
- Diverse speakers and accents
- Broad topical coverage
- Long-form audio content
- Suitable for AI training and evaluation workflows
- Foundation model and speech model development
Content Coverage
The dataset includes podcast content spanning a wide range of domains such as:
- Technology and Artificial Intelligence
- Business and Entrepreneurship
- Finance and Economics
- Healthcare and Medicine
- Education and Learning
- Science and Research
- News and Current Affairs
- Entertainment and Media
- Lifestyle and Culture
- General Knowledge
This diversity enables the development of domain-aware AI systems capable of understanding varied conversational contexts and specialized terminology.
AI Training Applications
The corpus is designed to support modern AI development workflows, including speech foundation model training, ASR development, transcription systems, conversational intelligence, NLP pipelines, multimodal AI systems, and next-generation Generative AI applications.
Organizations can utilize this dataset to develop speech recognition systems, voice assistants, intelligent search platforms, podcast analytics solutions, customer interaction systems, and multilingual AI applications.
Data Collection
The dataset consists of multilingual podcast audio collected and organized to support large-scale machine learning, speech processing, and artificial intelligence workflows. The corpus provides extensive linguistic, topical, and conversational diversity suitable for both research and commercial AI applications.
Licensing & Access
This listing contains sample data intended for research, evaluation, and educational purposes. Enterprise licensing and access to the full dataset are available upon request.
InfoBay AI
Email: taha.alvi@infobay.ai Phone: +91 9236354075
Highlights
- 57,000+ hours of multilingual podcast audio featuring diverse speakers, accents, topics, interviews, discussions, and real-world conversational speech.
- Includes single-channel and dual-channel recordings optimized for ASR, Speech Recognition, Speech-to-Text (STT), Voice AI, and Conversational AI applications.
- Designed for LLM training, Supervised Fine-Tuning (SFT), RAG, podcast transcription, speaker diarization, NLP, and Generative AI development workflows.
Details
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Delivery method
Deployed on AWS
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Pricing
This product is available free of charge. Free subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimensions summary
This listing uses a single pricing dimension: Product Access measured in Units. You subscribe at no cost to gain access to the podcast audio dataset. There are no tiers, instance sizes, or usage add-ons to compare. The one dimension grants access to the dataset, which covers podcast audio across 12 languages in single and dual channel formats. Because pricing is free, there is no scaling cost tied to volume or usage. You evaluate format, coverage, and suitability before any larger licensing discussion with the vendor.
Top-of-mind questions for buyers
One Unit grants subscriber access to the dataset at no cost. It is not a per-hour or per-file meter. Access lets you review the podcast audio across 12 languages in single and dual channel formats, so you can scope format, coverage, and fit before any larger licensing discussion.
No. Pricing is free under a single Product Access dimension, so cost does not scale with hours, languages, or channel type. There are no tiers, included-amount cutoffs, or overage charges tied to volume. Access covers the podcast audio across 12 languages in single and dual channel formats.
Dual channel audio keeps separate tracks for each speaker, which supports speaker diarization and transcription workflows. Single channel keeps all speech on one track. The dataset includes both formats across its 12 podcast languages, letting you match the format to your model training or evaluation needs.
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Delivery details
AWS Data Exchange (ADX)
AWS Data Exchange is a service that helps AWS easily share and manage data entitlements from other organizations at scale.
Additional details
You will receive access to the following data sets.
Data set name | Type | Historical revisions | Future revisions | Sensitive information | Data dictionaries | Data samples |
|---|---|---|---|---|---|---|
Podcast Audio Dataset for ASR & Speech AI | All historical revisions | All future revisions | Not included | Not included |
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