Our Open-Source Process for Speech-to-Text
and Searchable Audio Archive

Discover how we create accurate and searchable audio transcripts with our community-driven approach

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Community-driven Voice Corpus Training

We leverage the power of our community volunteers to collect and label audio data using the Mozilla Common Voice project. This allows us to train our AI models with a diverse set of voices and accents for optimal performance.

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Efficient Speech-to-Text Pipeline

We use state-of-the-art speech recognition algorithms to convert audio files to text, including support for multiple languages and dialects. Our pipeline is optimized for speed and accuracy, ensuring high-quality transcripts every time.

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Transcript Indexing and Public Access

We index all transcripts for efficient search and retrieval, and make them publicly accessible for easy sharing and discovery. This also enables search engines to index the content, expanding the reach of your audio content.

Listen and learn about the future of audio transcription!

Demystifying Speech-to-Text: A Deep Dive into Our AI Algorithm

In this audio report, two of our experts explain the intricacies of our AI algorithm, and how it generates accurate and efficient speech-to-text transcripts for audio content.

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Who We Are?

Radioship has been designed and built by a diverse team including journalitsts, machine learning, natural language process experts, data scientists and service designers.

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József Venczeli

data scientist

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Orsolya Putz

data engineer

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Gergely Gazda

product engineer

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Edit Köles

project manager

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Helga Fázold

journalist

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Zoltán Varjú

NLP specialist

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Emese Stork

service designer

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József Venczeli

data scientist

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Orsolya Putz

data engineer

Agon

Gergely Gazda

product engineer

Agon

Edit Köles

project manager

Agon

Helga Fázold

journalist

Agon

Zoltán Varjú

NLP specialist

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Emese Stork

service designer

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József Venczeli

data scientist