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Whisper: An Open-Source Automatic Speech Recognition System

Whisper is an open-source automatic speech recognition (ASR) system developed by OpenAI. Built on a sequence-to-sequence Transformer architecture, it converts spoken audio into written text.

Technology: Transformer ASR
Developer: OpenAI
License: MIT License

What Is the Whisper Model?

Whisper is an automatic speech recognition system released by OpenAI. The original research describes training on 680,000 hours of multilingual and multitask weak supervision. Konthora uses only the English-focused small.en variant for its current transcription product.

OpenAI releases Whisper's code and model weights under the MIT License. This upstream license does not make Konthora affiliated with OpenAI, and it does not expand the capabilities exposed by Konthora's product.

How Whisper Processes Audio

Whisper employs an encoder-decoder Transformer architecture. When an audio file is provided, it is first converted into a log-Mel spectrogram, which is a visual representation of the audio frequencies over time.

The Transformer encoder processes this spectrogram to understand the acoustic features, while the decoder generates the corresponding text transcript. During this process, the model can also predict timestamps, allowing the text to be aligned with the precise moment it was spoken.

Whisper Implementation in Konthora

Konthora utilizes the Whisper model to power its free, browser-based transcription tool. Specifically, Konthora implements the small.en variant of the Whisper model for English-language audio.

The Konthora implementation introduces specific guardrails and processing features to support practical user workflows:

Supported Formats

  • Audio: MP3, WAV, M4A, AAC
  • Video: MP4, WebM, MOV
  • Export: TXT, SRT, VTT, JSON

Processing Constraints

  • 10-minute maximum duration
  • 100 MB maximum file size
  • English language only

Privacy and Data Lifecycle

Because the Whisper model is open-source, Konthora hosts the inference engine independently. Audio files are not transmitted to OpenAI for processing.

Within the Konthora infrastructure, all uploaded media and generated transcripts are subject to a strict 60-minute automatic deletion policy. The tool does not require user accounts, ensuring that transcriptions remain untethered to persistent user identities.

Using Whisper for Captions

Konthora groups timing data into word, sentence, or paragraph modes and can export timed SRT or VTT transcripts. These exports are a starting point for captions; review and add speaker labels or non-speech cues when fully authored closed captions are required.

Try the Audio-to-Text Tool

Sources

Frequently Asked Questions

Who developed the Whisper model?
The Whisper automatic speech recognition model was developed and open-sourced by OpenAI.
What architecture does Whisper use?
Whisper uses a Transformer sequence-to-sequence architecture, commonly found in modern large language models, to map audio spectrograms directly to text.
Which version of Whisper does Konthora use?
Konthora integrates the Whisper small.en model for its browser-based audio-to-text tool, balancing processing efficiency with accurate English transcription.
Is Konthora affiliated with OpenAI?
No. Konthora is an independent platform that utilizes the open-source MIT-licensed Whisper model. It is not affiliated with or endorsed by OpenAI.
What are the audio length limits in Konthora's Whisper implementation?
Konthora currently accepts audio or video files up to 10 minutes in duration and up to 100 MB in file size for transcription.