Whisper (OpenAI)

Whisper is an automatic speech recognition model released by OpenAI in September 2022. It turns spoken audio into text, can translate speech in other languages into English, and copes well with accents, background noise and punctuation. It is available in two ways: as free, open-source model weights you can run yourself, and as the whisper-1 model in the OpenAI API.

How it works

Whisper is a Transformer (Architecture) encoder-decoder model. Audio is cut into 30-second windows and converted into a spectrogram (a visual representation of sound frequencies); the encoder reads it and the decoder writes out the text token by token. OpenAI trained it on 680,000 hours of multilingual and multitask audio collected from the web, which is why it handles mixed languages and noisy recordings better than many earlier systems.

Through the API there are two audio endpoints: transcriptions (text in the original language) and translations (into English). Outputs can be plain text, JSON or subtitle formats such as SRT and VTT with timestamps. Uploaded files are limited to 25 MB, so longer recordings need splitting or compressing. Since 2025 OpenAI also offers newer GPT-4o-based transcription models alongside whisper-1.

How it’s used in SEO and marketing

• Transcribing webinars, podcasts and interviews to repurpose into articles, newsletters and social posts.
• Creating accurate captions and transcripts for YouTube, which help viewers and give search systems text to understand the video.
• Turning customer calls or research interviews into text for entity and sentiment analysis.
• Building searchable archives of video and audio content.

Example

In How to transcribe audio with OpenAI’s Whisper API in Google Colab (Python), you install the OpenAI library in Google Colab, add your API key, upload an audio file and send it to the transcription endpoint. The notebook writes the transcript to a text file you can download, and the same loop can process a whole folder of recordings. The YouTube SEO guides then use transcripts as a source for descriptions, chapters and on-page copy.

Limitations and things to watch

• Whisper can “hallucinate” words during silence or very noisy passages. Check transcripts before publishing quotes.
• Accuracy varies by language; it is strongest in widely spoken languages with lots of training data.
• Speaker labels (who said what) are not part of the original model, so you may need a separate diarisation step.
• API pricing is per minute of audio. Running the open-source model locally avoids that cost but needs a capable machine or GPU.
• Consider consent and privacy before transcribing calls or meetings.

Learn it on MLforSEO

• How to transcribe audio with OpenAI’s Whisper API in Google Colab (Python)
• YouTube SEO: How to boost video visibility and grow your channel (free checklist)
• How to do a YouTube SEO Audit for improved organic search visibility (step-by-step guide)
• Semantic ML-enabled Keyword Research