OpenAI's Whisper is a powerful speech-to-text AI model that utilizes state-of-the-art deep learning techniques to transcribe natural human speech into written text with high accuracy. Whisper differs from other speech recognition models in that it can accurately transcribe quietly spoken words, making it perfect for use in scenarios where being vocal is not an option. The model was trained on a large dataset of spoken language to enable it to recognize and transcribe speech patterns with a high degree of accuracy.

Whisper also employs context-aware attention mechanisms that take into account the surrounding spoken language when transcribing to ensure that sentences are accurately transcribed and punctuated. The model is currently being used in various applications, including transcribing podcasts, voice notes, and meetings. As technology continues to advance, we can expect even better AI models like Whisper to be developed that will continue to revolutionize language processing.


    AI Transcription
    AI Translation
    Speech-to-Text API
    Cloud Services
    Open source versions for self-hosting with versions such as tiny, base, small, medium, large, large-v2

Fune Tuning / Tips:

  • File: provide your audio file to Whisper AI
  • Model: this parameter is required and the default is whisper-1 at the moment
  • Prompt: This is a favorite parameter as it makes so much difference in the quality of AI output if you actually provide a brief prompt. It can summarize the audio or you can also give creative prompts to alter the results, works like a charm!
  • response_format: the text provided by Whisper AI can be in multiple formats such as json, srt or even vtt
  • temperature: fine-tune speech-to-text with Whisper using determinism level with temperature parameter
  • language: In some cases it will be very helpful for the AI model if you disclose the language already

Whisper Pros:

  • Supports almost all common languages of the world
  • Significantly cheaper than competition
  • Extreme accuracy even with challenging audio recordings
  • Very economical per minute transcription and/or translation
  • Smooth API experience
  • Well-documented developer files
  • Prompting speech-to-text AI is a fantastic fine tuning opportunity.
  • Can be synergized with ChatGPT models from OpenAI
  • Generous free credits for AI development experiments
  • Open source state-of-art model
  • Various versions for lighter applications
  • Cloud native speech-to-text solution via OpenAI API

Whisper Cons:

  • It doesn't provide speaker identification like some alternatives
  • Whisper API can be too complicated for non-develoeprs
  • Not too many customization features.
  • Lack of AI and integration makes it a power tool for developers but businesses might prefer more finished apps built on Whisper.
  • Not included in Azure Cognitive Services
  • Trustworthy privacy practices by OpenAI so your audio and text will be safe and secure

Whisper Price:

App pricing information for Whisper is as below:

Price: $0.006 per audio minute


Whisper's accuracy is amazing even with noisy audios
- Research Associate
I wish Whisper had speaker identification as working with dialogue type audio become troublesome.
- News Reporter

More Details:

OpenAI's Whisper speech-to-text AI model is a highly versatile tool that has many potential applications across various industries. In the business world, for example, Whisper could be used to transcribe meetings, enabling employees to focus on the content being discussed, rather than taking notes.
Similarly, in the education sector, this technology could be used to record lectures and generate automated transcripts, making learning more accessible to students who may struggle with note-taking.
Whisper could also be used as a translation tool, providing almost real-time speech-to-text conversion in a wide range of languages, which would be especially useful in tourism and foreign language services.
Additionally, businesses and content creators could use Whisper to generate written content from verbal discussions or presentations, saving time and effort.
Overall, OpenAI's Whisper speech-to-text AI model has immense potential to revolutionize the way we communicate across different sectors, making it an essential tool for today's world.
One of the primary areas where the model can be implemented is in customer support. The AI model can detect and transcribe customers' spoken queries, allowing for speedy resolution of issues.
Sales automation is another area where this technology can be wielded to great effect. The speech-to-text capability of the model can enable sales representatives to quickly take note of client specifications and requirements, allowing them to provide a more personalized service.
Documentation is yet another area where this AI technology can be leveraged. The model can accurately transcribe speech in real-time, greatly reducing the time and effort required to produce written records of meetings and other essential interactions.


Q: How much is OpenAI Whisper API?
A: The API usage pricing is $0.36/hour of audio. The large-v2 model is priced at $0.006/minute. Alternatively there are open source versions for self-hosted AI implementations.
Q: Where can I use OpenAI Whisper API?
A: You can use OpenAI Whisper API to transcribe audio into text or translate audio into English. You can access the API through OpenAI's developer platform.
Q: Which is the best speech-to-text AI app?
A: This is a subjective question and may depend on your preferences and needs. However, some possible candidates are OpenAI Whisper, Google Cloud Speech-to-Text, Amazon Transcribe, IBM Watson Speech to Text, and Microsoft Azure Speech Services.
Q: Who created OpenAI’s Whisper?
A: OpenAI’s Whisper is an open source speech-to-text model that was created by a team of researchers at OpenAI.
Q: Can I connect Whisper to ChatGPT?
A: Yes, you can connect Whisper to ChatGPT, which is another API from OpenAI that allows you to have natural conversations with an AI agent..
Q: How can we make our world a better place using AI like Whisper?
A: Whisper can make speech more accessible and understandable across languages and domains. It can also help people with disabilities, such as hearing impairment or dyslexia, to communicate and learn more easily.
Q: How can I use Whisper?
A: You can use Whisper to transcribe and translate audio files or live speech using a command-line interface or a web app. You can also use Whisper API with Python to integrate it with your own applications.
Q: Which companies use Whisper in their services?
A: Some examples of companies that use Whisper in their services are Narrativa, a natural language generation company that uses Whisper to create captions and summaries for videos5, and Bytexd, a tech blog that uses Whisper to transcribe and translate podcasts3.
Q: What are the best alternatives to Whisper?
A: Some possible alternatives to Whisper are Google Cloud Speech-to-Text, Amazon Transcribe, Microsoft Azure Speech Services, IBM Watson Speech to Text, and Facebook Wav2Vec 2.0. However, these alternatives may not have the same level of robustness, multilingualism, and multitasking as Whisper.
Q: How can I use Whisper API with Python?
A: You can use Whisper API with Python by installing the whisper-client package from PyPI and following the documentation on GitHub4. You will need an API key from OpenAI to use the service.

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