Whisper Review 2024: What It Is, How to Use It & Is It Worth It?

Multilingual speech recognition, speech translation, and language identification.

Whisper logo

Multitasking model

Transformer sequence-to-sequence approach

Supports a wide range of languages

Whisper Description

Whisper is a versatile speech recognition model that's been trained on a wide variety of audio data. It's a multitasking model, capable of performing multilingual speech recognition, speech translation, and language identification. The model is based on a Transformer sequence-to-sequence approach, trained on various speech processing tasks. These tasks are represented as a sequence of tokens to be predicted by the decoder, allowing a single model to replace many stages of a traditional speech-processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets. Whisper is developed by the AI powerhouse OpenAI. The code and model weights are released under the MIT License. The codebase relies on a few Python packages, most notably OpenAI's tiktoken for their fast tokenizer implementation. You can download and install the latest release of Whisper using pip, or use it through an API.

Starting price


  • Free plan
  • Paid
  • Free trial

Whisper Detailed Review

Whisper is a real workhorse in the realm of speech recognition. Its training on a massive 680,000 hours of multilingual and multitask data makes it a versatile tool for a variety of applications. Whether you're dealing with accents, background noise, or technical language, Whisper's got your back. It's also a polyglot, capable of transcribing in multiple languages and translating those languages into English. This makes it a handy tool for international businesses and multilingual environments.

The architecture of Whisper is straightforward, using an encoder-decoder Transformer approach. It takes audio input, splits it into manageable 30-second chunks, and converts it into a log-Mel spectrogram. This is then passed into an encoder, and a decoder is trained to predict the corresponding text caption. The model also uses special tokens to perform tasks like language identification and multilingual speech transcription. This means you're not just getting a transcription tool, but a multi-purpose AI that can handle a variety of speech processing tasks.

However, Whisper isn't without its limitations. While it's been trained on a diverse dataset, it hasn't been fine-tuned to any specific one. This means it doesn't outperform models that specialize in certain tasks, like LibriSpeech performance, a benchmark in speech recognition. But when it comes to zero-shot performance across diverse datasets, Whisper shines, making 50% fewer errors than other models. So, if you're looking for a jack-of-all-trades, Whisper might be your best bet.

One of the standout features of Whisper is its ability to handle non-English audio. About a third of its audio dataset is non-English, and it can transcribe in the original language or translate to English. It's particularly effective at learning speech to text translation, even outperforming the supervised state-of-the-art on CoVoST2 to English translation zero-shot.

OpenAI has made Whisper open-source, meaning developers can take this tool and build upon it, potentially creating even more powerful applications. This is a big plus for the tech community, as it allows for further research and development in the field of robust speech processing.

If you want to use the OpenAI Whisper API, the pricing is usage-based. You pay $0.006 per minute, rounded to the nearest second. This could be a pro or a con, depending on your usage. For occasional users, it might be cost-effective, but for heavy users, the costs could add up. It's worth doing the math to see if it's a good fit for your budget.

In summary, Whisper is a robust and versatile speech recognition tool. Its ability to handle multiple languages and tasks makes it a valuable asset for a wide range of applications. While it may not outperform specialized models in certain areas, its overall performance and versatility make it a strong contender in the field of speech recognition. Just keep an eye on your usage to ensure it's cost-effective for your needs.