Whisper is an open-source model from OpenAI that uses automated speech recognition (ASR) to turn spoken audio into text. It can handle a wide range of dialects, languages, and background noise because it was trained on a huge dataset of 680,000 hours of multilingual and multimodal audio from the internet. It can translate speech from many languages into English in addition to transcribing audio.
How is it used?
Developers can use the OpenAI API or open-source equivalents found on sites like GitHub to incorporate Whisper into their own apps.
It can be applied to projects like creating voice-activated apps, creating subtitles for films, and transcribing meetings.
OpenAI Whisper is a cutting-edge Automatic Speech Recognition (ASR) technology that transcribes spoken words into written text using deep learning. This neural net, released in September 2022, has since become a famous tool in natural language processing, providing unprecedented accuracy and versatility and inspiring a plethora of open-source and commercial applications.
As a speech-to-text provider that has specialized in Whisper optimizations since its inception, we have put together a comprehensive introduction to the most frequently asked questions about Whisper ASR, including how it works, what it can be used for, key alternatives, and factors to consider when deploying the model for in-house projects.
Whisper: model or system?
OpenAI Whisper might be referred to as a model or a system, depending on the context.
At its core, Whisper is an AI/ML model, specifically an ASR one. The model includes neural network topologies for processing audio input and producing accurate transcriptions. More specifically, Whisper refers to a group of models spanning in size from 39 million to 1.55 billion parameters, with 'bigger' models providing higher accuracy at the tradeoff of longer processing times and higher computing costs.
In a larger sense, Whisper can be called a system because it includes not only the model architecture but also the complete infrastructure and activities that support it.
What can Whisper do?
Whisper's primary goal is to convert speech into text output. It can also translate speech from any of its supported languages into English text. Beyond these basic features, Whisper can be tweaked and fine-tuned for certain activities and capabilities.
For example, Gladia has enhanced Whisper to do additional tasks such as live-streaming transcription and speaker diarization. The model can also be improved to recognize and transcribe additional languages, dialects, and accents. It can also be made more sensitive to specific domains in order to identify industry-specific jargon and terminology. This flexibility allows developers to customize Whisper for their own use cases.
What was it trained on?
OpenAI Whisper was trained on a massive dataset of 680,000 hours of supervised data, making it one of the most complete ASR systems accessible. The dataset, gathered from the internet and academic resources, covers a wide range of topics and acoustic settings, ensuring that Whisper can reliably transcribe speech in a variety of real-world scenarios. Furthermore, 117,000 hours (about ⅓) of labeled pre-training data is multilingual, resulting in checkpoints applicable to 99 languages, including many low-resource languages.
The vast volume of training data contributes to Whisper's capacity to generalize and perform efficiently across a wide range of applications. As a model pre-trained directly on the supervised task of voice recognition, it has a higher average level of accuracy than most other open-source models.
However, due to the generalist nature of its initial training dataset, the model is mathematically more biased towards phrases unrelated to professional audio data, implying that it would normally require some fine-tuning to produce consistently accurate results in business environments.
What precisely is Whisper used for?
- Whisper is a highly versatile model that can be used to construct a number of voice-enabled apps across sectors and use cases, for example:
- Using Whisper ASR, create a call center assistant capable of comprehending speech and reacting to consumer requests via voice interactions.
- Whisper's exact transcription capabilities make it an excellent choice for automating transcription in virtual meetings and note-taking systems that cater to both general audio and specialty verticals such as education, healthcare, journalism, legal, and more.
- Whisper can be used in media goods to generate podcast transcripts and video captions, especially in live streaming contexts, to improve the viewing experience and accessibility for consumers throughout the world. Together with text-to-speech,
- Whisper is often used in sales-optimized apps to provide CRM enrichment capabilities with transcripts from customer and prospect meetings.
Is there a Whisper API?
In March 2023, OpenAI made the large-v2 model available through our API, which performs quicker than the open-source model and costs $0.006 per minute of transcribing. Whisper API handles typical audio formats such as m4a, mp3, mp4, and wav.
There are also Whisper-based APIs, such as Gladia, which uses a hybrid and upgraded Whisper architecture to provide a wider range of capabilities and features than the official OpenAI API.
What are the limits of Whisper AI?
Vanilla Whisper has various limitations. First, the upload file size and duration are limited to 25MB and 30 seconds, respectively. The model is unable to handle URLs and callbacks. The model, which is powered by a forerunner of the legendary GPT-3 during the decoding phase, is also notorious for causing hallucinations, which result in transcript errors. In terms of features, it offers speech-to-text transcription and translation into English, but no extra audio intelligence functions such as speaker diarization, summary, or others. Real-time transcribing is also not supported.
What are the main alternatives to Whisper ASR?
There are both commercial and open-source options available. Which route you take is determined by your use case, budget, and project needs. You may want to read this article to understand more about the benefits and drawbacks of a Whisper-based API versus OSS.
Some open-source alternatives to Whisper.
Mozilla DeepSpeech is an open-source ASR engine that enables developers to train unique models, enabling flexibility for individual project requirements.
Kaldi: Kaldi is a robust toolbox for voice recognition systems that offers substantial customization options.
Wav2vec is Meta AI's speech recognition system for self-supervised and high-performance speech processing.
Top API alternatives to Whisper Big Tech: Google Cloud Speech-to-Text, Microsoft Azure AI Speech, and AWS Transcribe are examples of multilingual speech-to-text services that include transcription, translation, and custom vocabulary.
Why is Whisper so excellent?
Whisper's outstanding base accuracy and performance in handling a variety of languages make it stand out as a best-in-class ASR system. Differentiating itself from other speech recognition systems is its capacity to adapt to difficult acoustic settings, such as noisy and multilingual audio. By default, it is 92% accurate, with an average word mistake rate of 8.06%, according to the Open ASR Leaderboard.
Whisper is very adaptable and helpful for a variety of applications because it comes in multiple sizes and enables developers to balance computational cost, speed, and accuracy according to the needs of the intended use.
How much time does it take to transcribe using Whisper?
Using a GPU, Whisper AI transcription typically takes 8 to 30 minutes, depending on the nature of audio. If the transcription is done using only CPUs, it takes x2 longer.
Ready to explore Whisper AI? Try out yourself and share your learnings and experience in comments section.
Happy Learning :)
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