OpenAI’s most accurate speech-to-text model, Whisper, has now been released through their API, providing developers with access to cutting-edge transcription capabilities.
Whisper was open-sourced in September 2022 and has taken the internet by storm with its state-of-the-art transcription accuracy in close to 100 languages. However, it can be challenging to run in production apps, requiring GPU deployment for faster transcription, making it difficult for regular developers to use. With the large-v2 model now available through an API, developers can benefit from its state-of-the-art features with the added convenience of on-demand access priced at USD 0.006 per minute of transcription.
OpenAI’s highly-optimized serving stack ensures faster performance compared to other services, providing an added advantage to developers. The speech-to-text API offers two endpoints, transcriptions and translations, based on the large-v2 Whisper model.
Developers can use these endpoints to transcribe audio into whatever language the audio is in or translate and transcribe the audio into English. The latter is one of the unique features of OpenAI’s Whisper where it translates any language audio to English in single-shot without an intermediary step. The input file types currently supported include mp3, mp4, mpeg, mpga, m4a, wav, and webm, with file uploads limited to 25 MB.
OpenAI’s API currently supports over 50 languages through both the transcriptions and translations endpoint, including Afrikaans, Arabic, Chinese, Czech, Dutch, English, Finnish, French, German, Hindi, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Swedish, Thai, Turkish, Ukrainian, and many more. According to the OpenAI documentation, while the underlying model was trained on 98 languages, only languages that have a word error rate (WER) of less than 50% are listed, which is an industry-standard benchmark for speech-to-text model accuracy.
With the release of the Whisper model through the API, language SaaS builders and enterprises can benefit from the state-of-the-art features and convenience of on-demand access. They can now use this state-of-the-art technology to build speech-to-text applications and services that deliver faster, more accurate, and cost-effective results.
Use Cases with OpenAI’s Whisper API
Here are six key use cases and applications that can be built on top of the Whisper model.
OpenAI’s Whisper API can be used by transcription service providers to transcribe audio and video content in multiple languages accurately and efficiently. The API’s ability to transcribe the audio in near real-time and support multiple file formats allows for greater flexibility and faster turnaround times.
Language Learning Tools
Language learning platforms can leverage OpenAI’s Whisper API to provide speech recognition and transcription capabilities to their users. This would allow language learners to practice speaking and listening skills with real-time feedback, improving their language acquisition process.
Indexing Podcasts and Audio Content
With the rise of podcasts and audio content, the Whisper model can be used to transcribe and generate text-based versions of audio content. This can help improve accessibility for those with hearing impairments and also improve searchability for podcast episodes, making them more discoverable.
OpenAI’s Whisper API can be used to transcribe and analyze customer calls in real time, allowing call center agents to provide more personalized and efficient customer service.
With the Whisper model, developers can build automated market research tools that transcribe and analyze customer feedback in real time. This can help businesses gather valuable insights from customer feedback, improve their products and services, and identify areas for improvement.
With the ability to recognize and transcribe speech in multiple languages, OpenAI’s Whisper API can be used to create voice-based search applications that support multiple languages.
You can also combine Whisper’s API with the text generation APIs (ChatGPT/GPT-3) to build innovative applications like “video to quiz”, “video to blogpost”, etc.
The recent changes made by OpenAI’s API team also allow enterprise clients to have deeper control over the specific model version and system performance of their requests. With the option for dedicated instances, enterprises can now optimize their workload against hardware performance and significantly reduce costs at high volumes relative to shared infrastructure.
Additionally, the API now offers greater transparency and data privacy through the option to opt-in for service improvements using submitted data, as well as a default 30-day data retention policy for users.
In conclusion, OpenAI’s Whisper speech-to-text model and its newly released API provides developers with a wide range of applications and use cases. From automated transcription services to language learning apps, customer service tools, and more, the possibilities are endless.
With its highly-optimized serving stack and on-demand access, the Whisper API is an excellent choice for language SaaS builders and enterprises looking to leverage cutting-edge speech-to-text capabilities.