In our increasingly digital world, the need for transcribing speech to text is just getting more important. Whether it's for accessibility, content creation, data analysis, or something else, converting spoken language into written form is a problem that demands an efficient solution. Enter , an AI model by OpenAI that does exactly that: turning the spoken word into easily digestible text. Whisper This guide will walk you through what Whisper is, how it functions, and how to effectively use it. Currently ranked 19th on AIModels.fyi, Whisper is a powerful tool that can be of great use in various applications. We'll also explore how you can use to find similar models to suit your unique requirements. So, without further ado, let's delve into the world of AI-powered speech-to-text transcription. AIModels.fyi About the Whisper Model Whisper, an AI model created by , is designed to convert speech in audio files into text. Its applications are extensive, ranging from generating subtitles for videos to or meetings. With more than 2M runs, Whisper stands out as a reliable and in its category. OpenAI transcribing interviews popular model The model takes an audio input and transcribes it into the written word, effectively bridging the gap between spoken and written language. Furthermore, it supports a vast number of languages, making it an excellent tool for multilingual projects. More detailed information about the model can be found on its . detail page Understanding the Inputs and Outputs of the Whisper Model Before diving into the usage of the Whisper model, it's important to understand what goes in and what comes out - that is, the inputs and outputs of the model. Inputs The primary input for Whisper is an audio file, which it processes and transcribes into text. Additional input parameters allow you to customize how the model operates: : Allows you to select from different versions of the Whisper model. model string : Lets you choose the format for the transcription, with options for plain text, srt, or vtt. transcription string : Gives you the ability to translate the text to English. translate boolean : Allows you to specify the language spoken in the audio. language string : This parameter controls the 'creativity' of the model's output. temperature number : A list of token ids that you don't want the model to output. suppress_tokens string Outputs The model outputs an object containing the transcribed text with several fields: : The transcriptions are broken into segments. segments & : Transcription results can be obtained in these formats. srt_file txt_file : If the translation option is enabled, the translated text is provided here. translation : This is the final transcribed text. transcription : The language detected by the model. detected_language Now that we understand what the model takes in and puts out, let's see how we can use it to solve our transcription problems! Using the Whisper Model to Transcribe Speech to Text Whether you're a coder who likes to get hands-on or prefer a more interactive demo approach, using the Whisper model is simple and straightforward. Step 1: Authentication First, you'll need to install the Replicate Node.js client and authenticate using your API token. This allows you to interact with the Whisper model programmatically. npm install replicate export REPLICATE_API_TOKEN=your_api_token_here Step 2: Running the Model After authenticating, you can run the model with your audio input: import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, }); const output = await replicate.run( "openai/whisper:91ee9c0c3df30478510ff8c8a3a545add1ad0259ad3a9f78fba57fbc05ee64f7", { input: { audio: "your_audio_here" } } ); You can also set a webhook to be called when the prediction is complete, which can be useful for asynchronous processing: const prediction = await replicate.predictions.create({ version: "91ee9c0c3df30478510ff8c8a3a545add1ad0259ad3a9f78fba57fbc05ee64f7", input: { audio: "your_audio_here" }, webhook: "https://example.com/your-webhook", webhook_events_filter: ["completed"] }); Taking it Further - Finding Other Audio-to-Text Models with AIModels.fyi Perhaps you'd like to compare Whisper with other models or explore other models in the same problem space. How can you find them? Well, AIModels.fyi is a fantastic resource for this purpose, featuring a fully searchable and filterable database of AI models from various platforms. Step 1: Visit AIModels.fyi Head over to AIModels.fyi to start your search for similar models. Step 2: Use the Search Bar Use the search bar at the top of the page to search for models with specific keywords, such as "audio-to-text" or "transcription.” This will display a list of relevant models. Step 3: Filter the Results After using the search bar, you can further narrow down the results by using the filters on the left side of the page. You can filter and search models based on various criteria, including: : The platform the model is hosted on, such as OpenAI, Hugging Face, etc. Platforms : The creator or organization behind the model. Creator : The price range for using the model. Cost : What the model does and what it's used for. Description Step 4: Explore Model Details Once you've found a model that catches your interest, click on it to view more details. You'll be able to see a comprehensive breakdown of the model's features, including its inputs and outputs, performance metrics, and use cases. Conclusion Whether you're a seasoned developer or a newcomer to the world of AI, OpenAI's Whisper is an accessible and powerful tool for converting speech into text. Coupled with resources like , it's easier than ever to find the perfect model for your unique project needs. Dive in and start exploring the possibilities today! AIModels.fyi Subscribe or follow me on Twitter for more content like this! Also published . here
