# Speech to Text ### OpenAI Whisper 1 The **Speech to Text (OpenAI Whisper 1)** node allows you to convert audio files into text using AI models designed for audio transcription. Below is a breakdown of its key components: * **Label**: The name of the node, labeled **OpenAI Whisper 1** by default. You can rename it based on your specific workflow or use case. * **Audio**: This required field is where you upload the audio file that needs to be transcribed. You can either provide a file URL or upload an audio file directly. Supported formats include `.mp3`, and the maximum file size is 500 MB. * **Prompt**: This field allows you to provide specific instructions or context for how the transcription should be handled. For example, you can give guidance on certain phrases or terms. * **Temperature**: This controls the randomness of the transcription output. A higher temperature value (closer to 2) produces more creative and varied outputs, while a lower value (closer to 0) makes the output more predictable and consistent. * **Fallback Outputs**: This field allows you to specify an alternative output or action in case the transcription fails, ensuring the workflow continues without disruption. * **Results**: The transcribed text from the audio file will be shown here, which can then be passed to subsequent nodes or used as needed. #### Usage This node is ideal for transcribing audio content into text, whether for generating subtitles, transcribing meetings, or converting spoken content into written format. It can be used in workflows that involve voice data or audio files that need to be converted into text for further processing. *** We’d love to hear from you! Reach out to **** --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://help.integrail.ai/agent-studio/editor/speech-to-text.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.