Transcribe audio using Deepgram’s speech-to-text API
URL to which we’ll make the callback request
HTTP method by which the callback request will be made
Custom topics you want the model to detect within your input audio or text if present Submit up to 100
Sets how the model will interpret strings submitted to the custom_topic param. When strict, the model will only return topics submitted using the custom_topic param. When extended, the model will return its own detected topics in addition to those submitted using the custom_topic param
Custom intents you want the model to detect within your input audio if present
Sets how the model will interpret intents submitted to the custom_intent param. When strict, the model will only return intents submitted using the custom_intent param. When extended, the model will return its own detected intents in addition those submitted using the custom_intents param
Identifies and extracts key entities from content in submitted audio
Identifies the dominant language spoken in submitted audio
Recognize speaker changes. Each word in the transcript will be assigned a speaker number starting at 0
Identify and extract key entities from content in submitted audio
Specify the expected encoding of your submitted audio
Arbitrary key-value pairs that are attached to the API response for usage in downstream processing
Filler Words can help transcribe interruptions in your audio, like “uh” and “um”
Recognizes speaker intent throughout a transcript or text
Key term prompting can boost or suppress specialized terminology and brands. Only compatible with Nova-3
Keywords can boost or suppress specialized terminology and brands
The BCP-47 language tag that hints at the primary spoken language. Depending on the Model and API endpoint you choose only certain languages are available
Spoken measurements will be converted to their corresponding abbreviations
AI model used to process submitted audio
Transcribe each audio channel independently
Numerals converts numbers from written format to numerical format
Splits audio into paragraphs to improve transcript readability
Profanity Filter looks for recognized profanity and converts it to the nearest recognized non-profane word or removes it from the transcript completely
Add punctuation and capitalization to the transcript
Redaction removes sensitive information from your transcripts
Search for terms or phrases in submitted audio and replaces them
Search for terms or phrases in submitted audio
Recognizes the sentiment throughout a transcript or text
Apply formatting to transcript output. When set to true, additional formatting will be applied to transcripts to improve readability
Summarize content. For Listen API, supports string version option. For Read API, accepts boolean only.
Label your requests for the purpose of identification during usage reporting
Detect topics throughout a transcript or text
Segments speech into meaningful semantic units
Seconds to wait before detecting a pause between words in submitted audio
Version of an AI model to use
Successful transcription