whisper-small

This documentation is valid for the following list of our models:

• #g1_whisper-small

Note:

Previously, our STT models operated via a single API call to POST https://api.aimlapi.com/v1/stt. You can view the API schema here.

Now, we are switching to a new two-step process:

• POST https://api.aimlapi.com/v1/stt/create – Creates and submits a speech-to-text processing task to the server. This method accepts the same parameters as the old version but returns a generation_id instead of the final transcript.

• GET https://api.aimlapi.com/v1/stt/{generation_id} – Retrieves the generated transcript from the server using the generation_id obtained from the previous API call.

This approach helps prevent generation failures due to timeouts. We've prepared a couple of examples below to make the transition to the new STT API easier for you.

Model Overview

The Whisper models are primarily for AI research, focusing on model robustness, generalization, and biases, and are also effective for English speech recognition. The use of Whisper models for transcribing non-consensual recordings or in high-risk decision-making contexts is strongly discouraged due to potential inaccuracies and ethical concerns.

The models are trained using 680,000 hours of audio and corresponding transcripts from the internet, with 65% being English audio and transcripts, 18% non-English audio with English transcripts, and 17% non-English audio with matching non-English transcripts, covering 98 languages in total.

Whisper models use per-second billing. The cost of audio transcription is based on the number of seconds in the input audio file, not the processing time.

Setup your API Key

If you don’t have an API key for the AI/ML API yet, feel free to use our Quickstart guide.

API Schema

Creating and sending a speech-to-text conversion task to the server

post

Authorizations

Body

modelundefined · enumRequiredPossible values:

custom_intentany ofOptional

A custom intent you want the model to detect within your input audio if present. Submit up to 100.

stringOptional

or

string[]Optional

custom_topicany ofOptional

A custom topic you want the model to detect within your input audio if present. Submit up to 100.

stringOptional

or

string[]Optional

custom_intent_modestring · enumOptional

Sets how the model will interpret strings 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.

Possible values:

custom_topic_modestring · enumOptional

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.

Possible values:

detect_languagebooleanOptional

Enables language detection to identify the dominant language spoken in the submitted audio.

detect_entitiesbooleanOptional

When Entity Detection is enabled, the Punctuation feature will be enabled by default.

detect_topicsbooleanOptional

Detects the most important and relevant topics that are referenced in speech within the audio

diarizebooleanOptional

Recognizes speaker changes. Each word in the transcript will be assigned a speaker number starting at 0

dictationbooleanOptional

Identifies and extracts key entities from content in submitted audio

diarize_versionstringOptional

extrastringOptional

Arbitrary key-value pairs that are attached to the API response for usage in downstream processing

filler_wordsbooleanOptional

Filler Words can help transcribe interruptions in your audio, like “uh” and “um”

intentsbooleanOptional

Recognizes speaker intent throughout a transcript or text

keywordsstringOptional

Keywords can boost or suppress specialized terminology and brands

languagestringOptional

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

measurementsbooleanOptional

Spoken measurements will be converted to their corresponding abbreviations

multi_channelbooleanOptional

Transcribes each audio channel independently

numeralsbooleanOptional

Numerals converts numbers from written format to numerical format

paragraphsbooleanOptional

Splits audio into paragraphs to improve transcript readability

profanity_filterbooleanOptional

Profanity Filter looks for recognized profanity and converts it to the nearest recognized non-profane word or removes it from the transcript completely

punctuatebooleanOptional

Adds punctuation and capitalization to the transcript

searchstringOptional

Search for terms or phrases in submitted audio

sentimentbooleanOptional

Recognizes the sentiment throughout a transcript or text

smart_formatbooleanOptional

Applies formatting to transcript output. When set to true, additional formatting will be applied to transcripts to improve readability

summarizestringOptional

Summarizes content. For Listen API, supports string version option. For Read API, accepts boolean only.

tagstring[]Optional

Labels your requests for the purpose of identification during usage reporting

topicsbooleanOptional

Detects topics throughout a transcript or text

utterancesbooleanOptional

Segments speech into meaningful semantic units

utt_splitnumberOptional

Seconds to wait before detecting a pause between words in submitted audio

Responses

201Success

application/json

post

HTTPcURLJavaScriptPython

POST /v1/stt/create HTTP/1.1
Host: api.aimlapi.com
Authorization: Bearer <YOUR_AIMLAPI_KEY>
Content-Type: application/json
Accept: */*
Content-Length: 592

{
  "model": "#g1_whisper-small",
  "custom_intent": "text",
  "custom_topic": "text",
  "custom_intent_mode": "strict",
  "custom_topic_mode": "strict",
  "detect_language": true,
  "detect_entities": true,
  "detect_topics": true,
  "diarize": true,
  "dictation": true,
  "diarize_version": "text",
  "extra": "text",
  "filler_words": true,
  "intents": true,
  "keywords": "text",
  "language": "text",
  "measurements": true,
  "multi_channel": true,
  "numerals": true,
  "paragraphs": true,
  "profanity_filter": true,
  "punctuate": true,
  "search": "text",
  "sentiment": true,
  "smart_format": true,
  "summarize": "text",
  "tag": [
    "text"
  ],
  "topics": true,
  "utterances": true,
  "utt_split": 1
}

201Success

{
  "generation_id": "123e4567-e89b-12d3-a456-426614174000"
}

Requesting the result of the task from the server using the generation_id

get

Authorizations

Path parameters

generation_idstringRequired

Responses

201Success

application/json

get

HTTPcURLJavaScriptPython

GET /v1/stt/{generation_id} HTTP/1.1
Host: api.aimlapi.com
Authorization: Bearer <YOUR_AIMLAPI_KEY>
Accept: */*

201Success

{
  "status": "text",
  "result": {
    "metadata": {
      "transaction_key": "text",
      "request_id": "text",
      "sha256": "text",
      "created": "2025-07-03T02:25:19.359Z",
      "duration": 1,
      "channels": 1,
      "models": [
        "text"
      ],
      "model_info": {
        "ANY_ADDITIONAL_PROPERTY": {
          "name": "text",
          "version": "text",
          "arch": "text"
        }
      }
    }
  }
}

Quick Code Examples

Let's use the #g1_whisper-small model to transcribe the following audio fragment:

Example #1: Processing a Speech Audio File via URL

import time
import requests

base_url = "https://api.aimlapi.com/v1"
# Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
api_key = "<YOUR_AIMLAPI_KEY>"

# Creating and sending a speech-to-text conversion task to the server
def create_stt():
    url = f"{base_url}/stt/create"
    headers = {
        "Authorization": f"Bearer {api_key}", 
    }

    data = {
        "model": "#g1_whisper-small",
        "url": "https://audio-samples.github.io/samples/mp3/blizzard_primed/sample-0.mp3"
    }
 
    response = requests.post(url, json=data, headers=headers)
    
    if response.status_code >= 400:
        print(f"Error: {response.status_code} - {response.text}")
    else:
        response_data = response.json()
        print(response_data)
        return response_data

# Requesting the result of the task from the server using the generation_id
def get_stt(gen_id):
    url = f"{base_url}/stt/{gen_id}"
    headers = {
        "Authorization": f"Bearer {api_key}", 
    }
    response = requests.get(url, headers=headers)
    return response.json()
    
# First, start the generation, then repeatedly request the result from the server every 10 seconds.
def main():
    stt_response = create_stt()
    gen_id = stt_response.get("generation_id")


    if gen_id:
        start_time = time.time()

        timeout = 600
        while time.time() - start_time < timeout:
            response_data = get_stt(gen_id)

            if response_data is None:
                print("Error: No response from API")
                break
        
            status = response_data.get("status")

            if status == "waiting" or status == "active":
                print("Still waiting... Checking again in 10 seconds.")
                time.sleep(10)
            else:
                print("Processing complete:/n", response_data["result"]['results']["channels"][0]["alternatives"][0]["transcript"])
                return response_data
   
        print("Timeout reached. Stopping.")
        return None     


if __name__ == "__main__":
    main()

Response

{'generation_id': '88a282a7-6f90-4532-a2a2-882c9d20d08e'}
Processing complete:
 He doesn't belong to you, and I don't see how you have anything to do with what is be his power yet. He's he personed that from this stage to you. Be fire-

Example #2: Processing a Speech Audio File via File Path

import time
import requests

base_url = "https://api.aimlapi.com/v1"
# Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
api_key = "<YOUR_AIMLAPI_KEY>"

# Creating and sending a speech-to-text conversion task to the server
def create_stt():
    url = f"{base_url}/stt/create"
    headers = {
        "Authorization": f"Bearer {api_key}", 
    }

    data = {
        "model": "#g1_whisper-small",
    }
    with open("stt-sample.mp3", "rb") as file:
        files = {"audio": ("sample.mp3", file, "audio/mpeg")}
        response = requests.post(url, data=data, headers=headers, files=files)
    
    if response.status_code >= 400:
        print(f"Error: {response.status_code} - {response.text}")
    else:
        response_data = response.json()
        print(response_data)
        return response_data

# Requesting the result of the task from the server using the generation_id
def get_stt(gen_id):
    url = f"{base_url}/stt/{gen_id}"
    headers = {
        "Authorization": f"Bearer {api_key}", 
    }
    response = requests.get(url, headers=headers)
    return response.json()
    
# First, start the generation, then repeatedly request the result from the server every 10 seconds.
def main():
    stt_response = create_stt()
    gen_id = stt_response.get("generation_id")


    if gen_id:
        start_time = time.time()

        timeout = 600
        while time.time() - start_time < timeout:
            response_data = get_stt(gen_id)

            if response_data is None:
                print("Error: No response from API")
                break
        
            status = response_data.get("status")

            if status == "waiting" or status == "active":
                print("Still waiting... Checking again in 10 seconds.")
                time.sleep(10)
            else:
                print("Processing complete:/n", response_data["result"]['results']["channels"][0]["alternatives"][0]["transcript"])
                return response_data
   
        print("Timeout reached. Stopping.")
        return None     


if __name__ == "__main__":
    main()

Response

{'generation_id': '88a282a7-6f90-4532-a2a2-882c9d20d08e'}
Processing complete:
 He doesn't belong to you, and I don't see how you have anything to do with what is be his power yet. He's he personed that from this stage to you. Be fire-