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POST
https://api.keywordsai.co
/
api
/
audio
/
speech
You could use Keywords AI’s unified LLM API to call Text-to-speech model to turn text into lifelike spoken audio. Keywords AI now supports tts-1 and tts-1-hd models from OpenAI.
ModelDescription
tts-1OpenAI latest text to speech model, optimized for speed.
tts-1-hdOpenAI latest text to speech model, optimized for quality.

​
Integration steps:

1

Get your OpenAI API key

Go to OpenAI API plaform to get your OpenAI API key.
2

Add OpenAI's API key in Providers

You should add your OpenAI’s API key on Keywords AI credentials page.
3

Call your text-to-speech model

from openai import OpenAI

client = OpenAI(
    base_url="https://api.keywordsai.co/api/",
    api_key="YOUR_KEYWORDSAI_API_KEY",
)

response = client.audio.speech.create(
    model="tts-1", # or "tts-1-hd"
    voice="alloy",
    input="Today is a wonderful day to build something people love!",
    extra_body={"customer_identifier": "customer_11"}  # All Keywords AI parameters are supported
)

​
OpenAI parameters

​
model
string
required
The model to use. It should be one of tts-1 or tts-1-hd.
​
input
string
required
The text to generate audio for. The maximum length is 4096 characters.
​
voice
string
required
The voice to use when generating the audio. Supported voices are alloy, echo, fable, onyx, nova, and shimmer. Previews of the voices are available in the OpenAI’s Text to speech guide.
​
response_format
string
default:"mp3"
The format to audio in. Supported formats are mp3, opus, aac, flac, wav, and pcm.
​
speed
Number
default:1
The speed of the generated audio. Select a value from 0.25 to 4.0. 1.0 is the default.

​
Keywords AI parameters

See how to make a standard Keywords AI API call in the Quick Start guide.

​
Generation parameters

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customer_credentials
object
You can pass in your customer’s credentials for supported providers and use their credits when our proxy is calling models from those providers.
See details here
"customer_credentials": {

  "openai": {
    "api_key": "YOUR_OPENAI_API_KEY",
  }
}
​
disable_log
boolean
When set to true, only the request and performance metrics will be recorded, input and output messages will be omitted from the log.

​
Observability parameters

​
metadata
dict
You can add any key-value pair to this metadata field for your reference. Check the details of metadata here.Contact [email protected] if you need extra parameter support for your use case.
{
  "my_value_key": "my_value"
}
​
customer_identifier
string
Use this as a tag to identify the user associated with the API call. See the details of customer identifier here.
{
    //...other_params,
    "customer_identifier": "user_123"
}
​
customer_email
string
This is the email address of the user associated with the API call. You can add your corresponding user’s email address to the request.You could also edit customer’s emails on the platform. Check the details of user editing here.
{
    //...other_params,
    "customer_email": "[email protected]"
}
​
thread_identifier
string
See logs as a conversation log thread. Pass all logs with the same thread_identifier to see them in the same thread.
{
    //...other_params,
    "thread_identifier": "test_thread"
}
​
request_breakdown
boolean
default:false
Adding this returns the summarization of the response in the response body. If streaming is on, the metrics will be streamed as the last chunk.
{
  "id": "chatcmpl-7476cf3f-fcc9-4902-a548-a12489856d8a",
  //... main part of the response body ...
  "request_breakdown": {
    "prompt_tokens": 6,
    "completion_tokens": 9,
    "cost": 4.8e-5,
    "prompt_messages": [
      {
        "role": "user",
        "content": "How are you doing today?"
      }
    ],
    "completion_message": {
      "content": " I'm doing well, thanks for asking!",
      "role": "assistant"
    },
    "model": "claude-2",
    "cached": false,
    "timestamp": "2024-02-20T01:23:39.329729Z",
    "status_code": 200,
    "stream": false,
    "latency": 1.8415491580963135,
    "scores": {},
    "category": "Questions",
    "metadata": {},
    "routing_time": 0.18612787732854486,
    "full_request": {
      "messages": [
        {
          "role": "user",
          "content": "How are you doing today?"
        }
      ],
      "model": "claude-2",
      "logprobs": true
    },
    "sentiment_score": 0
  }
}