Speech

• bosonai/asr: Speech-to-text.
• bosonai/tts: Text-to-speech.

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Use Cases

• Transcription: Convert meetings, calls, lectures, or voice notes into searchable text.
• Voice assistants: Combine ASR, an LLM, and TTS to build conversational voice experiences.
• Subtitles and captions: Generate captions and subtitles for video and audio content.
• Accessibility: Read content aloud, or let users interact by voice instead of typing.

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Usage

• Text-to-speech (TTS): Send the text to be spoken as a normal user message. The generated audio appears in choices[0].message.audio.data as base64-encoded WAV. Decode it and write it to a file to play it.
• Speech-to-text (ASR): Send audio as a content part of type input_audio, including the base64 data and format information such as wav. The transcription result is returned as plain text in choices[0].message.content.

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Key Parameters

• TTS:
  • model: bosonai/tts.
  • messages: The role is user, and content is the text to be spoken.
• ASR:
  • model: bosonai/asr.
  • messages: The role is user, and content is an array containing {"type": "input_audio", "input_audio": {"data": "<base64>", "format": "wav"}}.

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Notes

• Supported audio formats and maximum file size or duration vary by model. For details, refer to the model details in Models.
• For long audio, splitting it into shorter segments helps stay within limits and reduces timeout risk.

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Billing

• Pricing unit: Speech models are billed by audio duration. For ASR, usage is based on input audio duration. For TTS, usage is based on output audio duration. Check the model details in Models for the exact unit price.
• Usage: Usage Detail may show audio duration for audio model requests. Longer input audio for ASR, or longer output audio for TTS, produces higher usage and cost.
• Billing reference: For common billing dimensions and usage fields, see Token Usage and Billing.
• Recommendation: For longer recordings, split the audio into multiple segments and process them separately to reduce timeout and retry costs.

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Examples

export API_KEY="YOUR_API_KEY"

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Text-to-Speech (TTS)

import os
import base64
import requests

api_key = os.environ["API_KEY"]
api_url = "https://api.scitix.ai/model-api/chat/completions"

response = requests.post(
    api_url,
    headers={
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json",
    },
    json={
        "model": "bosonai/tts",
        "messages": [
            {"role": "user", "content": "Welcome to the ScitiX Model Inference API."}
        ],
    },
)

audio_b64 = response.json()["choices"][0]["message"]["audio"]["data"]
with open("welcome.wav", "wb") as f:
    f.write(base64.b64decode(audio_b64))
print("Saved welcome.wav")

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Speech-to-Text (ASR)

import os
import base64
import requests

api_key = os.environ["API_KEY"]
api_url = "https://api.scitix.ai/model-api/chat/completions"

with open("welcome.wav", "rb") as f:
    audio_b64 = base64.b64encode(f.read()).decode()

response = requests.post(
    api_url,
    headers={
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json",
    },
    json={
        "model": "bosonai/asr",
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "input_audio",
                        "input_audio": {"data": audio_b64, "format": "wav"},
                    }
                ],
            }
        ],
    },
)

print(response.json()["choices"][0]["message"]["content"])