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 typically billed by audio duration or by the number of characters/tokens processed, depending on the model. Check the model details in Models for the exact pricing unit.
• 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"])