Pricing

Credit-based pricing for text-to-speech generation.

Credit Costs by Model

Cost Examples

Check Your Credits

curl "https://yourvoic.com/api/v1/user/credits" \
  -H "X-API-Key: your_api_key"

Response

{
    "credits": 10000,
    "used_this_month": 2500,
    "plan": "pro",
    "renewal_date": "2024-02-01"
}

Credit Usage Headers

Every TTS response includes credit information in headers:

import requests

response = requests.post(
    "https://yourvoic.com/api/v1/tts/generate",
    headers={"X-API-Key": "your_api_key"},
    json={"text": "Hello world!", "voice": "Peter"}
)

credits_used = response.headers.get("X-Credits-Used")
credits_remaining = response.headers.get("X-Credits-Remaining")

print(f"Credits used: {credits_used}")
print(f"Credits remaining: {credits_remaining}")

Optimizing Credit Usage

Choose the Right Model

• Notifications/IVR: Use rapid-flash (1 credit/char, ultra-fast latency)
• Conversational chatbots: Use aura-lite (1 credit/char, low latency)
• Professional content & e-learning: Use aura-prime (1 credit/char, natural expressiveness)
• Premium audiobooks & studio voiceover: Use aura-max (2 credits/char, highest fidelity)

Batch Processing

Process multiple texts in parallel to maximize throughput:

import asyncio
import aiohttp

async def generate_audio(session, text, voice="Peter"):
    async with session.post(
        "https://yourvoic.com/api/v1/tts/generate",
        headers={"X-API-Key": "your_api_key"},
        json={"text": text, "voice": voice, "model": "aura-lite"}
    ) as response:
        return await response.read()

async def batch_process(texts):
    async with aiohttp.ClientSession() as session:
        tasks = [generate_audio(session, text) for text in texts]
        return await asyncio.gather(*tasks)

# Process multiple texts
texts = ["Hello", "World", "From", "YourVoic"]
audios = asyncio.run(batch_process(texts))

Rate Limits

Rate limits vary by plan. Exceeding limits returns HTTP 429.