Voice cloning / MODEL API
ViiTorVoice-NAR
Input $0.1 · Output $0.5 / 1M tokens
OVERVIEW
Model overview
ViiTorVoice-NAR is a non-autoregressive speech generation model for voice cloning, local speech editing, and emotion / paralinguistic speech control.
Specifications
- Category
- Voice cloning
- Input modalities
- text, audio
- Output modalities
- audio
MODEL PRICING
Model API pricing
Voice cloning
USD per 1M tokens| Model | Input | Output | Cache read |
|---|---|---|---|
| ViiTorVoice-NAR | $0.1 | $0.5 | $0.05 |
Public platform rates refresh every five minutes. A dash means a rate is not provided; it does not mean free. Check the console for final charges.
BUILT FOR YOUR STACK
API quickstart
Expand connection setup and run the preparation commands first. Then save and run the Python or cURL example.
Connection setup & preparationActual API address, model ID, and environment variables
- API address
https://api.ecohash.com/v1- API model ID
viitor-voice-nar
Replace YOUR_API_KEY with the full inference API key issued in the console. Run setup in macOS/Linux or a compatible shell. JSON cURL examples use the Python standard library to build the request body.
python -m pip install requests
export GIGACOMMA_API_BASE_URL='https://api.ecohash.com/v1'
export GIGACOMMA_MODEL_ID='viitor-voice-nar'
export GIGACOMMA_API_KEY="YOUR_API_KEY"GIGACOMMA_API_KEY is the environment variable name. Its value is an inference API key from the console, not an OpenAI API key. Get an API key
Prepare this local audio file before running the example: reference.wav
import osimport requestsMODEL_ID = os.environ["GIGACOMMA_MODEL_ID"]BASE_URL = os.environ["GIGACOMMA_API_BASE_URL"].rstrip("/")headers = {"Authorization": f"Bearer {os.environ['GIGACOMMA_API_KEY']}"}with open("reference.wav", "rb") as audio: response = requests.post( f"{BASE_URL}/audio/voice-clone", headers=headers, data={"model": os.environ["GIGACOMMA_MODEL_ID"], "input": "Hello, welcome to GIGACOMMA!"}, files={"reference_audio": audio}, timeout=120, )response.raise_for_status()with open("audio.wav", "wb") as output: output.write(response.content)