Transcription / MODEL API
Whisper-Large-V3-Turbo
Input $0.1 / 1M tokens
OVERVIEW
Model overview
Whisper large-v3-turbo is a finetuned version of a pruned Whisper large-v3. In other words, it's the exact same model, except that the number of decoding layers have reduced from 32 to 4. As a result, the model is way faster, at the expense of a minor quality degradation.
Specifications
- Category
- Transcription
- Input modalities
- audio
- Output modalities
- text
MODEL PRICING
Model API pricing
Transcription
USD per 1M tokens| Model | Input | Output |
|---|---|---|
| Whisper-Large-V3-Turbo | $0.1 | — |
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
whisper-large-v3-turbo
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 openai
export GIGACOMMA_API_BASE_URL='https://api.ecohash.com/v1'
export GIGACOMMA_MODEL_ID='whisper-large-v3-turbo'
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: audio.mp3
import osfrom openai import OpenAIMODEL_ID = os.environ["GIGACOMMA_MODEL_ID"]client = OpenAI( base_url=os.environ["GIGACOMMA_API_BASE_URL"], api_key=os.environ["GIGACOMMA_API_KEY"],)with open("audio.mp3", "rb") as audio: response = client.audio.transcriptions.create( model=MODEL_ID, file=audio, )print(response.text)