Parakeet TDT 0.6B v3

An open speech model from NVIDIA that turns recorded calls and meetings into text. nvidia/parakeet-tdt-0.6b-v3

Get started Run in your cloud Hugging Face

Accuracy

LibriSpeech, 256 clean clips, word error rate · not measured
Time per request

Not timed one request at a time
Serverless price

$0.00075

per audio minute

Transcribe audio 83% cheaper than GPT‑Transcribe

$45

Cost per 1,000 audio hours

See the comparison on Speech to text

Call it

from pathlib import Path
from sie_sdk import SIEClient
client = SIEClient(api_key="API keysk-sie-…")
audio = {"data": Path(fileWAVami-es2004b-carphone-warehouse.wavbrowse).read_bytes(), "format": "wav"}
result = client.extract(
    "modelnvidia/parakeet-tdt-0.6b-v3",
    {"audio": audio},
)
print(result["data"])

audio_bytes=$(base64 < 'ami-es2004b-carphone-warehouse.wav' | tr -d '\n')
curl https://api.superlinked.com/v1/extract/nvidia%2Fparakeet-tdt-0.6b-v3 \
  -H "Authorization: Bearer sk-sie-…" \
  -H "Content-Type: application/json" \
  -d "{\"items\":[{\"audio\":{\"data\":\"$audio_bytes\",\"format\":\"wav\"}}]}"

Build the "Speech to text" capability into my app using the Superlinked Inference Engine (SIE).

Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: nvidia/parakeet-tdt-0.6b-v3 (SIE primitive: /extract). Keep the model id configurable.

Task
- Input: an uploaded audio recording.
- Behaviour: return a verbatim transcript of the uploaded audio
- Send one POST /v1/extract request (SDK: extract) with one item {audio: {data, format}}: the original audio bytes (base64 in a JSON request) and the file extension as format. Send no params. Do not add an instruction: Parakeet takes none and rejects a prompt. Read the transcript from the returned item's data.text.

Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example.

Run

Output

source · ami-es2004b-carphone-warehouse.wav

text: That's kind of what led us on to comparing TV remote controls with their design features. Chain companies like Carphone Warehouse.
duration_ms: 8340
language: null

parakeet-tdt-0.6b-v3

Next to the other speech models

Next to the other speech models
Model	LibriSpeech, 256 clean clips, word error rate	Time per request	Serverless price	Size
Parakeet TDT 0.6B v3 nvidia	Not measured	Not timed	$0.00075 per audio minute	627M
whisper large v3 turbo openai	1.7%	Not timed	$0.00276 per audio minute	809M

Both speech models

Facts

Size: 627M parameters
License: cc-by-4.0
Reads: Audio
Endpoint: extract

Evaluation

Accuracy: Run on SIE against the public dataset named in each figure
Prices: Serverless list prices
Sources: Task page runs and their inputs

Run Parakeet TDT 0.6B v3 on Serverless or in your own cloud

SIE Serverless

We host the models; you call one API.

No idle GPUs, pay for what you use
Fits your stack: SDK, API, CLI, MCP
Self-host the same open-source engine
SOC 2 Type 2, US or EU data residency
Create an account to try Serverless

Get started

SIE Cluster

Kubernetes in your own cloud, backed by our engineers.

Terraform modules for your cloud
Apache-2.0, same engine as Serverless
GPU workers scale to zero between jobs
Separate worker pools for each tenant

Agent prompt Deploy SIE to our AWS account with the superlinked/sie/aws Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Google Cloud project with the superlinked/sie/google Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Azure subscription with the superlinked/sie/azure Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Alibaba Cloud account with the superlinked/sie/alicloud Terraform module. Docs: superlinked.com/docs/deployment.

Talk to us Deploy guide

SIE Local

The open-source server on your own machine.

NVIDIA GPU
One container, no cluster
Process your data fully offline
Same SDK and model IDs as Serverless

docker run --gpus all -p 8080:8080 ghcr.io/superlinked/sie-server:latest-cuda12-transformers5

Quickstart

Transcribe audio 83% cheaper than GPT‑Transcribe

Call it

Next to the other speech models

Facts

Run Parakeet TDT 0.6B v3 on Serverless or in your own cloud