Model reference · open weights

whisper-ov

Available as managed deployment Audio OpenVINO Speech→text 1 variants 773 dl/mo

whisper-ov is an open-weight audio or speech model from OpenVINO. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released byOpenVINO
TypeAudio & music
TaskSpeech→text
Released2024-11-20
Popularity773 downloads / month
LicenceOpen weights

About

What whisper-ov is

Description

This is whisper-base model converted to the OpenVINO™ IR (Intermediate Representation) format with weights compressed to FP16.

Compatibility

The provided OpenVINO™ IR model is compatible with:

Read the full model card
  • OpenVINO version 2025.2.0 and higher
  • Optimum Intel 1.23.0 and higher

Running Model Inference with Optimum Intel

  1. Install packages required for using Optimum Intel integration with the OpenVINO backend:
pip install optimum[openvino] "datasets<4" librosa soundfile --extra-index-url https://download.pytorch.org/whl/cpu
  1. Run model inference:
from datasets import load_dataset
from transformers import AutoProcessor
from optimum.intel.openvino import OVModelForSpeechSeq2Seq

model_id = "OpenVINO/whisper-base-fp16-ov"
tokenizer = AutoProcessor.from_pretrained(model_id)
model = OVModelForSpeechSeq2Seq.from_pretrained(model_id)

dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True)
sample = dataset[0]

input_features = tokenizer(
    sample["audio"]["array"],
    sampling_rate=sample["audio"]["sampling_rate"],
    return_tensors="pt",
).input_features

outputs = model.generate(input_features)
text = tokenizer.batch_decode(outputs)[0]
print(text)

Running Model Inference with OpenVINO GenAI

  1. Install packages required for using OpenVINO GenAI.
pip install huggingface_hub "datasets<4" librosa soundfile
pip install -U --pre --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly openvino openvino-tokenizers openvino-genai
  1. Download model from HuggingFace Hub
import huggingface_hub as hf_hub

model_id = "OpenVINO/whisper-base-fp16-ov"
model_path = "whisper-base-fp16-ov"

hf_hub.snapshot_download(model_id, local_dir=model_path)

  1. Run model inference:
import openvino_genai as ov_genai
import datasets

device = "CPU"
pipe = ov_genai.WhisperPipeline(model_path, device)

dataset = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True)
sample = dataset[0]["audio"]["array"]
print(pipe.generate(sample))

More GenAI usage examples can be found in OpenVINO GenAI library docs and samples

Limitations

Check the original model card for original model card for limitations.

Legal information

The original model is distributed under apache-2.0 license. More details can be found in original model card.

Disclaimer

Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See Intel’s Global Human Rights Principles. Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.

From the published model card. Full card on the HuggingFace links in the sidebar.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys whisper-ov for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (whisper-ov below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/audio/transcriptions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -F model="whisper-ov" -F file=@audio.mp3

Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.

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