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OpenVINO/
whisper-large-v3-turbo-int4-ov

Name: whisper-large-v3-turbo-int4-ov
Availability: InStock

openvino

whisper

audio

automatic-speech-recognition

hf-asr-leaderboard

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whisper-large-v3-turbo-int4-ov

Model creator: OpenAI
Original model: whisper-large-v3-turbo

Description

This is whisper-large-v3-turbo model converted to the OpenVINO™ IR (Intermediate Representation) format with weights compressed to INT4 by NNCF.

Whisper large-v3-turbo is a finetuned version of a pruned Whisper large-v3 with the number of decoder layers reduced from 32 to 4, which makes the model significantly faster at the expense of a minor quality degradation.

Quantization Parameters

Weight compression was performed using nncf.compress_weights with the following parameters:

mode: INT4_ASYM
ratio: 1.0
group_size: 128

For more information on quantization, check the OpenVINO model optimization guide.

Compatibility

The provided OpenVINO™ IR model is compatible with:

OpenVINO version 2026.1.0 and higher
Optimum Intel 1.27.0 and higher

Running Model Inference with Optimum Intel

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

Run model inference:

from datasets import load_dataset
from transformers import AutoProcessor
from optimum.intel.openvino import OVModelForSpeechSeq2Seq

model_id = "OpenVINO/whisper-large-v3-turbo-int4-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

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

Download model from HuggingFace Hub

import huggingface_hub as hf_hub

model_id = "OpenVINO/whisper-large-v3-turbo-int4-ov"
model_path = "whisper-large-v3-turbo-int4-ov"

hf_hub.snapshot_download(model_id, local_dir=model_path)

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 MIT 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.