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.