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---
license: apache-2.0
license_link: https://choosealicense.com/licenses/apache-2.0/
base_model: openai/whisper-medium
base_model_relation: quantized
---
# whisper-medium-int8-ov
* Model creator: [openai](https://huggingface.co/openai)
* Original model: [whisper-medium](https://huggingface.co/openai/whisper-medium)
## Description
This is [whisper-medium](https://huggingface.co/openai/whisper-medium) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT8 by [NNCF](https://github.com/openvinotoolkit/nncf).
## Quantization Parameters
Weight compression was performed using `nncf.compress_weights` with the following parameters:
* mode: **int8_asym**
* ratio: **1**
For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html).
## Compatibility
The provided OpenVINO™ IR model is compatible with:
* OpenVINO version 2024.5.0 and higher
* Optimum Intel 1.21.0 and higher
## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index)
1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
```
pip install optimum[openvino]
```
2. Run model inference:
```
from transformers import AutoProcessor
from optimum.intel.openvino import OVModelForSpeechSeq2Seq
model_id = "OpenVINO/whisper-medium-int8-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 = processor(
sample["audio"]["array"],
sampling_rate=sample["audio"]["sampling_rate"],
return_tensors="pt",
).input_features
outputs = model.generate(input_features)
text = processor.batch_decode(outputs)[0]
print(text)
```
## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai)
1. Install packages required for using OpenVINO GenAI.
```
pip install huggingface_hub
pip install -U --pre --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly openvino openvino-tokenizers openvino-genai
```
2. Download model from HuggingFace Hub
```
import huggingface_hub as hf_hub
model_id = "OpenVINO/whisper-medium-int8-ov"
model_path = "whisper-medium-int8-ov"
hf_hub.snapshot_download(model_id, local_dir=model_path)
```
3. Run model inference:
```
import openvino_genai as ov_genai
import datasets
device = "CPU"
pipe = ov_genai.WhisperPipeline(model_path, device)
dataset = 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](https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md) and [samples](https://gith
## Limitations
Check the original model card for [original model card](https://huggingface.co/openai/whisper-medium) for limitations.
## Legal information
The original model is distributed under [apache-2.0](https://choosealicense.com/licenses/apache-2.0/) license. More details can be found in [original model card](https://huggingface.co/openai/whisper-medium).
## 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](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). 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.
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