--- 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.