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--- |
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license: apache-2.0 |
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license_link: https://choosealicense.com/licenses/apache-2.0/ |
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base_model: |
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- openai/whisper-large-v3 |
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base_model_relation: quantized |
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--- |
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# whisper-large-v3-int4-ov |
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* Model creator: [OpenAI](https://huggingface.co/openai) |
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* Original model: [whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) |
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## Description |
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This is [whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT4 by [NNCF](https://github.com/openvinotoolkit/nncf). |
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## Quantization Parameters |
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Weight compression was performed using `nncf.compress_weights` with the following parameters: |
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* mode: **int4_asym** |
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* ratio: **1** |
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* group_size: **128** |
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For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html). |
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## Compatibility |
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The provided OpenVINO™ IR model is compatible with: |
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* OpenVINO version 2024.4.0 and higher |
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* Optimum Intel 1.20.0 and higher |
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## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) |
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1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend: |
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``` |
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pip install optimum[openvino] |
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``` |
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2. Run model inference: |
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``` |
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from transformers import AutoProcessor |
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from optimum.intel.openvino import OVModelForSpeechSeq2Seq |
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model_id = "OpenVINO/whisper-large-v3-int4-ov" |
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tokenizer = AutoProcessor.from_pretrained(model_id) |
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model = OVModelForSpeechSeq2Seq.from_pretrained(model_id) |
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dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True) |
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sample = dataset[0] |
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input_features = processor( |
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sample["audio"]["array"], |
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sampling_rate=sample["audio"]["sampling_rate"], |
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return_tensors="pt", |
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).input_features |
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outputs = model.generate(input_features) |
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text = processor.batch_decode(outputs)[0] |
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print(text) |
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``` |
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## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai) |
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1. Install packages required for using OpenVINO GenAI. |
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``` |
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pip install huggingface_hub |
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pip install -U --pre --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly openvino openvino-tokenizers openvino-genai |
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``` |
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2. Download model from HuggingFace Hub |
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``` |
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import huggingface_hub as hf_hub |
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model_id = "OpenVINO/whisper-large-v3-int4-ov" |
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model_path = "whisper-large-v3-int4-ov" |
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hf_hub.snapshot_download(model_id, local_dir=model_path) |
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``` |
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3. Run model inference: |
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``` |
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import openvino_genai as ov_genai |
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import datasets |
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device = "CPU" |
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pipe = ov_genai.WhisperPipeline(model_path, device) |
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dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True) |
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sample = dataset[0]["audio]["array"] |
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print(pipe.generate(sample)) |
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``` |
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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://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples) |
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## Limitations |
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Check the original model card for [original model card](https://huggingface.co/openai/whisper-large-v3) for limitations. |
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## Legal information |
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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-large-v3). |
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## Disclaimer |
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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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