|
--- |
|
license: apache-2.0 |
|
license_link: https://choosealicense.com/licenses/apache-2.0/ |
|
base_model: |
|
- openai/whisper-base |
|
base_model_relation: quantized |
|
--- |
|
# whisper-base-int4-ov |
|
* Model creator: [OpenAI](https://huggingface.co/openai) |
|
* Original model: [whisper-base](https://huggingface.co/openai/whisper-base) |
|
|
|
## Description |
|
This is [whisper-base](https://huggingface.co/openai/whisper-base) 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). |
|
|
|
## Quantization Parameters |
|
|
|
Weight compression was performed using `nncf.compress_weights` with the following parameters: |
|
|
|
* mode: **int4_asym** |
|
* ratio: **1** |
|
* group_size: **128** |
|
|
|
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 2024.4.0 and higher |
|
* Optimum Intel 1.20.0 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-base-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 = 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-base-int4-ov" |
|
model_path = "whisper-base-int4-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://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples) |
|
|
|
## Limitations |
|
|
|
Check the original model card for [original model card](https://huggingface.co/openai/whisper-base) 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-base). |
|
|
|
## 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. |
|
|
