Update README.md

README.md

@@ -3,7 +3,7 @@ license: apache-2.0
 license_link: https://choosealicense.com/licenses/apache-2.0/
 ---
 # whisper-large-v3-int4-ov
-* Model creator: [Openai](https://huggingface.co/openai)
+* Model creator: [OpenAI](https://huggingface.co/openai)
  * Original model: [whisper-large-v3](https://huggingface.co/openai/whisper-large-v3)
 
 ## Description
@@ -27,7 +27,7 @@ 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
+## 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:
 
@@ -38,21 +38,62 @@ pip install optimum[openvino]
 2. Run model inference:
 
 ```
-from transformers import AutoTokenizer
-from optimum.intel.openvino import OVModelForCausalLM
+from transformers import AutoProcessor
+from optimum.intel.openvino import OVModelForSpeechSeq2Seq
 
 model_id = "OpenVINO/whisper-large-v3-int4-ov"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = OVModelForCausalLM.from_pretrained(model_id)
+tokenizer = AutoProcessor.from_pretrained(model_id)
+model = OVModelForSpeechSeq2Seq.from_pretrained(model_id)
 
-inputs = tokenizer("What is OpenVINO?", return_tensors="pt")
+dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True)
+sample = dataset[0]
 
-outputs = model.generate(**inputs, max_length=200)
-text = tokenizer.batch_decode(outputs)[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)
 ```
 
-For more examples and possible optimizations, refer to the [OpenVINO Large Language Model Inference Guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html).
+## 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-large-v3-int4-ov"
+model_path = "whisper-large-v3-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