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Update README.md

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@@ -50,6 +50,7 @@ pip install optimum[openvino]
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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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@@ -60,14 +61,14 @@ 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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@@ -101,7 +102,7 @@ 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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  2. Run model inference:
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  ```
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+ from datasets import load_dataset
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  from transformers import AutoProcessor
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  from optimum.intel.openvino import OVModelForSpeechSeq2Seq
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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 = tokenizer(
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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 = tokenizer.batch_decode(outputs)[0]
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  print(text)
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  ```
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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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