[LLM] Small fixes to the Whisper transformers INT4 example (#8573)

* Small fixes to the whisper example

* Small fix

* Small fix

python/llm/README.md

@@ -29,6 +29,7 @@ We may use any Hugging Face Transfomer models on `bigdl-llm`, and the following
 | Phoenix   | [link1](example/transformers/native_int4), [link2](example/transformers/transformers_int4/phoenix)   | 
 | StarCoder | [link1](example/transformers/native_int4), [link2](example/transformers/transformers_int4/starcoder) | 
 | InternLM  | [link](example/transformers/transformers_int4/internlm)  |
+| Whisper   | [link](example/transformers/transformers_int4/whisper)   |
 
 
 ### Working with `bigdl-llm`

python/llm/example/transformers/transformers_int4/README.md

@@ -17,6 +17,7 @@ You can use BigDL-LLM to run any Huggingface Transformer models with INT4 optimi
 | Phoenix   | [link](phoenix)   | 
 | StarCoder | [link](starcoder) | 
 | InternLM  | [link](internlm)  |
+| Whisper   | [link](whisper)   |
 
 ## Recommended Requirements
 To run the examples, we recommend using Intel® Xeon® processors (server), or >= 12th Gen Intel® Core™ processor (client).

python/llm/example/transformers/transformers_int4/whisper/readme.md

@@ -5,8 +5,8 @@ In this directory, you will find examples on how you could apply BigDL-LLM INT4
 ## 0. Requirements
 To run these examples with BigDL-LLM, we have some recommended requirements for your machine, please refer to [here](../README.md#recommended-requirements) for more information.
 
-## Example: Predict Tokens using `generate()` API
+## Example: Recognize Tokens using `generate()` API
-In the example [generate.py](./generate.py), we show a basic use case for a Whisper model to predict the next N tokens using `generate()` API, with BigDL-LLM INT4 optimizations.
+In the example [generate.py](./generate.py), we show a basic use case for a Whisper model to conduct transcription using `generate()` API, with BigDL-LLM INT4 optimizations.
 ### 1. Install
 We suggest using conda to manage environment:
 ```bash
@@ -18,7 +18,7 @@ pip install bigdl-llm[all] # install bigdl-llm with 'all' option
 
 ### 2. Run
 ```
-python ./generate.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --repo-id-or-data-path REPO_ID_OR_DATA_PATH --language LANGUAGE
+python ./recognize.py --repo-id-or-model-path REPO_ID_OR_MODEL_PATH --repo-id-or-data-path REPO_ID_OR_DATA_PATH --language LANGUAGE
 ```
 
 Arguments info:
@@ -33,7 +33,7 @@ Arguments info:
 #### 2.1 Client
 On client Windows machine, it is recommended to run directly with full utilization of all cores:
 ```powershell
-python ./generate.py 
+python ./recognize.py 
 ```
 
 #### 2.2 Server
@@ -53,7 +53,7 @@ numactl -C 0-47 -m 0 python ./generate.py
 #### [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny)
 
 ```log
-Inference time: 0.23290777206420898 s
+Inference time: xxxx s
 -------------------- Output --------------------
 [" Mr. Quilter is the Apostle of the Middle classes and we're glad to welcome his Gospel."]
 ```

python/llm/example/transformers/transformers_int4/whisper/recognize.py

@@ -23,9 +23,9 @@ from transformers import WhisperProcessor
 from datasets import load_dataset
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='Predict Tokens using `generate()` API for Whisper model')
+    parser = argparse.ArgumentParser(description='Recognize Tokens using `generate()` API for Whisper model')
     parser.add_argument('--repo-id-or-model-path', type=str, default="openai/whisper-tiny",
-                        help='The huggingface repo id for the whisper model to be downloaded'
+                        help='The huggingface repo id for the Whisper model to be downloaded'
                              ', or the path to the huggingface checkpoint folder')
     parser.add_argument('--repo-id-or-data-path', type=str,
                         default="hf-internal-testing/librispeech_asr_dummy",
@@ -45,11 +45,11 @@ if __name__ == '__main__':
                                                       load_in_4bit=True)
     model.config.forced_decoder_ids = None
 
-    # Load tokenizer
+    # Load processor
     processor = WhisperProcessor.from_pretrained(model_path)
     forced_decoder_ids = processor.get_decoder_prompt_ids(language=language, task="transcribe")
 
-    # load dummy dataset and read audio files
+    # Load dummy dataset and read audio files
     ds = load_dataset(dataset_path, "clean", split="validation")
 
     # Generate predicted tokens