Add openai-whisper pytorch gpu (intel#11736)

* Add openai-whisper pytorch gpu

* Update README.md

* Update README.md

* fix typo

* fix names update readme

* Update README.md

python/llm/example/GPU/PyTorch-Models/Model/openai-whisper/README.md

@@ -0,0 +1,142 @@
+# Whisper
+
+In this directory, you will find examples of how to use IPEX-LLM to optimize OpenAI Whisper models within the `openai-whisper` Python library. For illustration purposes, we utilize the [whisper-tiny](https://github.com/openai/whisper/blob/main/model-card.md) as a reference Whisper model.
+
+## Requirements
+To run these examples with IPEX-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to [here](../../../README.md#requirements) for more information.
+
+## Example: Recognize Tokens using `transcribe()` API
+In the example [recognize.py](./recognize.py), we show a basic use case for a Whisper model to conduct transcription using `transcribe()` API, with IPEX-LLM INT4 optimizations on Intel GPUs.
+### 1. Install
+#### 1.1 Installation on Linux
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.11
+conda activate llm
+# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
+pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
+pip install -U openai-whisper
+pip install librosa # required by audio processing 
+```
+
+#### 1.2 Installation on Windows
+We suggest using conda to manage environment:
+```bash
+conda create -n llm python=3.11 libuv
+conda activate llm
+
+# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
+pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
+pip install -U openai-whisper
+pip install librosa
+```
+
+### 2. Configures OneAPI environment variables for Linux
+
+> [!NOTE]
+> Skip this step if you are running on Windows.
+
+This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.
+
+```bash
+source /opt/intel/oneapi/setvars.sh
+```
+
+### 3. Runtime Configurations
+For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
+#### 3.1 Configurations for Linux
+<details>
+
+<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
+
+```bash
+export USE_XETLA=OFF
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+export SYCL_CACHE_PERSISTENT=1
+```
+
+</details>
+
+<details>
+
+<summary>For Intel Data Center GPU Max Series</summary>
+
+```bash
+export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
+export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
+export SYCL_CACHE_PERSISTENT=1
+export ENABLE_SDP_FUSION=1
+```
+> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
+</details>
+
+<details>
+
+<summary>For Intel iGPU</summary>
+
+```bash
+export SYCL_CACHE_PERSISTENT=1
+export BIGDL_LLM_XMX_DISABLED=1
+```
+
+</details>
+
+#### 3.2 Configurations for Windows
+<details>
+
+<summary>For Intel iGPU</summary>
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+set BIGDL_LLM_XMX_DISABLED=1
+```
+
+</details>
+
+<details>
+
+<summary>For Intel Arc™ A-Series Graphics</summary>
+
+```cmd
+set SYCL_CACHE_PERSISTENT=1
+```
+
+</details>
+
+> [!NOTE]
+> For the first time that each model runs on Intel iGPU/Intel Arc™ A300-Series or Pro A60, it may take several minutes to compile.
+### 4. Running examples
+
+```bash
+python ./recognize.py --audio-file AUDIO_FILE
+```
+
+Arguments info:
+- `--model-name MODEL_NAME`: argument defining the model name(tiny, medium, base, etc.) for the Whisper model to be downloaded. It is one of the official model names listed by `whisper.available_models()`, or path to a model checkpoint containing the model dimensions and the model state_dict. It is default to be `'tiny'`.
+- `--audio-file AUDIO_FILE`: argument defining the path of the audio file to be recognized.
+- `--language LANGUAGE`: argument defining language to be transcribed. It is default to be `english`.
+
+> **Note**: When loading the model in 4-bit, IPEX-LLM converts linear layers in the model into INT4 format. In theory, a *X*B model saved in 16-bit will requires approximately 2*X* GB of memory for loading, and ~0.5*X* GB memory for further inference.
+>
+> Please select the appropriate size of the Whisper model based on the capabilities of your machine.
+
+#### Sample Output
+#### [whisper-tiny](https://github.com/openai/whisper/blob/main/model-card.md)
+
+For audio file(.wav) download from https://www.youtube.com/watch?v=-LIIf7E-qFI, it should be extracted as:
+```log
+[00:00.000 --> 00:10.000]  I don't know who you are.
+[00:10.000 --> 00:15.000]  I don't know what you want.
+[00:15.000 --> 00:21.000]  If you're looking for ransom, I can tell you I don't know money, but what I do have.
+[00:21.000 --> 00:24.000]  I'm a very particular set of skills.
+[00:24.000 --> 00:27.000]  The skills I have acquired are very long career.
+[00:27.000 --> 00:31.000]  The skills that make me a nightmare for people like you.
+[00:31.000 --> 00:35.000]  If you let my daughter go now, that'll be the end of it.
+[00:35.000 --> 00:39.000]  I will not look for you. I will not pursue you.
+[00:39.000 --> 00:45.000]  But if you don't, I will look for you. I will find you.
+[00:45.000 --> 00:48.000]  And I will kill you.
+[00:48.000 --> 00:53.000]  Good luck.
+Inference time: xxxx s
+-------------------- Output --------------------
+ I don't know who you are. I don't know what you want. If you're looking for ransom, I can tell you I don't know money, but what I do have. I'm a very particular set of skills. The skills I have acquired are very long career. The skills that make me a nightmare for people like you. If you let my daughter go now, that'll be the end of it. I will not look for you. I will not pursue you. But if you don't, I will look for you. I will find you. And I will kill you. Good luck.
+```

python/llm/example/GPU/PyTorch-Models/Model/openai-whisper/recognize.py

@@ -0,0 +1,59 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import whisper
+import time
+import librosa
+import argparse
+from ipex_llm import optimize_model
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Recognize Tokens using `transcribe()` API for Openai Whisper model')
+    parser.add_argument('--model-name', type=str, default="tiny",
+                        help="The model name(tiny, medium, base, etc.) for the Whisper model to be downloaded."
+                             "It is one of the official model names listed by `whisper.available_models()`, or"
+                             "path to a model checkpoint containing the model dimensions and the model state_dict.")
+    parser.add_argument('--audio-file', type=str, required=True,
+                        help='The path of the audio file to be recognized.')
+    parser.add_argument('--language', type=str, default="English",
+                        help='language to be transcribed')
+    args = parser.parse_args()
+
+    # Load the input audio
+    y, sr = librosa.load(args.audio_file)
+
+    # Downsample the audio to 16kHz
+    target_sr = 16000
+    audio = librosa.resample(y,
+                            orig_sr=sr,
+                            target_sr=target_sr)
+
+    # Load whisper model under pytorch framework
+    model = whisper.load_model(args.model_name)
+
+    # With only one line to enable IPEX-LLM optimize on a pytorch model
+    model = optimize_model(model)
+
+    model = model.to('xpu')
+
+    st = time.time()
+    result = model.transcribe(audio, verbose=True, language=args.language)
+    end = time.time()
+    print(f'Inference time: {end-st} s')
+
+    print('-'*20, 'Output', '-'*20)
+    print(result["text"])