Add openai-whisper pytorch gpu (#11736)
* Add openai-whisper pytorch gpu * Update README.md * Update README.md * fix typo * fix names update readme * Update README.md
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Zijie Li
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# Whisper
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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.
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## Requirements
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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.
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## Example: Recognize Tokens using `transcribe()` API
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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.
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### 1. Install
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#### 1.1 Installation on Linux
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We suggest using conda to manage environment:
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```bash
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conda create -n llm python=3.11
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conda activate llm
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# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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pip install -U openai-whisper
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pip install librosa # required by audio processing
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```
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#### 1.2 Installation on Windows
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We suggest using conda to manage environment:
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```bash
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conda create -n llm python=3.11 libuv
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conda activate llm
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# below command will install intel_extension_for_pytorch==2.1.10+xpu as default
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pip install --pre --upgrade ipex-llm[xpu] --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
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pip install -U openai-whisper
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pip install librosa
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```
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### 2. Configures OneAPI environment variables for Linux
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> [!NOTE]
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> Skip this step if you are running on Windows.
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This is a required step on Linux for APT or offline installed oneAPI. Skip this step for PIP-installed oneAPI.
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```bash
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source /opt/intel/oneapi/setvars.sh
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```
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### 3. Runtime Configurations
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For optimal performance, it is recommended to set several environment variables. Please check out the suggestions based on your device.
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#### 3.1 Configurations for Linux
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<details>
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<summary>For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series</summary>
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```bash
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export USE_XETLA=OFF
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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export SYCL_CACHE_PERSISTENT=1
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```
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</details>
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<details>
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<summary>For Intel Data Center GPU Max Series</summary>
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```bash
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export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libtcmalloc.so
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export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
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export SYCL_CACHE_PERSISTENT=1
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export ENABLE_SDP_FUSION=1
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```
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> Note: Please note that `libtcmalloc.so` can be installed by `conda install -c conda-forge -y gperftools=2.10`.
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</details>
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<details>
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<summary>For Intel iGPU</summary>
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```bash
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export SYCL_CACHE_PERSISTENT=1
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export BIGDL_LLM_XMX_DISABLED=1
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```
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</details>
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#### 3.2 Configurations for Windows
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<details>
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<summary>For Intel iGPU</summary>
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```cmd
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set SYCL_CACHE_PERSISTENT=1
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set BIGDL_LLM_XMX_DISABLED=1
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```
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</details>
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<details>
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<summary>For Intel Arc™ A-Series Graphics</summary>
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```cmd
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set SYCL_CACHE_PERSISTENT=1
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```
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</details>
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> [!NOTE]
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> 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.
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### 4. Running examples
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```bash
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python ./recognize.py --audio-file AUDIO_FILE
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```
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Arguments info:
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- `--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'`.
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- `--audio-file AUDIO_FILE`: argument defining the path of the audio file to be recognized.
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- `--language LANGUAGE`: argument defining language to be transcribed. It is default to be `english`.
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> **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.
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>
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> Please select the appropriate size of the Whisper model based on the capabilities of your machine.
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#### Sample Output
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#### [whisper-tiny](https://github.com/openai/whisper/blob/main/model-card.md)
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For audio file(.wav) download from https://www.youtube.com/watch?v=-LIIf7E-qFI, it should be extracted as:
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```log
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[00:00.000 --> 00:10.000] I don't know who you are.
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[00:10.000 --> 00:15.000] I don't know what you want.
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[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.
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[00:21.000 --> 00:24.000] I'm a very particular set of skills.
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[00:24.000 --> 00:27.000] The skills I have acquired are very long career.
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[00:27.000 --> 00:31.000] The skills that make me a nightmare for people like you.
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[00:31.000 --> 00:35.000] If you let my daughter go now, that'll be the end of it.
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[00:35.000 --> 00:39.000] I will not look for you. I will not pursue you.
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[00:39.000 --> 00:45.000] But if you don't, I will look for you. I will find you.
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[00:45.000 --> 00:48.000] And I will kill you.
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[00:48.000 --> 00:53.000] Good luck.
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Inference time: xxxx s
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-------------------- Output --------------------
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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.
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```
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#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import whisper
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import time
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import librosa
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import argparse
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from ipex_llm import optimize_model
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Recognize Tokens using `transcribe()` API for Openai Whisper model')
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parser.add_argument('--model-name', type=str, default="tiny",
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help="The model name(tiny, medium, base, etc.) for the Whisper model to be downloaded."
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"It is one of the official model names listed by `whisper.available_models()`, or"
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"path to a model checkpoint containing the model dimensions and the model state_dict.")
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parser.add_argument('--audio-file', type=str, required=True,
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help='The path of the audio file to be recognized.')
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parser.add_argument('--language', type=str, default="English",
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help='language to be transcribed')
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args = parser.parse_args()
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# Load the input audio
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y, sr = librosa.load(args.audio_file)
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# Downsample the audio to 16kHz
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target_sr = 16000
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audio = librosa.resample(y,
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orig_sr=sr,
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target_sr=target_sr)
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# Load whisper model under pytorch framework
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model = whisper.load_model(args.model_name)
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# With only one line to enable IPEX-LLM optimize on a pytorch model
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model = optimize_model(model)
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model = model.to('xpu')
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st = time.time()
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result = model.transcribe(audio, verbose=True, language=args.language)
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end = time.time()
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print(f'Inference time: {end-st} s')
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print('-'*20, 'Output', '-'*20)
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print(result["text"])
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