ipex-llm/python/llm/example/GPU/PyTorch-Models/Model/openai-whisper

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 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 for more information.

Example: Recognize Tokens using transcribe() API

In the example 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:

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:

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.

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

For Intel Arc™ A-Series Graphics and Intel Data Center GPU Flex Series

export USE_XETLA=OFF
export SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS=1
export SYCL_CACHE_PERSISTENT=1

For Intel Data Center GPU Max Series

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.

For Intel iGPU

export SYCL_CACHE_PERSISTENT=1
export BIGDL_LLM_XMX_DISABLED=1

3.2 Configurations for Windows

For Intel iGPU

set SYCL_CACHE_PERSISTENT=1
set BIGDL_LLM_XMX_DISABLED=1

For Intel Arc™ A-Series Graphics

set SYCL_CACHE_PERSISTENT=1

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

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 XB model saved in 16-bit will requires approximately 2X GB of memory for loading, and ~0.5X 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

For audio file(.wav) download from https://www.youtube.com/watch?v=-LIIf7E-qFI, it should be extracted as:

[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.