Whisper

In this directory, you will find examples on how you could apply BigDL-LLM INT4 optimizations on Whisper models on Intel GPUs. For illustration purposes, we utilize the openai/whisper-tiny as a reference Whisper model.

0. Requirements

To run these examples with BigDL-LLM on Intel GPUs, we have some recommended requirements for your machine, please refer to here for more information.

Example: Recognize Tokens using `generate()` API

In the example recognize.py, we show a basic use case for a Whisper model to conduct transcription using generate() API, with BigDL-LLM INT4 optimizations on Intel GPUs.

1. Install

We suggest using conda to manage environment:

conda create -n llm python=3.9
conda activate llm
# below command will install intel_extension_for_pytorch==2.0.110+xpu as default
# you can install specific ipex/torch version for your need
pip install --pre --upgrade bigdl-llm[xpu] -f https://developer.intel.com/ipex-whl-stable-xpu
pip install datasets soundfile librosa # required by audio processing

2. Configures OneAPI environment variables

source /opt/intel/oneapi/setvars.sh

3. Run

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:

--repo-id-or-model-path REPO_ID_OR_MODEL_PATH: argument defining the huggingface repo id for the Whisper model to be downloaded, or the path to the huggingface checkpoint folder. It is default to be 'openai/whisper-tiny'.
--repo-id-or-data-path REPO_ID_OR_DATA_PATH: argument defining the huggingface repo id for the audio dataset to be downloaded, or the path to the huggingface dataset folder. It is default to be 'hf-internal-testing/librispeech_asr_dummy'.
--language LANGUAGE: argument defining language to be transcribed. It is default to be english.

Sample Output

openai/whisper-tiny

Inference time: xxxx s
-------------------- Output --------------------
[' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.']

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